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hamgarianhamgarian/loot-skill/skills/skill-factory/SKILL.md

skill-factory

Skill2 starsChanged 27 days agoMITView on GitHub

Turn a skill idea into a finished, tested, reviewed and released agent skill in one run - spec, tests first, an independent review against a release checklist, and a release that waits for the user's yes. Use for "skill factory", "/skill-factory", "build and ship a skill for X", "make this a skill and release it", or when loot hands over a drop whose form is a skill.

13 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

6 rules. 0 are common, 3 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 12 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Write SPEC.md: goal, inputs and outputs, and an acceptance checklist where every line can be tested.

    line 17
  2. Very specific:

    If the agent has a skill-authoring helper (in Claude Code: anthropic-skills:skill-creator), use it for the skill and its evals.

    line 23
  3. Distinctive:

    Run every test and eval. Keep the exact command and its output for step 4.

    line 30
  4. Very specific:

    Best: start a subagent that has not seen this conversation.

    line 34
  5. Distinctive:

    Fallback: no subagents: for each checklist item, write the strongest case that it FAILS before deciding.

    line 39
  6. Distinctive:

    test and eval results (command + pass count), the review verdict

    line 52

“Common” is measured: kept by at least 6 in 10 of the 12 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

skill-factory

Builds a skill the way a careful maintainer would, then holds the release until the user approves it. Works in any agent; each step names the best tool and a fallback that always works.

Input: a skill idea, or a drop handed over by loot with its evidence (query, search results, proof, edge). Target folder: where the user says, else ../<slug> outside the current repo's history.

1. Spec

Write SPEC.md: goal, inputs and outputs, and an acceptance checklist where every line can be tested. Under a page. If loot handed the item over, reuse its evidence instead of searching again. Ask the user only when two readings of the idea would produce different skills.

2. Build, tests first

  • If the agent has a skill-authoring helper (in Claude Code: anthropic-skills:skill-creator), use it for the skill and its evals.
  • Otherwise write SKILL.md yourself: frontmatter with name (kebab-case) and a description that says what it does and when to trigger, then the steps.
  • Scripts get tests before code (node:test or pytest). Implement until they pass. Write files with your file-editing tool, not shell heredocs.
  • Run every test and eval. Keep the exact command and its output for step 4.

3. Independent review

The builder doesn't grade its own work.

  • Best: start a subagent that has not seen this conversation. Give it only the target folder and references/release-checklist.md with this brief: "Review the skill in <folder> against the checklist. Answer PASS or FAIL per item with file:line evidence. When unsure, FAIL. Flag any claim the tests or evals don't prove."
  • Fallback: no subagents: for each checklist item, write the strongest case that it FAILS before deciding.

Fix every FAIL, rerun tests, review again. Stop when all items pass, or when an item is blocked on the user (say which and why).

4. Release gate

Scan for secrets: node <loot skill folder>/scripts/scan.mjs <folder>, plus gitleaks dir <folder> if installed. Then show the user a manifest and stop for a clear yes:

  • repo name, public or private
  • version and changelog line
  • the files to publish
  • test and eval results (command + pass count), the review verdict
  • anything skipped

A yes covers exactly that manifest. Then publish from a fresh git init: commit release vX.Y.Z, tag, gh repo create and push, and install the skill where the user's agent loads skills. No gh or not signed in: print the commands. Never ask for, type or store credentials.

5. Report

One short message in plain language: links, test results, review result, and what's left for the user.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

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  • It worked
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  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

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