- Explanation: https://www.minid.net/2026/7/14/how-to-automatise-with-ai
- Git source: https://github.com/meerita/monorepo-nextjs-golang-rust-pytho...
81–90 of 327 posts
- Explanation: https://www.minid.net/2026/7/14/how-to-automatise-with-ai
- Git source: https://github.com/meerita/monorepo-nextjs-golang-rust-pytho...
- 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 a…
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?
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-
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.
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.
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- 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.
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 reac…
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.
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.
Earlier quoted context omitted.
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)
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?