Ask HN: How do you manage skills files? | Hacker News Reader

Ask HN: How do you manage skills files?

320 pointsimadtaieber299 comments

How do you find skills, keep them organized, and make sure they actually work? Do you keep improving them over time?

I believe skills will eventually be eating by model capabilities, but until then I'm just looking for a better way to manage things.

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Skills are mostly snake oil, the way people use them (the aspiration to download kung foo from a celebrity).

There was a time when maybe it mattered (last year), but with good repos and good prompts today's agents can find exactly what they need without any skills.

"Skills" as developer macros can be useful, but at most those are things shared with the team (in the repo), not something you download from the internet. If you have so many skills that you feel the need to manage them, that's a code smell.

100% - influencers pretend they know something and produce all in one skills pack - that doesn't make sense

Great way to go viral though

Yes, skills as a "portable power" isn't really the use case for me unless it's entirely generic and even then sparingly.

I've mostly followed what anthropic suggests, which is putting less into context and more into skills, to keep the "how" out of context until it is needed to reduce context bloat.

Skills have some instructions but are primarily informed repo specific instructions and keep their context away from the rest of the repo to keep things sanitised for me.

I've found it to be useful in that context.

> Skills have some instructions but are primarily informed repo specific instructions and keep their context away from the rest of the repo to keep things sanitised for me.

Skills and agents in the Claude world can also be extended and evolved over time, as they are committed "code".

For example, we have an agent which can take a statement or a support ticket and identifies the services, tenants and infrastructure components likely meant in the ticket or request. Similar to a skill, Claude can invoke this on demand in a conversation.

This started very simple, but various people spent time tuning it over the last 4-6 months. They have "taught" it to pick up on jargon from different departments, writing style of different departments, how they think about their systems.

With all of that tuning over time it has become quite "clever" in identifying the mentioned systems and - if requested - the train of thought leading to this conclusion.

Similar things are happening with skills for various task, be it Ansible integration tests, upgrade chores and so on. The first version can be fairly underwhelming, but continuously improving it after each usage can make them very powerful.

i agree, skills downloaded from the internet are all snake oil.

creating your own skills however good for both reducing the token usage & increasing reliability. those damn llms are not deterministic, asking same thing twice produces 2 different results.

This has been my experience (with downloaded skills), and currently my workflow is almost 100% skill driven.

Every feature I build uses a skill that does the following:

1. Read a ticket and get context on the task. The ticket was probably written by another agent after a conversation with myself about what is happening/needs to happen, etc.

2. Plan the task, asking for clarification where needed

3. Pressure test the plan, and validate the plans logic (subagents)

4. Implement

5. Runtime/local validation

6. Post PR, review it using applicable agents (database, security, code, prose...)

7. Fix PR based on feedback

I generally get excellent results out of this process, and I cannot imagine trying to orchestrate this without a skill. But I also can imagine my workflow isn't tuned to be super usable for anyone else.

What does your prompting look like for step 3 - pressure test the plans?

I had claude define an agent for a "skeptic", the first few lines in the agent file are "You are a skeptic. Your job is to refuse to take claims on faith and instead verify them against ground truth. You are not a general code reviewer. You are not a stylist. You are not a cheerleader. You take a list of claims (explicit or implicit), and for each one, you find the evidence — or the absence of it — and report what you found."

It goes on to describe what counts as a claim, how to verify the claims, and how to respond. It responds to each claim with verified, unverified, or contradicted.

The skeptic agent has been the most high value thing I've added to the workflow.

>but with good repos and good prompts

I think waaay more people struggle with this than HN would have you believe. In the real world, not everyone is a software dev with a developer mindset to using these tools. Normal people essentially type the equivalent of "Make me X!" and complain when the model assumes anything in their underspecified mess of a prompt. There are skills like grill-me that can potentially help these people a lot, but in the end I believe models will just be smart enough to understand your level of knowledge and intent to do this stuff on their own. They are getting much better on pushing back on poor user input already. The problem is that when they double down on hallucinations (very rare nowadays but I still see it happen in enterprise projects with the latest models). So you kind of need to know when to push back on the model as well. But for that you have to be really good at the subject.

I tend to agree. Skill files become less useful as developer skill increases.

As a skilled developer my repetitive instructions are mostly one or two sentence phrases for staring something like a highly-interactive planning session, or a self-supervised implementation session with my preferred setup of implementation and review subagents. I can specify those out by hand, or save a couple keystrokes with a tiny skill file.

But if you are not a software dev you might lack the vocabulary to tell the agent what you want. If you don't know what tenant isolation is, chances are your app will have a broken security model because you can't ask for it, and probably won't think to ask the agent for a security review either. Skills can mitigate a lot here

I still find the superpowers to be very useful, just as a way to manage the feature process, getting you a spec to go with the feature, quizzing you about edge case handling.

> I think waaay more people struggle with this than HN would have you believe.

I'm sure we all know. I mean, just ask anyone to write a story and break it into small tasks that can each be accomplished completely in a day.

It's interesting as grill-me and the other Matt skills are very much positioned to people who would consider themselves as developers. In fact, I'm not sure if people who were completely new to development would have heard of him at all.

I find them useful for deploying task specific agents, like reviewing Jira tickets, or otherwise ensuring compliance in open format submissions.

Otherwise I agree, and you don't even have to be that verbose with prompt engineering these days as LLMs have gotten increasingly good at figuring out what you want.

If you are spending time on all text forums like this, you are likely a person whose skill set skews towards the verbalization of abstract concepts. This is also the exact skill set needed to use LLMs well. If you are able to articulate exactly what you want in a concise prompt, little else is needed.

I think we tend to overlook the fact that LLMs have tilted the scales heavily in favor of those with good verbal skills. A huge portion of the population (including a portion of highly skilled software engineers) is not great at doing this. For them, harness skills still act as a kind of scaffolding; they support automated work on a project in cases where insufficient details is given in the prompt.

The closest I get to finding skills useful is when I find myself repeating myself to an LLM. This tends to happen most when I am starting new projects and want to communicate basic design principles and patterns to follow and libraries to use. What I did was to factor and store these "chunks" of instruction in some text files. I then made a little script that can list what chunks are available and when given a subset will essentially `cat` the selected files to emit AGENTS.md content which I save into the new project or append to shore up an existing one.

Your observation on the readership bias of HN is a good one for people to add to their HUMANS.md before reading and commenting. :)

In my experience, while software engineers can be socially awkward/introverted, they generally do have good verbal skills, an excellent vocabulary and can be concise and articulate in writing, when there's no social pressure. There are some exceptions of course, but being able to translate an idea or set of tasks into a concise written language form is essentially what programming is.

Where they are very useful is as a documentation source for LLMs. For example, I work in infosec and often have to reference DSLs (Cobalt Strike aggressor script for example). Having a skill which is an offline index to carved up function docs, which an LLM can use without having to think, then search for, then download huge 1 page documents with all function documentation, and pollute the context… very useful.

Looks like this is a combination of documentation with a skill, right? How do you manage these skills across projects?

Commit them in the root of your monorepo :P

Or create a repo for skills and have people check those out in their user skills dir.

Or in case of Claude, create a plugin that your people install.

I did create special repo that has all skills/agents/scripts and MCP configuration. All agents ( currently 3 in use: claude code, opencode, and pi ) packed in docker container, with artifacts required by project technologies, and task at hand ( planning, code review, documentation management, web design, ... ). Nothing but a small file with list of technologies committed to project.

For evaluation, there is command to record session, repository commit, and observed problems that stored in special database, so each session can be reproduced. Developer commit reports, I do analysis, refine and evaluate system.

> "Skills" as developer macros can be useful, but at most those are things shared with the team (in the repo), not something you download from the internet. If you have so many skills that you feel the need to manage them, that's a code smell.

I have three development machines. You kinda need something like git to keep everyone in sync!

And there's still value in encoding a process in a skill - it's way more token efficient to tell the model what but also HOW to do something. Otherwise, it just spends a lot of tokens figuring out something that they previously did already.

> I have three development machines. You kinda need something like git to keep everyone in sync

I got tired of the constant git push/git pulling just for inter machine transfer, now I just have a ~/Git directory with all my projects that I just Syncthing between my desktops/laptops and Syncthing ignore any repos that specific machines don't need

Depends on what you do. If you work with proprietary tech that is not in LLM training data and can't easily be found on the internet, you're cooked without good skill files.

I agree it depends, but I can offer another angle: By writing a few py tools and creating skills around them I was able to save tokens, so these skills were cost-effective in my case, they lowered the cost of the tasks I execute.

Yeah, this is my use case for skills. Even with good documentation, it feels better to have things local and easy to tweak.

I'd add to that I've also used them as a style guide. The project involved taking in unstructured inputs and creating structured outputs. Lots of choices along the way, and it seemed a neat way to encapsulate decisions we'd made as a team.

Storage, well it's just for the one project, so the repo. Can't say I've used them beyond that.

Yes, but isn't that kind of a waste? If there's not enough AI training data or even just the internet, how can we find information just from a command called a skill? It would be nothing?.

I'm a bit confused by what you're asking.

Let me give you an example. Let's say you're building processes using the process management tool FooTool from the company BigFoo. You tell the LLM, "make a new process." A process is just an XML file, but BigFoo is highly proprietary, so the LLM has no examples of how to make one. No public documentation exists on the Internet, so it's not in the LLM's training data and can't be searched.

So you make a skill "make FooTool process" that explains what a FooTool process XML looks like, what options there are for initializing a new FooTool process, and so on.

Now your LLM went from "Let me spend five minutes looking at other stuff in your repo to find anything that tells me what I'm supposed to do" to 20 seconds and a working process.

This could be better summed up to the misuse of skills. Skills were not designed to be a way to make an agent more intelligent. Instead, skills are designed to allow agents to have certain tasks that are repeatable and predictable. It’s a misnomer really.

Not everyone use AI only for coding, for “code smell” being even applicable here. Many of my skills are just processes distilled from actual sessions doing odd tasks and coordinating different tools. It’s pretty reasonable to assume that it saves the agent from repeating that first time exploration fumbling

Woaaah buddy this is such a wrong statement that I’d delete it if I were you.

Can’t believe that people confidently spew blatantly false statements like this.

Skills matter, a lot, to every action that requires the AI to find stuff out, so that it doesn’t have to find the same stuff out again. Operating a website, building PowerPoints the way you like them, operating across different surfaces like APIs + GUIs…otherwise the AI has to relearn how to do it every time.

Be confident about things you know. Study about things you don’t.

I don’t feel strongly on skills either way, but why would you suggest GP delete their comment, without which we wouldn’t even be having this (in my view productive) discussion?

I exaggerated. But I'm so deep into these things that it's like someone writing emphatically 'colours don't exist,' or 'toenails don't grow on their own.' (Dunno why I thought of this second example)

It's not about giving hints to agents because they wouldn't find it out otherwise. It's about saving the work of them having to find out. Good skills files save tokens.

Having too many skills files increase tokens even when they don't get fully read. It's a fine line.

That's why you need to curate and adapt the skills for your specific project. That is, don't blindly accumulate skills downloaded from the web

How do you actually measure the token savings? Do you compare the same task with and without the skill, or is it more of a noticeable difference over time?

By watching them get it the first time, instead of watching them hit 10 locations before finding it.

Both, in a way. In order to be sure it's the skill reducing token usage, you measure it with and without the skill, and then you do that periodically to account for all the other variables.

Alternatively, you pick a belief based on prior evidence from either approach you mentioned, which is the natural thing that many of us do.

You say: "you measure", "you do that", "you pick".

But what about "I"?

Hah I think if a repo is “standard” enough that an agent can navigate it freely without any help or direction, maybe it’s not worth having altogether?

Even fable _regularly_ stumbles as big repos or custom configurations, even for projects that fable itself built with high dev quality standards and modern design direction.

It just can’t hold it all in its context and will be forced to do “software archeology” all the time to figure things out - yeah it will work _most_ of the time, but to truly be able to scale and have autonomous agents reliably work and mold your codebase you need a lot more structure - tests, lints, compilers, validators etc. Your “skills” or policy files are there so agents can resolve issues and heal things themselves without your explicit direction.

If I have several tabs, each holding an agent team, with each agent spawning subagents as it sees fit, all of that apparatus has to ground itself _somewhere_ and if you don’t make decisions yourself, it will make decisions for you, save them in its own skill files, but some of these you might not like.

They're literally just documentation with a hat on. I don't mind a skill saying where the docs are but an overreliance on skills is simply proof someone isn't able to reason about the gestalt

My approach is different. Skills I write mostly use Python to save on the agent needing to run its own loops.

I use these loops to monitor the CI build and PR approvals rather than having the agent poll, and even Opus gets the commands wrong enough to make it worth it.

Last week I wired up a skill for the agent to share screenshots in PRs via specific S3 buckets and AWS CLIs. Again, the agents guess at the right commands often enough to make it worth being explicit.

Sure these could have gone in CLAUDE.md, but not every agent needs the context.

And at the company level, I can push skills to everyone’s Claude via the Teams function, they don’t need to edit configs or even know what a skill is.

small behaviours are fine, yes, what i was referring to was people making "foobar api skill" with just a bad compression of the foobar docs in a .md

the problem with that, that i'm seeing is just the sheer proliferation of internal skills. There are now so many that I can't see how they can possibly remain maintained.

It feels like at the project/product/middle management layer this type of 'big pile of skills to do some very specific task' is very popular. I think this is probably for a few reasons.

I think the largest factor is that these task management items are really just like .. calling a few different apis and slapping it through some jinja to post to github and create a jira ticket.. whatever. For an engineer, we can knock that out over coffee. But for this middle management layer, not a lot of them have the will or the skills to pop open the ide and code themselves a tool. Until the advent of skills that is. So I think they are a bit drunk with power. Which yaknow we'll see.

The skills are starting to become the documentation for these types of things as well as a way to automate it. This is great, but this type of 'documentation' is exactly the type of thing that goes non-updated for years in some dark corner of confluence. So I suspect the little used skills are going to fall to 'context rot'.

As an engineer, I keep all those skills in one very specific claude project and keep that largely separate from any given claude session that's helping me design a feature.

Yea I definitely keep engineering skills and org skills separate.

The only useful generic skill I have is the ast-grep one.

No, not really. Yesterday I asked Opus if it can read the logs from the sessions I have on the local ChatGPT app. It looked around and said no, that’s not possible. I said “what about these jsonl files in this folder?” It read them and said “ah yes these seem to be it!”

So I went ahead and created a skill for it. This is so that future Opus agents won’t come to the wrong conclusion the first one did. I can say “read the codex session titled ‘X’” And they will know exactly what to do and do it effortlessly.

My company ran a test and found that they reduce token output on flagship model by something like 2-4x, and that number has been increasing with newer models. I suspect the increased subagent usage is driving this trend, because this means we're relying on models to do their own prompt engineering.

Yes, they are just text, and can therefore be replaced with good prompting. However, this also means they confer a real benefit: a good set of skills creates a transferable baseline, raising the skill floor and offering a more consistent experience across the organization.

I am somewhat confused by takes like this. Of course skills are just prompts, this is the whole point.

A skill is just a stored prompt you want to put more information into than you're likely to type out every time you intend to do that thing. Documentation of a business process.

What are you confused by? You're saying the same thing they said.

They added the additional claim that writing the skills down (apparently) prevents the models from having to self-prompt on the fly and therefore reduces token consumption.

I think skills also count towards the token counts. In the end, everything becomes a 1-D array of input text.

Yes, skills that are actually used count toward token consumption.

The question is whether the number of tokens required to achieve a certain behavior/intelligence/quality is equal between you manually providing those tokens via skills versus the model "deriving" the "skills" it needs on-the-fly in order to produce the outcome you want.

The claim above is that the former requires far fewer tokens.

Also skills only consume tokens when they are used, and part of the value is that the model will dynamically find and disclose only what's needed (assuming the skill is "well-designed").

Their claim is not about the prompt or skill tokens, it's about output tokens - skills can help the model bypass some thinking tokens or avoid reasoning deadends, and that way reduce output token usage. That's what they seem to have found empirically from their testing. (If it's truly 2x-4x, the time savings in waiting for the output is a pretty nice benefit too.)

It's not just a stored prompt, you can attach re-useable scripts to them to offer more determinism. ex: a script that validates that a PR follows exactly the template you want, with a max of N lines per entry.

The more determinism you have, the more consistent you can be and the more leverage you can build. (yes I understand that skill calls are non deterministic).

That's just a stored prompt that references a script :)

Yes, that's my point :)

We do that, but keep the scripts in the code and just tell them in the markdown where the scripts are, same with "references" (docs/) for us. It never made sense to me to put those in a skill dir, many are useful across skills and for humans (many written for humans before agents were a thing)

One of the more interesting benefits to skills is that many harnesses now run the inline command(s) in backticks, shortcutting the model needing to make a tool call. This is helpful for deterministically building up context content for the skill before the agent ever sees it.

We take this further in some instances and have workflows that (1) does deterministic context gathering (2) invokes an agent (3) processes a file the agent is told to produce. This has made our PR review agent much better and removed it's access to all credential files. We have a step that gathers the diff + existing pull request comments into a .review dir, let the agent process that and create a comments.jsonl, then run a script in a new step to apply the comments against the API

+1 on all of this, it is similar to the pattern I see emerging as well. For example I have open-sourced (let's be honest, it's just my personal tooling of taste) a repo on gh to try and formalize these things. I'd rather not share out of mostly imposter syndrome but essentially it's a scaffolding tool ala "create-react-app" for the stack I generally use.

Part of that is to create standardized "docs/{architecture, decisions, contracts, operations}" folders that my harness recognizes in my repos to have a better sense of where was it.

Glad others are converging towards similar ideas!

I was solving your problem.before LLMs with my personal developers Swiss army knife (hof gen/CUE+templates). I'm somewhat keen to see how I can capture the agent markdown I want in many places, like project init, in CUE via embed and modules I can import and version, skipping the need for a markdown dependency cycle management system.

https://github.com/hofstadter-io/hof

(sharing because it has sloppy parts and that/I.S. shouldn't stop you from sharing!)

There needs to be a word for this type of interaction because it’s so common in software engineering:

Q: I need help doing X

A: if you’re doing X, you’re doing it wrong.

I propose the word shamesplaining. What do you think?

Not saying your opinion isn’t valid. It just doesn’t answer the question and it’s disturbing that this is the top voted answer. It sounds more like a criticism than an answer.

It's called the xy problem already.

The corrollary interaction is this:

Q: I need help doing X

A: What are you really trying to do?

That's maybe the case if you always use the most popular framework and restrict your environment to a basic setup. But as soon as you e.g. build a website with SolidJS, Bulma and vite++ (vp), at least the models I tried are all the time somewhat confused, want to steer your project in a certain direction and build strange workaround so it works the way they are trained on.

Same with mcp. I want them to use the jsdelivr cdn instead of them scraping github against the rate limit. etc. But if I dont explicitly state to strictly use the $%!$@@! mcp for searching in repositories they simply ignore the mcp and even if clearly instructed, they still often fall back to gh.

Putting every detailed instruction in the AGENTS.md would just unnecessarily bloat the context and it works well enough to just instruct them in the AGENTS.md when to use which skill. Yet I agree that Skills are not some voodoo magic to provide your model super capabilities.

I think eventually, some form of self updating llm memory will replace skills, but in the meantime it is token inefficient for the model to have to parse the entire repo from scratch every time, and skills are an imperfect way to shortcut some of that (drawbacks being the skills are sometimes wrong or outdated)

Agree. Good file management will be if not is already good enough.

I don’t think it’s about telling agent what to do with skills for most part anymore as well.

Problem is not having to remember to run agents in exact sequence and not having to repeat the processes.

That’s why I’ve created ctx traits and using them everyday, biggest wins for me are: sync across repos using git-based dependency manager and validator, which ensures version of the process / knowledge I’m using has been approved by me and hasn’t moved without my approval (similar to dependency managers lockfile approach).

This has allowed me to use same traits (it’s extension of skills with typed schemas and procedures, you can use skills with it as well as usual) across different repos without any copy pasting etc.

There are additional cool things like type-driven procedures with specific harness/agent associations and many more cool things coming soon.

TIMTOWTDI. "[T]hings shared with the team (in the repo)" implies project-specific skills. See e.g. https://github.com/mattpocock/skills for a stellar example of third-party skills for battle-hardened workflows.

I largely agree, but skills are still useful for thing after the knowledge cutoff. (e.g. https://github.com/google/skills)

I don’t find skills. I write my own skills based on things I do frequently and repeatably. I keep them version controlled locally and in GitHub, and I symlink that folder to my various agent skill folders so they all stay up to date.

I feel like downloading a bunch of skills is another one of those useless collections people make purely because they have infinite options. It’s like those collections of thousands of bookmarks you’re never going to click or pirated ebooks you’re never going to read.

It’s trivial to write your own skills with agents. The best way to use them, imo, is to make them when you have repeatable agent workflows, written to your own personal taste, and updated as your workflows change.

Here’s what I have for reference:

- Remove agent-speak from code, docs, and markdown files.

- Ask sequences of questions the way I like to be asked questions. Used instead of the question tool. This is my primary design skill as well.

- How to use jj the way I want my agent to use jj

- Dispatch subagents with 6 different sets of priorities. Those priorities are defined in the skill, so I can always dispatch all 6 of them to write or review code. Includes a template for code reviews

- Manage a local MD issue tracker for personal projects

+1 to keeping ownership of skills defining just what I like and symlinking them in where I work.

Similar take here - I only find value in skills as a way to repeatably carry out tasks that are specific to my workflow; any skills I've seen shared broadly don't seem to add much value beyond what the model itself can already handle.

At work I'll occasionally repurpose a skill that someone else has shared as a starting point, but those skills are already somewhat customized to the environment I operate in.

I have all of my skills maintain a single table in a markdown file with a description of each skill, when it last ran, exceptions it encountered, and when it was last edited.

Tracking skill usage like this is really smart, stealing this!

Skills are used to steer individual models towards desirable behaviors they don't exhibit by default. They're not Pokemon cards. They clutter your context, sometimes for essentially 0 benefit.

What are the benefits of skills at all. Does it increase the capability of the LLM?

If you have a certain set of actions that repeat a lot or if you have scripts that need ran.

A skill can help so the model doesn’t need to relearn how to use said scripts.

Skills are like human-readable computer programs. I have one to do releases at work; how to get the canary approved (2 slack messages required), how to ensure that the rollout is happening, what to look for while the rollout is proceeding (any incidents opened during the rollout? error rate look good vs. the non-drained pods?) and then how to repeat that process for the next two tiers. It keeps a ticket updated with the current status so others can follow along and not wonder "is there a release going on?" "did we do a release yesterday?"

You could also write a computer program to do this, but because so much random special stuff can come up during a multi-hour release, it's sort of great to not have anything hard-coded and to let a frontier model be there to assist you when things are weird. (Weird things that have come up -- hung kustomization controllers, release freezes, etc. When Claude notices during the release, you can just get a ticket to go fix it. When you're doing it manually, it would probably be a half hour of investigating "why didn't our release go here?". Such a time saver and a safety net on risky rollouts.)

So that's the sort of thing I use Claude for. Things that are computer-program like, but ad-hoc enough to not really want to write a computer program for it.

No it customizes the capabilities of the LLM to your own workflows.

I don’t like the Q&A tools built into my agent harnesses. I have a skill that describes how to ask me questions, including a template and a few rules for more rare situations.

Instead of typing an 80 line prompt every session about how to ask me questions, I just tell my agent to use this skill.

- I don't find skills, I create them

- Keep them organised in software repos that you install with symlinks for all coding harnesses that you have. Progressive disclosure based on the frontmatter does the rest.

- I make sure they work with AI evals. Think of them like integration tests to prove behaviour. They're useful to optimize your flows. I try to make my skills be mostly a translation between natural language and good small fast tools that they call.

- I change them as a new problem arises. Not just because.

Skills can't be eaten by model capabilities if skills represent a workflow that is custom to my team or my person.

I wrote about a good mental model in the past:

https://alexhans.github.io/posts/series/evals/building-agent...

People always say this about the evals, but I find it hard to have a practical implementation of such a thing where you won’t end up spending 100x the amount of time on the evals than building the skill itself.

Like, ok, I have a debugging skill, now how do I make evals except for the most trivial things?

A skill should only document behaviour the LLM didn't/couldn't exhibit on its own.

So you take your failed case (eg. working with gdb or whatever), write a skill and then test for that failed case.

There are also skills that help LLM do the thing it can do without the skill, but faster (by cutting out unnecessary discovery). I guess for such skills the fail case is "being slow"?

I imagine many fail cases can burn a lot of tokens/usage/time because failing LLMs can be very persistent. Maybe some upper bound (turn count, timeout) would help too.

You don't. If you're using skills to force the AI to fullfill some must criterias, it's not going to work. Must criterias need deterministic checks -> be it hooks or what not.

This is also my biggest gripe with AI. I.e. for specifications, no matter what hype machine I tried, it never fulfilled my criterias, which are: easily verifiable, concise, small specs. Hence I built https://github.com/RicardoMonteiroSimoes/Yamlet initially for claude code, but then decided to use extend it for pi.dev. I now have a dedicated docker image for pi.dev, that only contains Yamlet plugin, and whenever I work on spec I spin it up.

The end result is a .yaml file that easily works in git + git diff, so that I can then proceed with the technical specs-

Why do you have a debugging skill? Just tell it to read the docs.

Skills are for packaging instructions for how to interact with your organizations homebrew process and tools. By definition skills shouldn’t be useful outside of your org because they’re just docs and third party tools already have them for humans.

That’s one, very narrow use case of skills.

Anthropic must love you. Re-blow hundred of thousands of tokens to relearn how to use your profiler and build system at every debugging attempt.

Can you elaborate why a debugging skill would save those tokens?

Rather than teach the agent where grafana is, how to use a sentry trace id to find a otel traceparent, where my ALB is, I'm what clusters do what, which namespace prod is in, what our stack looks like, that thing that looks broken actually isn't, etc etc etc. I just paste in the sentry trace id, a guess of what might be wrong ("i think we overtuned gunicorn again" or "developer bob pushed short sha 123456 and nothing is working") and say "use your investigate skill" then get up and go get a coffee and usually by the time i get back i have an investigate doc filled out from a common template in my notes repo. The agent almost never spends any time spinning it's wheels finding out what the various environments do, where they're located, what our metrics and logging looks like, how to access it etc etc.

To be fair it's the only skill i have/use but I got real tired of explaining the same 12 things over and over. Having it document every incident means I have a dense library of every problem we've run into over the last six months which helps identify recurring problems for RCA

I am starting to wonder if I am doing something wrong: I ignore evals and instead I just try new models or new harnesses (or tweak my own harnesses) by solving problems I want to solve in any case; I just use new tools and form my own subjective opinions of them.

When I say evals I mean the evals you write that verify that your use cases are upheld. Think of it like a regression test for different behaviours/user stories.

The idea would be that if you already know what you want from an autonomous system, you don't need to verify manually every time and instead just run these tests to see if there's any regression of any kind. Generally I recommend structure output and evals that are just a plain assertion, if possible. Cheaper, faster, deterministic assertions.

Does that make more sense?

Big yes on this. I do not understand the appeal of skill shopping. The one exception I have is things like the Axiom Apple development skills and e.g. the official Flutter skills. At that point the skills are just docs though. It's either I remember to paste a URL to the official docs or I just install the skill. But shopping around for random skills just sounds extremely unappealing.

I do both, or rather I do 'skill browsing', for new ideas to then evaluate the skill with my agent if they are useful. Most are not, but some I extract ideas from to augment my own skills https://github.com/flurdy/agent-skills/tree/main/skills#shar...

Though most of the time my skills are just things I found useful and could avoid repeating myself by having as a skill.

That I also use it to route model used with https://github.com/flurdy/pi-skill-model-router is also a reason

Have you seen mattpocock’s skills? I know they are hecka popular with people I work with.

What about skills that you need across projects?

Since I install them with symlinks in the tools "global" locations I get access to them across projects.

Think ~/.codex/skills/<symlink-to-myskill-a/

Same for ~/.Claude or any other tool that supports skills.

First, a lot of people in thread are saying you don't need skills. This is pretty wrong. There is a lot of alpha in using any set of skills that implements SPACE (search, plan, assert, code, evaluate). See: https://open.substack.com/pub/theahura/p/agentics-using-meta...

Second, we share all of our sets of skills in a purpose built registry: https://noriskillsets.dev/ you can use any of our public skillsets from there. If you're on a team you can also sign up to get your own private registry. Makes organization much easier.

Finally, for local development, we use this CLI to manage skills (https://github.com/tilework-tech/nori-skillsets). This is a tool that lets you bundle skills into groups, and then switch between those groups. So for eg if I'm making a slide deck I'll use an admin skillset, and for coding I'll use a swe skillset, and for debugging I'll use a debugging skillset.

We do keep tinkering with our skillsets, but not very much. I don't get the need to adjust things for every model release, doesn't seem necessary for us in practice

I'm building something similar with `Agents`[0] (think of it as a package manager for AGENTS.md snippets). I'm getting good results by including detailed instructions for certain tasks in the repo, and providing hints in AGENTS.md about when to use them. It's basically a simpler version of Skills—but it feels like AGENTS.md adherence is higher than Skill adherence.

[0]: https://github.com/bensyverson/agents/

Can you compare what you have built with superpowers?

Yea, we like obra/superpowers a lot. For folks who are unaware, see: https://github.com/obra/superpowers

the original version of our high autonomy skillset was a fork of obra's superpowers back in...October? November? of last year. Whenever it first came out.

I think basically all of the best skillsets implement SPACE, including superpowers, mattpocock skills, get shit done, etc. There are aesthetic reasons why you might prefer one over the other, but candidly I think any of them will get the job done and are strictly better than not using one of the SPACE skillsets.

(I haven't played with the latest version of superpowers. My hunch is that on average our skillsets have better compliance rates with the models because of some of the ways in which we write the skills themselves, but its a hunch)

How do you think about using these vs using something like plugins + plugin marketplaces to distribute skills that ship natively in a lot of tools?

for the most part skills are so portable that fiddling with moving them around is not that hard. The really hard thing is the 'is this skill worth the bits that its written with'. I'd say 99.99% of skills out there are so bad, you're actually worse off for installing them

standards are good though, we should make plugins for our skillsets so theyre easier for some folks

Definitely. Especially since those standards are fairly aligned across lots of tools. The other cool thing is a lot of desktop clients like Claude Cowork and ChatGPT Desktop also speak plugins, so hooking up marketplaces help with further distribution outside of technical teams.

FWIW I work on AI enablement in my day job for both our build org and the broader company and we’ve found success with using marketplaces for distribution.

I also built a free tool that diffs a lot of agent configs to help me keep current on changes.

Here’s the main page, but you can checkout the marketplace and plugin entries if you’re interested!

https://frontmatter.news

I don't use any skills, what kinds of skills are people finding most useful?

For general tasks, the model seems perfectly capable of figuring out things itself, for project or environment specific tasks, I just put that information in the readme or agents.md file.

For starters, if you repeat a specific prompt multiple times per day, you may save it as a skill.

Claude will do this for you after a few times. But yes, I have a skill called plan-to-epic which creates a Jira epic and ticket per milestone. It helps my agents persist context and, because I’m terrible at competing with my coworkers for “visibility,” means I can point to all my work if asked.

I have a couple of skills with project-specific conventions for how to write a plan and how to write HTML-generating code. But they could probably just as well be .md files in a docs directory, linked to from the AGENTS.md file.

It's not that, is for: Ensuring certain vetted implementation method is used. E.g. you always want tests or docs, or always done.

Caching certain scripts so it's not reinvented each time with risk of error/need reviewing.

I use skills for offloading work onto subagents. By configuring the skill to use a specific model it gets enforced at the harness instead of depending on the good will of the orchestrating model to actually delegate. This also saves context.

Today Fable had to fetch a zip file from a web page with a eula prompt, then get at a file in a disk image in the zip.

This is something that will need to happen a lot as part of this project.

I asked Fable for a skill/script combo suitable for Haiku to accomplish the task, and now that task happens at minimal cost during an analysis run.

Did you consider writing a small Python script for that?

... or telling the LLM to write a small python script for that and install it as a mcp or something ...

One way I use skills, which I don’t see mentioned very often, is as “shortcuts”. Imagine some frequently issued prompt like “fetch origin and rebase this branch onto origin/master and resolve conflicts”. I make that into a little skills file called “rebase” with a one sentence description, and next time just type something like “/reb-tab-enter”.

Well, for non-general tasks, of course. For example particular tooling that's required for the environment.

I will often make a skill out of the docs for any of the frameworks or libraries that we're using but with which I'm unfamiliar. When I'm creating that skill, I focus on idiomatic implementation and usage. It's not enough for the code to work—I want it to work "with the grain" and "through the front door", as it were.

By default, these models are just all too willing to reinvent the wheel and monkeypatch as they go.

I make skills for «this is how I like to do things in this company / project». Query test database, git branch names, commit message style, which cloud things can be inspected like logs etc. I don’t see the point in trying to teach the models things that is in the documentation of git, python, what have you. They already know.

Isn’t that what the agents.md in your project is for?

I try to keep agents/Claude.md as tiny as possible. With high level "truths" that don't change. Stack used, invariants, file structure, and some scripts.

Skills are more for things you do often. I run mutation tests, type check,linting,etc. I _could_ just prompt and copy/paste the same prompt each time I need to, or I can just run /tests.

I also have skills for specialized tasks I need every once in a while, like a ux skill, a text skill optimized for xyz, etc.

Depends on how much information and details you have. The agents.md always goes into context. Detailed testing or process information might be excessive, when agent is working on UI. Skills are pulled when needed.

I handle the context problem by splitting the details to dozens of small md files. Agents.md acts as a router that directs the llm to correct documentation file/folder according to the task at hand.

This documentation is its own git repo, and the agents.md file has an explicit instruction to update the docs when it has learned something general that can be useful in future sessions. I then occasionally review and prune those docs.

That's exactly what skills do.

The description in the front-matter (at the top of the skill markdown file) is the only thing in the context and used by the agent to determine when to read in the rest of the skill file.

Skills are evaluated by short description whether to read them into context.

Skills itself may be lengthy so...

It's possible I invented skills before they were common. I've always had some instructions in agents.md that are something like "when working with typescript, read prompts/conventions.ts.md, when working with our fooBar module, read prompts/foobar.md"

I'm not sure if this differs greatly from skills. Maybe my wording makes these "skills" less likely to be read at the correct times, but I haven't seen an issue.

yes, it is. it's almost exactly the same thing.

skills are just an agents.md broken up into chunks so you can manage and share them separately. unfortunately there's no real good workflow for managing or sharing them separately, so most people end up treating them exactly the same way they do agents.md.

Most guidance I’ve read, and experienced success with, is keeping a lean agents.md file and building out a tree of docs or skills that an agent can navigate via progressive disclosure.

> I don't use any skills, what kinds of skills are people finding most useful?

I create/edit/delete at least one skill per day. I can't imagine working effectively without those files.

The most common case: if I see something took AI too much time and tokens and it is done, I ask my Cursor immedietly after to save it as skill. So next time I do the same I just refer to skill. I don't need to remember the name of the skill, I just mention something like "do {explaining briefly the task}, you have done something similar in the past and it is saved as skill"

Your agents.md is a good place for high level facts, but if you have something that requires a lot of info to explain (ie: if there is a complex build process, testing patterns, things like that), loading up your agents.md for every request may be a bad idea. Offloading that information to a skill ensures it's only included in the context if you're actually using it.

How about linking to a separate docs file from the Readme, same as how you'd split separate topics into different files for humans? The context cost is low and as far as I can tell it's pretty much how Claude's "memory" feature works.

That splitting is basically what a skill is, with some instructions as to when to load it. Depending on the harness used it might be quite equivalent, but not sure how easy it follows links in the readme compared to skills (which is just a glorified name of a readme anyways)

Right, it's all just text file management. My point is that if I can add a few lines of description with links in my readme/agents/etc instead of manually including skills in the prompt, without any downside, I'd rather do that.

The content of AGENTS.md is typically included in the system prompt and benefits from prompt caching.

Something else I want to add on to be more specific is that I use skills to document tasks that my model doesn't know how to do out of the box. For example, there is a jira cli[0] that I use for interfacing with jira. If I just say something like "add an issue for X on jira", the model will have no idea how to interface with jira. I could add that the jira cli is installed on my system, but it's not popular enough for the model I use to just know how to use the CLI, and it will end up spending a lot of tokens guessing how to use it, failing, reading the help output, trying again, etc. Adding a skill for jira lets me capture how to use the cli, and makes prompts like the original usually work first try. If something still requires the model to iterate with the cli, I will ask the model why it failed originally, and ask it to update the skill file accordingly, to avoid the same failures in the future.

[0] https://github.com/ankitpokhrel/jira-cli

So the term used internally is to make things "Determinishtic". I use skills extensively, combined with SOPs, scripts and MCP servers.

An example skill I have is SessionMiner, which is installed via post session hooks in Claude and Kiro, and analyzes the session, what was accomplished, and whether or not it should be turned into a skill, then when it summarizes it, the decisions it came to and either fires off a message to me for followup if it decides a new skill or tool should be built, or it catalogues the approach so that future analysis can identify trends in how I use the tools.

Over time it has built me a fairly decent stable of repeatable skills and tools, and highlighted process deficiencies and nominated process changes that I have pursued.

Another skill is a communications analysis skill; I started using it summer last year I think, and it scans my communications across a broad cross-section of my activity online. It tracks the commitments I make, ensures that I follow up with people that I might miss, ranks and scores my communication against my own personal targets that I set to make sure that I am communicating effectively. As a person who has had a decently successful career despite autism spectrum and unmedicated ADHD (I was medicated, but unfortunately each medication I tried had adverse side effects), it has made me much more effective in tracking work and following through, especially on the "boring" stuff that is actually critical to being a dependable team member, and effective partner for the teams I support.

Just a couple of examples.

You're putting a lot of trust into the judgement abilities of what is just a next token predictor there.

I can see what the goals are there, and they do make sense I suppose, but I'm not confident that what you're handing off there can be handed off to that degree.

But maybe that is not the point and the point instead is to see what the LLM thinks would be correct, and then think about that and collect learnings about the world from it. It might not be right, but it still tells you how normal people think. So that's useful.

Just a very roundabout way to achieve that, but that's fine, I guess.

"Another skill is a communications analysis skill"

It is interesting how people delineate what is a "skill". A 50 word prompt can be called a skill. This process you are describing sounds like it is highly authed and polling or hooked into multiple apps (slack?, text messages?, email?, forums, etc.) and then piping output to an LLM and to generate reports that it pushes to you based on output. You might need some data store to hold all the different communications locally as well.

That is almost a full on app/service that uses an LLM for one layer, but it is still just called a "skill".

Gateway drug is “/grilling” by Matt Pocock.

I made an agent skill for `tsort` and that unlocked a lot of interesting things.

Giving the agent an external tool to consider the sequencing of anything with dependencies led to some creative construction and offloading sequencing not unlike offloading calculation. (I also made a `bc` skill).

https://github.com/sj4nes/clanker-tools is where I've been riffing on this. My plan is to collect not "just skills" so much but "capsules" of reliable knowledge that agents can pull without confabulation. I'm already hitting the organizational stumbles, so this HN thread is right-on-time.

Skills are encoding a process. The more niche the process the more useful the skill. As the process grows to a larger audience it becomes more generic and thus converges with the models knowledge. So skills are better for a smaller group of people. And similarly how it's packaged and maintained becomes specific to that group.

This is it in a nutshell. Great advice.

Get them under version control. I have a git repo with my skills for software development [1]. There is an installer script that symlinks to the skills. Updating the skills on a machine is then just a matter of advancing the git repo. One of the skills comes with some bash scripts, but the rest are effectively just prompts. Putting project specific skills in projects works well.

I make sure they work by understanding every skill, reviewing pull requests, and testing the end product. The result is rarely perfect, so I am constantly tweaking the skills and how I use AI.

[1] https://github.com/gregwebs/skills-sdlc/

I commit them to git(so complete team leverages them)., each repo has kind of different skills and the skills are the ones which I update at least twice a week. I’ve skills on how to add instrumentation , debug, code, code review, tech design review etc. I found most of the skills I find on skills.sh are not very useful for me., but I browse occasionally to get some inspiration. One more paradigm I’m seeing good results on adding new skills is ‘how to do X’, for instance ‘how to add logs’., “how to review code” etc., if i’m not able to frame it that way I don’t think it’s a good use case for me to add that skill to the llm arsenal.

Another thing i discovered is less is more (in case of skills as well)., don’t add lots of skills., keep them very handful - I’ve got 9 skills so far (many people have 100s installed from marketplaces and plugins)

Thats exactly how I use skills as well and I got great results with it. I work in a proprietary codebase with a lot of niche or custom tooling, weird technical details and historical quirks. What skills do for me, is essentially skip the "learning" phase of an agent working in the codebase. With a fitting skill the agent does not need to read the tooling docs, look at existing repos and learn the coding style, but it can get to work immediately.

This is probably less relevant for code that exists a ton in the LLM training data already as an llm is probably competent to some degree in that anyway.

A big caveat here is though that now you need to treat your skills repo very carefully as mistakes in there can easily spread to all of the new code you write using a coding agent.

Last week I had to reuse homemade skills on different project. I very much liked the AI proposed solution and it works quite well: ship as a plugin and add your git repo as a marketplace.

The installation is effortless and I don't have to mess with symlinks as I may be working with same codebase on different platforms which would make things.. different.

  codex plugin marketplace add "https://path-to-my-git-repo"
  codex plugin add agent-tools@mycompany

  claude plugin marketplace add "https://path-to-my-git-repo"
  claude plugin install agent-tools@mycompany

Let the AI generate .json files for marketplace.

Haven't got to these bits yet, but I'm sure they will work as easy as install does.

  claude plugin marketplace update mycompany
  claude plugin update agent-tools@mycompany

Surprised it has not been mentioned, but I think relying on https://github.com/vercel-labs/skills is sound. It handles global installs for a wide range of coding agents. If I was working on an internal only skill I'd probably still use the same foundation.

To the extent that skills are contextual guidance (for this author, this project, etc) and not just (raw) capabilities they are unlikely to be eaten by models.

I maintain all my skill files in a central location (like dotfile management) and have guix home sync it to the skill folders of various harnesses that I'm playing with (codex, pi, antigravity, Claude Code, Deepseek harness, etc). They're set up to be bidirectional links rather than read-only like the default configuration, so I can keep editing them / adding to the corpus from any harness.

This works well for skills since all harnesses expect the same format, but is more annoying for other features.

EDIT: This is actually an example of a potentially useful skill. You might choose to manage your skills slightly differently. All you need to do is write a skill-management skill for your agents to be able to wire things up correctly / access them for edits.

Some other nifty skills/plugins in my experience: render latex equations, cetz diagrams inline, jujutsu, guix, code reviewer, writing feedback.

Don't know why the below comment by killix got flagged; it's a legitimate point.

In the current version of my setup, I've decided to accept that tradeoff.

But it would also be interesting to check whether agent behavior can be controlled well enough by a skill-management skill telling them to synchronously commit any changes with their signature; that would get the best of both worlds.

"Don't know why the below comment by killix got flagged"

Because it's obviously written by AI.

The main problem I encountered around this is that skills need to be edited across projects and across team members in a controlled way. Git is of course required for this but is not enough so I built a tool to do just that:

https://github.com/genged/capshelf

Using capshelf I manage my skills across projects. When I start a new project I can just:

$ capshelf add security-review

From the skill repo.

And if I create a new skill I can promote it to the repo so everyone can install it:

$ capshelf promote security-review

It pins the skill content hash so there are no unexpected edits that can break your flow. It also supports MCP configs and agent configs.

After a brief 'collect-them-all' phase, I scaled back to skills I've hand-selected & created to help me run repeated processes.

To keep me organized, here's the directory structure I use. So I don't have to think about it, I've created a skill for my skill folder that files any new skills in this structure as I add / ablate any that aren't useful.

   - `Discovery/` — Finding docs, searching, information retrieval
   - `Execution/` — Running processes, CI/CD, releases, deployments
   - `Planning/` — Goals, specs, tickets, threat models
   - `Tools/` — Reusable tool integrations (browser, notebooks, CLI creators)
   - `Debugging/` — Debugging, troubleshooting, fixing
   - `Expertise/` — (reserved) Domain-specific deep knowledge

How skillshare helps: symlinks across agents, syncs to my GH, and for the few skills I've pulled in from other repos, it tracks and handles updates. Every couple of months I review which ones I don't use and remove them.

I commit my standard set of agent configuration (including Skills) to this repo, similar to how many people have a "dotfiles" repo: https://github.com/yokuze/aix-config

Then, I use this tool:

https://github.com/a1st-dev/aix

to keep my Claude, Codex, and OpenCode in sync with my config.

The major benefits are that my adoption/deletion of skills, rules, hooks, MCP servers, etc. are deliberate, versioned, and shareable.

And since aix allows you to create extensible configs, that aix-config public GitHub repo is my base set of rules, and my internal/employer-specific rules are just a small layer on top of that.

It also makes trying out different subscriptions easy: you define your config in one place (one source of truth), and write to Claude, Codex, Devin, etc. with one command.

interesting project mate. I have struggled with this as I built my workflow to rely on Claude, but when I put Codex into the mix it became a bit of a hassle, will check it out.

Cheers!

There's also a command you can run to convert your Claude config to what Codex expects: `aix sync claude-code --to codex --dry-run` (just drop the --dry-run flag if the result looks good to you).

More here: https://aix.a1st.dev/editors/migrations/how-to-migrate-from-...

Feel free to suggest any improvements or features you'd like to see, or let me know if you find any bugs.

5 Stages using local GIT (no remote, don't need it) to prevent preloading in the prompt:

1. A single Skill finder skill, loaded in the prompt, prevents having to import all the summaries in the prompt the harness would add. Uses git's own search.

2. Private repo, per agent, contains main (production) and draft-<name of skill> branches.

3. Shared repo, like 2, but general access for all group agents.

4. Fallback mode, search the harness for skills using the harness mechanism when a relevant skill cannot be found.

5. Skill audit cron. Identify junk skills / drafts that have never changed / not in any recent sessions history, and categorise monthly for me to decide.

This means it's compatible with existing skill folders, removal of git and the finder skill is non destructive and critically debloats the prompt of skills that aren't used and lazy loads them when needed.

> "make sure they actually work?"

I've been working on a tool (https://dynobox.xyz) that acts as a deterministic integration test / behavioral test layer for some of the skills i've been working on / sharing.

It feels like a full eval suite is a bit heavy handed and really all I care about is if certain files are touched / left alone or if my skill is actually read. The tooling has much more functionality built in if you want to check it out!

For skill files / prompts I share I make sure that I use the cross harness functionality since I use codex but a bunch of my coworkers use claude (and then one using antigravity...)

I manage them as part of my dotfiles using chezmoi. A `.agents/skills/` directory + a symlink to there from `.claude/skills/`.

> Do you keep improving them over time?

In my global AGENTS.md I have a note to agents to explain any frustrations they had doing a task, and to suggest any skill/tool/AGENTS.md improvements. I am trying to keep AGENTS.md files small but still finding the balance.

We have an ongoing discussion and some fans of https://microsoft.github.io/apm/

Because it's from Microsoft and sounds sufficiently enterprisey probably.

for skills related to specific cli tools, i just wrote a standard for this! it's obviously not widely used yet, but since mise will support installing the skills alongside the tool, i suspect it will have decent adoption

i used to be a bit bearish on skills—thinking that llms should just use --help, but i've come around on that. i think skills are a great way to describe higher level workflows that use multiple commands.

https://jdx.dev/posts/2026-09-05-introducing-packslip/

Here's how I do it:

- Explanation: https://www.minid.net/2026/7/14/how-to-automatise-with-ai

- Git source: https://github.com/meerita/monorepo-nextjs-golang-rust-pytho...

Personally I have found "meta-prompting" to be much more useful than any pre-canned set of skills. Simply ask the AI (inside of a workspace already set up):

Create a standalone prompt to <xyz>

The latest AIs will print out a long prompt with all of the assumptions, tools, and general files it plans to use. Review that, and then run the whole prompt in a new context.

Skills that are so generic that you can find them on the internet, and which you think can be replaced by model improvements, are useless, possibly even harmful, considering how much the models get clingy to the context. Useful skills describe workflows specific to your project, and they can live in the project repo for everyone to use and improve.

Don't find them. Ask the AI to do something. When it does it correctly, ask it to make a skill for it. Clear the session, try to use the skill, fix any problems found, modify your repo and harness if necessary. Repeat until skill works 0-shot. Improve with the same process.

This largely works with a specific model, specific harness, specific prompt, specific context. You may need to modify your agent harness to manage skills depending on runtime parameters. Pi is a great general purpose agent for the these modifications.

If you do find other skills and want to use them, put them through the loop above. But keep in mind that since they were created in their own circumstances, they may not work in yours.

Also separate rules from skills. Rules tell AI when to do things, skills tell AI how to do things. Tool call/MCP limitations, agent configurations, and harness extensions, can help it stay on track.

Im talking about skills like design critique, landing page creation....ect

We keep the skills in a repo, where an agentic workflow runs biweekly to check if their content drifted compared to the docs and opens PRs if they did. The repo is also a Claude plugin. The biggest problem is keeping skills up to date across users, so I developed a small Go binary that takes care of that across harnesses.

That's very cool. How does the binary keep skills updated across users?

It clones the skills repo if not present and relies on the git last commit as the "version".

> How do you find skills

I try to keep my collection of community skills short, usually a few established names (mattpocock, mcollina, trailsofbit). And then I check new releases (or when mattpocock published a youtube video for instance :D)

> keep them organized

For skills I wrote myself, I have my own private github repo. I use skills like /commands most of the time, so I can tell if they work straight away.

For community skills, a package manager really helps. vercel-labs/skills and withastro/rosie are good options. I also built one myself: https://github.com/osrim/ski. It has some cool features like an update command and a security scan.

I have an old colleague who I've been leaning on for ai things (they're more on the pulse than I) who uses cue (cuelang.org) for distributing and formalizing a set of skills/prompts.

It's the best thing I've come across (that I don't need to mange myself) https://github.com/p3bot/start

The tool itself does more than just manage skills/prompts but I found that part of it particularly good (well new to me; not familiar with cue but the idea seems like a good fit)

I mostly prompt my own skills, and add a self-improvement directive (to those I think can use it). Usually if a skill isn't working (well) there will be extra tool calls. Actually that's usually the trigger to create a skill in the first place: multiple tool calls to do a repetitive task, where those tool calls can be reduced. But from comments on most AI-related posts, people are hardly-if-ever reading the agent transcripts (and the self improvement directive doesn't always get triggered) so their skills never improve.

I wrote a small command-line tool that installs skill packs into agent-specific project folders. It works pretty much like `brew` (or any package manager, really). The skills are compiled into the binary so that I don't have to worry about where they're located and can quickly move the skills between machines by copying the tool.

Making sure they actually work? Trial and error, mostly. I know some folks have tried auto-researcher approaches, but I haven't found that to be the best use of time in my work.

When I work on a new problem and the agent struggles with it, I will ask the agent to distill the knowledge and experiences it gained during the session into a skill file. Then I review and publish it depends on the assistant system. I find this is an effective way for the agents to learn new skills, both from its own discoveries but also from my steering and the mistakes it made.

If you work in a niche or on special problems, this template could be useful.

We have some company-managed skills, that help coding agents find the relationships between our repos, and our conventions, architecture, and other high-level decisions. These are supposed to be portable between agents, and so distributing them is currently awkward.

We have a bootstrap script to deploy company-managed skills to each developer's "personal" skills. Hooks for codex and claude code try to refresh the skills on each startup.

A few simple rules worked for me:

  - start with zero skills

  - add a skill if you encounter behavior that you want to ward against or if 
    you want to associate a meaningful phrase with a certain method of doing things 

  - NEVER copy a skill from someone, do not clone skills repos, do not let LLMs
    write their own skills

  - occasionally revise or delete a skill, less is more

I basically create a skill when I'm tired of always writing the same prompts, and then I adapt it over time.

I have like 3 skills, and so far so good, most of my recent changes have been asking Claude to please stop using metaphors and creative figures of speech that make the documents so much harder to read and understand (maybe it's only annoying to non-native speakers, I don't know)

Skills live in two source-of-truth git repos (private and public). Agents edit skills by my request, and syncs to all coding agents ~/.claude/skills/, ~/.codex/skills, ~/.pi/agent/skills, ~/.config/opencode/skills etc. with agent written sync-agent-skill script. script ensures that no local changes was made in-place.

Why do you use so many different agents if I may ask?

I mainly use Claude Code, but I had an idea to build an orchestrator for coding agents, so I experimented with several

Codex and Claude Code both respect ~/.agents/skills; you don't need to have ~/.codex/skills and ~/.claude/skills .

I create and refine my own skills and commit them to my dotfiles repository.

I'm pretty late to this thread, but I battled with the whole "keep them organized" issue across 20+ repositories. I see a lot of folks suggesting scripts and symlinks, but I stopped using that approach because filtering (i.e. figuring out which files belong in each repository) and syncing became a headache.

The Agency HQ (https://theagencyhq.dev) is another approach that uses a central repository of skills, rules, and other files that are pushed down to developer machines and then placed into project directories as needed. It uses a simple daemon on to pull down the files and distribute them. And it supports filtering and translation for most of the popular agents.

Disclaimer, I wrote The Agency and it is in early stages. I'd love feedback if anyone gives it a test drive.

I've been using a project called SX https://github.com/sleuth-io/sx to manage them. It has its quirks, but overall I find it to be very useful.

One thing I've had to write as a layer on top of it is a way to assemble agent-specific CLAUDE.md / AGENTS.md from fragments.

For example, I have a little fragment that has all agents respond to me in ordered list format. (Since they often ask a bunch of question all jumbled throughout a response, the ordered list format allows me to respond to those specific questions.) And I also have a growing anti-Claudeism fragment as well.

Then I combine this with project-specific fragments and have it assembled into into a single CLAUDE.md / AGENTS.md. The layer also does a little reporting on the length of the resulting files and notifies me if it ever grows beyond a certain size.

>I believe skills will eventually be eating by model capabilities

a model capability is never going to fill in an unknowable blank that a custom skill (or whatever equivalent your paradigm supports) can.

a model might have the cleverness to whoami and look through the .ssh folder for keys and evidence of past connections when asked to connect to bob, but a skills file can just easily say "We connect to bob using key Z and user X." so that the operation gets done without all this nonsense needless inference as far into the future as the information is valid for.

a concise information dense skill is going to always dominate on tokens-burnt for any given task that requires insider knowledge. it simply gets rid of the entire investigative phase of work.

Agree here. My philosophy is the "general-purpose" coding agent will keep getting better and better, making skills less and less useful. And it will probably get better at a pace far greater than the customization folks can build around them via skills.

This of course is from my own experience writing code, where agents are already good at software engineering conventions. This probably doesn't hold as well for other tasks, say writing marketing copy with a unique voice

For now, I keep skills pretty minimal - single sentence prompts I send all the time, like "Remove all the slam poetry from the docs in this repo."

I also tend to share often. All skills go into a repo my team can access. No pressure, use them, riff on them, add your own - sharing and engaging on how we do the work is more important than making everyone do the work the same way to me.

What I meant by skills getting eating by models are the "general use" skills, like design critique, code review...ect

But, for custom use skills, ofc no model will be able to replace them and it's not efficient to try to do that as well. For this type of skills I create and maintain them by myself, my question was about "general use" skills, they are everywhere on the internet, how do you manage them?

Do you find any of the general use skills useful? I'm not sure I've ever used any of them, and when I've looked at them it's been some YouTuber trying to make money. That, and their Substack.

I know everyone's down on MCP, but custom-built client side MCP tools are what I find useful instead. But that's me.

Honestly Im not able to verify, I just throw things at the model and iterate with it. The most useful skills are the ones that I create

“ determinist harness around the agent ”

Can you explain what this means?

If you can express something deterministically with code, it's better to do that rather than have an agent do it, because it's faster, cheaper, and deterministic. E.g. you regularly copy file A to file B. You can ask the agent to do it, or you can write a script and have the agent call the script via skill. That's the beginning of a harness.

Eventually you arrive at building custom software that does a lot in the traditional way, but delegates certain tasks to the model where it makes sense or it's non-trivial/impossible to express via code.

That makes sense. Sounds similar to what I am usually doing. I let AI write a python script or similar, review it and then use the python script. I don't think I would let AI do anything important directly.

How do you disseminate that information to humans?

I keep my skills in a Home Manager repo and install them into my .claude / .codex / whathaveyou directory through the home manager config. I'll know if they don't work because they are specific instructions on how to git commit, how to merge code, how to author text (without the typical AI tells), or API usage documentation for specific libraries, etc. If they didn't work the agent would do things incorrectly and I'd notice.

And sometimes it doesn't follow the instructions well. I have a skill for that too: it tells the agent, given what it knows about attention and LLM:s in general, to evaluate the instructions and the mistake the LLM made, try to diagnose why it didn't follow the instructions as expected, and come up with an improvement of the skill based on that diagnosis.

I have created a little system for this, placed directly in the ~.<youragent>/skills folder.

I have a configuration file of marketplaces and other skills to fetch, it can look like. I have my own marketplaces as well, including ones from my company. I use vercel's tool for managing skills with npx, but to easily handle specifically _which_ skills to fetch, the config file is set up as follows:

  SOURCES = {
    'some-marketplace-name': [
        'some-skill',
        'another-skill',
        'yet-another',
    ],
    'https://designsystem.yourcompany.com': [], (empty list: fetch all skills)
  }

from there I simply run "skills.py" (a single helper) to clean/fetch updated versions of the skills.

For the my branch of the Norwegian Government we have a public skill registry and a tool to sync them locally according to what «profile» you select, https://ki-utvikling.nav.no/verktoy

Source at navikt/copilot

Lookup the Claude managed agents architecture for skills, they have a kind of progressive exposure where skills have a title that triggers the skill, an index file that is loaded when triggered, and additional files and scripts that are available once triggered but not loaded by default.

I recently completely overhauled repo’s skill setup.

I tried to control the execution of tasks performed by each project using claude.md within the project, but claude.md is only read at the beginning of each session, so it felt like the instructions weren’t being properly reflected.

So I revised the strategy to manage frequently used features in skill units. In doing so, instead of organizing skills by project, it was structured to be integrated into the general skills of the individual repo.

When skills are spread out across multiple projects and the number increases, it becomes impossible to keep track of which skills are available, so they end up not being used.

I also think that eventually, once Claude(model) advances, it will be able to replace most of the skills, so I believe registering and managing countless skills actually degrades performance.

I am testing out Skillshare https://skillshare.runkids.cc and I am using skills.sh to find skills.

You can have your own skill repository with Skillshare and sync across agents (symlinks or copys).

Have your agent make a plugin marketplace -- mine is here (feel free to crib): https://github.com/photostructure/coding-skills

Be sure to increment unofficial plugin versions when you make edits: codex's auto-update works reasonably well, claude not so much, but when asked, both can fix their own config.

And like others have said, imho the skills that are incanted as macros are much more reliably useful. I use my technical project plan skill suite in 90% of my sessions via direct reference, and the stage -> cross-model second-opinion review is how I land all my commits.

Codex and Claude Code support plugin symlinks for local development; if you don’t mind keeping a local clone of your skills repo they can just use it. Claude needs a shim, as it wants to cache plugins; it’ll run a shell command (!) locally to get the plugin path. Codex supports this natively.

Here’s the spec I used for my skills repo:

https://claude.ai/public/artifacts/d37077a6-2cdd-4961-b504-b...

I’ve been keeping all skills in .agents/skills and symlinking .claude/skills, this way I can use them across both claude code and codex. One of the not so straight forward things is knowing what frontmatter overlaps, or subtle config differences exist for skills between tools, and making sure to take that into account.

I wrote a free tool to constantly diff the agent configs for a handful of tools I use. If interested you can check it out at https://frontmatter.news

I'm on mobile but the first skill I made was a skill improvement skill. This is basically stating that if the AI struggles in another skill but finds a way that the skill it used needs reworking and it needs to do so.

There is a rule to always use this skill and then track notes in a version file. Then back it up in a share folder or external drive.

Skills have made my tools immensely better, cheaper to use and faster. I've also added to it that it should write scripts it can just use in the future to do tasks like query information it needs to answer questions.

I wish there was a better way to share these over a team but I haven't taken that time yet.

I read somewhere that Boris (created Claude Code) recommends deleting skills, and hooks every so often and then observe how the LLM performs without them.

Maybe better to periodically prune: tweak some skills, shorten some, delete some.

observing changes is slow and very subjective. i ask ai itself to update skill once a while

I have CC do a review / audit of my entire CC stack and architecture every major model / harness release for recommended changes. What goes into my CLAUDE.md files has been shrinking over time. There is a skill review tool as well which helps determine how effective they are.

Skills are for me special knowledges that an agent in general doesn’t know in details: using a specific framework, create reports, new programming languages etc. So having them not only enable the AI to work more effectively but also more efficiently and cheaper (why digging through the ten thousands lines of help again if it was already distilled?). I don’t use SKILLs for quirks and gotcha. They go into AGENTS.md because it’s always loaded and therefore much more reliable (there is a study about it). I consider SKILLs load-on-demand even though the agents can discover it and decide to load by itself.

I don’t seem much point to intentionally curating a set of skills, and specifically invoking them by name, only to watch the ai skip them all and do better by just reading code and internal/external websites.

SkillCatalog (https://skillcatalog.dev/) - stored in git, managed via desktop app (macos) and and CLI

full disclosure: I'm the author

I use an agent plugin spec repo. Codex is already compatible with it and it supports skills + MCP definitions.

https://agent-plugins.org/

You might want to look at this CLI that Tencent released recently.

https://github.com/Tencent/teamai-cli

I need to manage skill files across 2 Macs and 1 VPS, and also across fast inferencing APIs vs. very slow local models.

The first dimension is easy: I simply keep copies of debugged skill files in iCloud and copy them where I need them.

The second dimension is where I spend my time: I use short skill files for fast inference APIs and tiny skill files when I am running slow local models, and I simply spend a lot of time writing and tuning tiny skills files.

Of course, with increasingly better models, skill files become less relevant, but not totally irrelevant.

I find myself spending a lot of time updating skills, which is not wasted time, since they are at the system design level work. It's how I automate myself ;)

I have a repo/project called Loadouts & Summons. It has a primary skill, `capsule`.

All skills, MCPs, CLIs, etc. live inside of it. I have it symlinked to all my dev machines so that it doesn't have to be an MCP.

`capsule` is then progressive to dozens of skills/tools thru `capsule` -- ex. `$capsule plannotator [args]`.

In some harnesses, I make it human-invoke only, and call it directly. In others, I let the model invoke it, and it has a top-level description that hints at what's inside.

Maximal context/session start control and capability extension.

If your problem is keeping skill files organized and easy to maintain with a team, Marktwin might help. It lets you edit and review the Markdown in your GitHub repo together without moving it somewhere else. https://marktwin.com. Feedback gives me a good excuse to procrastinate and work on it. Is open source on GitHub.

I haven't used skills files at all. I think my codebase scaffolding and AGENTS.md just kind of does everything I need.

E.g. https://github.com/eliask/lawvm/blob/master/AGENTS.md

EDIT: Ah, but what I do instead is I constantly refer to my various public essays. I think it's very useful to have externalized thinking like that available for use with LLM contexts.

Mostly use homegrown skills. For shared skillset at work, it's a standalone repo with a script for everyone to install the full set. For a personal collection I also use a script to sync a few upstream ones to keep everything in one place.

For evals I use the method outlined in the `skill-creator` skill from Anthropic.

In the skills, I try to use scripts, along with templates and json worksheets, as much as possible to scaffold and validate the work to make things more consistent and reliable.

I run AI on my server. All skills and relevant info is saved to a Wiki. Agent only has an instruction to check the wiki (MCP) at the beginning and get the necessary context.

I think people undervalue skills on the cross repo boundaries and how systems interact with other systems.

For instance… how to deploy a service or new service’a docker container. Get secrets in value blind, manage secrets value blind. Those sorts of things have been wildly valuable. Also due to the nature of skills and how they are pulled in by your harness they can really prime the context in a way that is really useful to agent autonomy if that is your thing.

For things of global relevance, I keep a git repo in ~/.skills. It has a script that syncs skills across agent harnesses when run. If I think of an interesting skill that seems like it'd be broadly useful, I put it in this repo. If I want to use someone else's skill, I read it with my eyeballs, decide whether I like it, and then copy and paste it into the repo.

Disclaimer: I hand curate them in the end

I keep most of my sessions in Zed (you can import them there anyway). After some big feature I let a frontier agent go over these sessions and suggest improvements. Typically I use gemini for this because it's really good at pruning text. Claude/GPT really wants to append more text for some reason.

I end up with smaller skills but more "actioned" skills. They kind of force the agent to do things the way that works well.

I use SKILLS.md to define workflows rather than having instructions in them. Like, delete the foreign keys in the database before loading the table using AWS DMS for CDC. This is required because LLM may not be aware of why we are deleting the foreign keys in the DB in the first place. The SKILLS.md helps the LLM to identify the tables for which foreign keys needs to be deleted before they are loaded by DMS for CDC.

I like this usecase, I could have used it myself. But I actually moved out of DMS to OLake, its open source and gives ways to operate it via both UI and CLI. But thanks for sharing this!

I don't use them.

Everything is organised into repos, i select the directories with the context the agent needs for the task. If I want it to adjust something in my homelab, I drop it into the homelab repo. Stuff agents need to do commonly has shell scripts to speed it up.

I do however have some system prompts. I pick the prompt based on the goal, whether I want to implement something, or just web search, or just need a short one-off command to be done.

I asked that question myself a couple of times, which is why I created a side project to answer it.

I built Skill Grill, an open-source directory and community trust layer for AI agent skills.

Its live now at https://skillgrill.dev

I'm not sure about posting links in threads but this project needs community backing in order to work and its free :)

Its still in MVP stage, but feedback is welcome

There is a very similar connection in the relation of skills/mcp/… and everything related to a prompt: they are all just a special-purposed prompt named differently. In a way, it reminds me of the concept in OOO: we give lots of names to “design patterns”, even write books about it. But in the end, they’re all based on polymorphism. Know this fundamental and I feel less overwhelmed.

Can you explain more?

by the way Alan kay was right, tech is pop culture. We keep re-naming things.

One thing to consider when you do look at how you manage your installed skills - is the security side of them.

You also need to manage the authority of each skill too. Signed skills is a step in the right direction, but it only proves provenance and doesn't prove behavior.

I don't use skills unless I have something specific to tell the agent. For example, if I want the agent to use Tailwind V3 instead of V4, I'll have a skill for that. Or, if I want the agent to always use the repository pattern for database access, I'll create a skill for that.

I don't need to manage skills files because I have so few of them and they're only a couple lines long.

If I have a session with something that I expect to do it again, I ask Claude to make a skill out of it and store at user level somewhere at ~./claude/skills I think, so next time I can do just "/xyreport from-to" for example and dont have worry about leaving out things from the prompt or to rediscover some gotchas the agent ran into.

GitHub. Then you symlink them into the relevant folders. You can write a skill to manage them for you across all harnesses as well.

going to do that, very helpful!

i have a docs/

it has all the skills/docs my particular application needs

i treat it as ADRs as it helps the AI understand the parts of the system it is working on

I use a handful of skills I keep in a repo (to track updates) with an install script. Mainly is to replicate them across my various dev computers, and to share them with the team.

https://asmat.ca/blog/mad-skills/

I've used this to install some 3rd party skills: https://github.com/vercel-labs/skills It allows to install and update skills.

Caveat: It works for Claude Code and Codex, but does not work for Claude Desktop.

Instead of managing skills as files, I have been using a simple utility which helps me create, update/attach skills and finally search it across sessions https://github.com/viggy28/recall/

My agents use https://rcarmo.github.io/projects/memento/ to manage shared skills and propose changes. But I also have template projects with skills baked in for some scenarios.

I've got a skill repository on github, and I got a hermes automation to sync skills repo - this hermes automation is on every device. Any skills I make on the fly - my global AGENTS.md has instructions to update the skills repo path and place them appropriately in there.

I make skills to allow AI to integrate with my platform; it's documentation with cURL commands. It can interact with every aspect of my platform via HTTP and access its full capabilities. I can tweak its token permissions as I like and revoke access if necessary.

I only use skills that are docs of software. Anything else is pure garbage.

They get pinned with nix together with the software that they come from.

It's just two 3rd party skills now:

playwright-cli and herdr.

All the rest are skills for the software itself, so they live in the same repo and get updated the same way docs get updated.

I store mine in my prompt/skill drive at https://prompeteer.ai/skills-hub

I've put mine inside `~/.agents/skills` and `ln`ed all `skills` folders that I could to point to them. It only doesn't work for Claude Desktop. But it works for grok, antigravity and cc CLIs (for some, at least partially).

It's a bit of a shameless plug, but I do hope it is helpful to you:

I've been using this heavily the past 6 months to solve exactly this problem: https://github.com/a1st-dev/aix It doesn't support Grok (yet) though.

Oh, seems it supports Grok now. Seems very interesting. Will give it a try. Thanks!

Anyone got a good flow for sharing their skills across their various repos and apps?

I ideally want to have a single place I store my skills with an easy way to make them available to my repos, Amp and ChatGPT desktop/iOS, Grok web but can’t see a way

Yes! See my comment here: https://news.ycombinator.com/item?id=49601053

Though it does not support ChatGPT (just the Codex part inside of the ChatGPT app), or Grok web (yet).

But it does encourage you to keep versioned config in one spot. You can also commit repo-specific config to any project that has specific needs, and translate that config into . That can be helpful when working with a team or an open source project that needs to support a variety of agents/tools.

It currently supports 8 editors/agents: https://aix.a1st.dev/editors/supported-editors/

Nice! Will check it out, thanks

My friends and I created skulto to help us manage and install skills across projects and workstations.

https://github.com/asteroid-belt/skulto

Like you said, I think skills are quickly subsumed by model abilities. I leave relevant notes about my own projects in agents.md and local comments around tricky bits of code that need them, but otherwise I just use vanilla codex.

I think a lot of people are missing some of the advantages of skills - they're to give an agent/harness the knowledge of how to do something or more context without it always being part of the prompt.

This could be to try and expand the capabilities of the model but in it can be used to store personal preference/workflow/organisational knowledge/improved efficiency etc. The harness could work through and build some of this on its own - but it's much more efficient if it has a document it can just look up. I use them for the database structure and it's quirks, how to use branded document templates, external document tone, system workflows, compliance requirements etc.

I wouldn't want these to load every time but being able to call them is really useful.

Nowadays I often create skills myself (or with the aid of coding assisants) for any repeating tasks. For example, I use my own skill to make Claude CLI send the worktree to codex cli for the review, then read the verdict and make changes

Skills files only help when the model can't infer the procedure from the repo. Most of the time a good prompt does the same job.

I manage them in a GH repo in synch with local, with regular checks and updates.

https://github.com/boraoztunc/skills

I have a custom skills registry which allows dynamic retrieval in any harness, scoped by directory for permission (including global for super useful ones - output pdf for example).

I’ve tried to keep up with “best practices” around AI use, but things are improving so quickly that I’ve largely given up. The vanilla agents are just fine for my needs as they come.

Can you elaborate more how do you setup vanilla agents ? Which agents you use and which use case that it’s greatly show benefit for you. Thanks

That’s what I’m saying, I don’t set it up. I just install Claude code and I’m good to go.

Managing configuration files feels like worrying about fine-tuning in 2023

Why do you think like that? "I believe skills will eventually be eating by model capabilities, but until then I'm just looking for a better way to manage things."

I have my skills in my dotfiles repo, then symlink them to my home directory and/or projects where I want to use them. Project specific ones go into the project.

Skills is just a tech bro word for a simple markdown file with instructions.

No need to over complicate it. Write down things you feel like re-using. Like how to specifically implement something in your system ("when adding a new API endpoint we need to do x y and z", or "when making a github PR we tag Æ and Å") so you don't have to repeat it. And I mostly add it in cases where it didn't infer it itself. So very reactive, not proactive.

Most public skills are useless and over complicated. Lots of people are spending too much time on their harness, than actually making stuff.

Edit: but do get inspired by public ones. For instance a "grill me" skill can ve be useful, but I find the public one very mumbo-jumbo. But the idea of forcing the agent to ask clarifying questions is good.

My genuine question is:

Are there any "skills" at all that have proven to be useful? And if so, what's the context?

Because, for me anyway, LLMs usually do one thing, and that then produces a durable artifact. So the prompt that got me there by that point expired and is not really needed anymore.

I also occasionally have recurring tasks (rarely though), but there, the prompt to do stuff is embedded in code that orchestrates the doing, so I have no use-case for that either.

___

For the "add this endpoint" example you've described, I just throw commit IDs at the clanker and say "go do that again". That works, and doesn't decouple knowledge from code.

I believe skills are much more than a simple markdown file with instructions. They are a very powerful script engine. How I organize it is that of course there's a markdown with instructions but I split the work in a hybrid of script + instructions. So all the work that can be deterministic is a python script api surface and all the logical or thinking work is in instructions. and agent is also instructed on how to use the api of the python helper functions. This makes it almost like a normal script but the runtime is a harness and the business logic can be any combination of code + human-level intelligence.

So I like to do all the edge case handling and validation etc via a helper function, and the agent is simply instructed to call the function to do something. It is extremely powerful and a completely different way of automating things. I am constantly forced to re-think how computers are supposed to work and its limitations.

reduce and simplifies your skills periodically . And use workflow to separate skills ,not by functionality . The workflow would be simplified but will always be useful as long as business runs. Such as the software could be build by C/C++/Java/Go/Python but the workflow based on software keeps live.

tbh I use 1 custom skill (workflow for completing a github issue and opening dev server/PR) and have it checked-in to my main project repo.

For me, it's like a dev script basically and gets that level of care. I don't need an eval... I'm the only user and I use it like 5 times a day.

github repo with a sync skill (ha!) that copies to the global skills on each computer, and also checks and updates the github repo with any skills that got... well.. updated.

at best, skills keep me consistent. at worst, its a prompt repo.

I like skills

I have a separate repo which has to be pulled locally and the skills and agents are sym linked to projects.

Any skills, I just add into the tool itself. I then have the py tools in their PWD, don’t bother with mcp.

Openspec has a subcommand (init) to manage them: clever because they provide also an update path.

I don't like skills. It's a really annoying form of technical debt, especially when people try to fill up company repos with random skills they think are so cool. Same goes for polluting a repo with custom instructions.

I only want the model to have the tools it needs to get the job I ask of it done.

the only ones {i use,claude decides to use} regularly come with claude code plugins so they automatically update. i just define the marketplaces and plugins in my .claude/settings.json for the project.

i use an agentic host folder, full methodology here: github.com/connollydavid/host

i then A/B test skills for terseness with weco’s auto-research within this using a much weaker model e.g. Qwen 3.5 4B

Treat skills as runbooks, not as a way to turn your agent into an expert.

Two ways I use skills

1. Personally - these are for apps I use like Codex. This one is pretty simple

2. For our product (which is mostly an internal tool, with minimial customer facing UI) - we have a skills factory that lets our employees create reusable instructions for our internal agent (fully custom built harness with routing). User describes the task and writes the instructions (can attach reference files, etc.). Custom skills and their versions are stored in postgres, with attachments in file storage. The assistant loads those instructions and references when it uses the skill. To update a shared skill, users edit a draft, test it, and submit it for review. Once approved, that version becomes live. Skills can learn or rewrite themselves from conversations (cuts a new draft and prompts the user if they want to update/improve the skill).

EDIT: I should say that our employees have a particular set of expertise and knowledge that make skill sharing insanely useful. Which is why I took the time to build this out. It's helped reduce manual work, and has increased our AI usage drastically. We also measure AI output (in terms of quality) and the reduction in slop has sky rocketed.

“Context Injection Library” might be more accurate than Skills per se.

by not having them at all.

The internet has broken me. Whenever I see a question like this i now automatically expect it to be some marketing attempt. There will be a product/service/blog post somewhere in the comments.

I usually use this technology called Shift+Delete

Never understood the “skills” part tbh. Thse models are being trained in trillions of texts. Adding something small and particular about my codebase doesn’t really move the needle

I think it's developers coping with not coding anymore.

There is this urge to create a non-ephemeral library of at least something.

I don't manage them, they just kinda rot

i haven't written any skills and it works fine. I do use Agents.md for repo and that's all.

I blocklist all of them.

I allowlist one going over our issue management system, and a few around internal processes I believe have value (basically very short pointers at OpenAPI specs for making HTTP reqs - could probably be in the repo AGENTS.md but w/e).

When opening a new repo I'll look if any skills look like they actually give helpful context and aren't Claude vomiting out a torrent of text (over)fitting some insanely specific use case and allowlist those.

Or give up after too much exposure to vile AI created text.

Great responses in this thread

I find the whole Skills.md idea a little silly. We have a technology that struggles to stay within rails due to its inherent makeup. And you think you can fix that by just telling it to?

It makes as much sense as the so-called “humanizer” tools that purport to make LLMs stop using their well-known verbal tells.

All you are doing is saying: “Hey you know that thing you can’t stop doing? Can you stop doing that?” The machine will say “Absolutely!” but eventually start doing it again.

You’re not wrong, but I don’t see a lot of alternatives. So we just don’t try to fix it? At least skills give us a landing pad for “here’s how to attempt to do things consistently in a way I generally approve of”

I use skills all day, every day. One of my greatest annoyances right now is having to switch between `/` and `$` in moving between CC and codex. At one point I attempted to hack the CC TUI to accept $ but failed, I may yet return to that.

All of my skills are custom to my workflows except Contextify (more on that at the end) This extends to how I distribute them across multiple development machines.

They live in my `cli-ai-setup` repo alongside agent settings, git worktree tooling, iTerm workspace restoration (important, machines have to restart and crash sometimes), code review scripts, and machine setup guides. I use git to carry changes between machines and a setup script to symlink the skills into a shared directory that Claude Code and Codex both use.

I have a `skills-and-settings` skill specifically for deciding where new skills belong and how to make them available (project, global, application). I also have a custom `skill-create` skill that turns sessios into new skills or updates existing ones.

Importantly, I also have entire custom applications I have not yet made open source that my cli-ai-stack relies on. I do expect to distribute these so they live in their own repo and are symlinked or installed in as appropriate.

For maintenance, I've largely handled this manually and organically. When a skill is not performing, I'll use the context of the situation as the ~1 shot or pull in more examples for the ai:

   This skill seems to not be performing as expected on [something]. 
   
   This has happened a couple of times now use /total-recall to find similar recent situations for example [something I remember]"

   Recommend updates to the skill and upon approval commit and push them...etc.

My other machines watch this repo and the symlink structure means that the updates are carried into live cli-ai sessions almost immediately.

This past week I was exploring the automatic skill improvement behavior described in the Anthropic blog guest post with their partner org. I'd previously build a "dreaming" skill that works okay and think there may be some value yet to plumb there.

For skill creation, I have a skill that reads the official skill docs for both Claude Code and Codex. This way the skills are built to handle both platforms particularities. I automatically pull those docs into local Markdown daily, so it has a regularly refreshed reference for what each tool supports.

As mentioned above, I have built Contextify (https://contextify.sh) which provides a sql database of all of my Claude Code an Codex session transcripts across all of my development machines. The skill for this (/total-recall) is the most important skill I have and I use it constantly.

Skills are workflow caches

I usually rm -rf them.

Not really related but I wonder how people benchmark the effectiveness of skills/agents?

I'm seeing the agent working quite fine with just direct prompting and the agent doing things by itself rather than using skills. Is it better for certain task size?

wdym "your skills files"

It’s all nonsense. Just ask it for what you want with natural language. Why would you want to reintroduce all the random magic incantations we’ve had in tech for decades.

5% git

95% rm

I am yet to find a skill useful. It’s usually just vibecoded slop-bloat for a one-off somebody thought they document in a markdown file.

They're useful for one-offs. I don't manage them though.

how did stupid markdown file become a thing, this industry really went to shit.

Should be .txt

With a custom skills workspace in mininote

https://mininote.ink/docs/mcp-docs

Agent can use mcp to update its own skills, or I can copy template skills into local dorectories via the api. Very useful, like notion on steroids but is completely free.

Skills are no longer useful.

I find them very useful.

I have been finding them decreasing in the effectiveness with each model release. We got rid of skills and built a determinist harness around the agent instead.

I use skills to automate non-development workflows, like for malware analysis, etc.

WTF is a "skill"? I really think people are getting ahead of themselves here.

You wrote some bullet points so your agent harness doesn't keep making builds in the wrong environment? You have a very specific debugging setup? Your agent doesn't understand when to rebase?

README is where you should be writing anything specific to your project, and if you're worried about context size then your README is too long, it should be just enough information for any competent dev or agent to get the gist of how you do things around here and where to look for deeper answers.

If your particular harness / orchestrator is just not pushing back enough or can't seem to solve certain problems then thats a tool issue, either edit the tool system prompts or move to better tools or models.

Calling this 'skills' is disingenuous, this word was chosen by marketers and implies some kind of deeper learning. I'm not saying there's no value in tuning prompts, but your 'skills' should be managed in only 2 ways: 1. It's specific to your project, it's a README, or 2. It's specific to your tooling, it's part of config, system prompts etc.

I mainly use them as macros. Not even an advanced and smart macro system, just plain basic macros to avoid copy-pasting recurrent prompts. Is there more to them?

a skill gets injected into context in a certain way, and behaves in certain ways with respect to things like compaction.

the readme and agent.md are not the right places to put long workflows worth of llm instructions, and also isnt the right place to put global details.

skills also get a piece of text injected into context all the time, so the agent gets a hint to go read and follow the whole thing, without needing to embed the whole definition into context all of the time.

One of the engineers I know is building this product called SkillEd for just this. Lemme know if you need an invite

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