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.

- 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...

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.

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.

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.

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.

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

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)

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.

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.

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.

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.

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

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 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.

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.

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 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.

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/

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.

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 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 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.

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

https://agent-plugins.org/

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

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

>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.

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

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.

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

Skills are no longer useful.

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