agentsclimarketplace

Create skill

Skill Bennyoooo/skillmaxxing/skill-maxing-plugin/skills/create-skill

Create a new reusable agent skill — either explicitly ("turn this workflow into a skill") or by reflecting on a just-completed task that you might do again. Synthesizes SKILL.md, scripts, and a real eval scaffold, then stages it for review before committing. Use when the user asks to make/save/capture a skill, or after finishing a non-trivial workflow worth reusing.From its SKILL.md

Install
npx -y skills add Bennyoooo/skillmaxxing --skill create-skill

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 21 stars21 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 4 commands, including `scripts/discover.sh "<capability>" --json` and 3 more.

SKILL.md

3.3 KB, 705 tokens by cl100k_base, as published. Nobody here has run it

create-skill

Crystallize a workflow into a durable, tested skill. The CLI is model-agnostic: you synthesize the content (name, body, scripts, real eval tasks); the CLI stages, smoke-tests, and commits it atomically.

Two entry points

  1. Explicit — the user says "turn X into a skill."
  2. Reflection — after completing a non-trivial task, consider whether it is reusable. Fire this sparingly (review fatigue is real): only when the work was non-trivial (several steps, a fixed bug, a discovered workflow) AND plausibly recurs. When in doubt, don't interrupt.

Step 0 — Prefer update over create

Before creating, search for an existing skill that already covers this:

scripts/discover.sh "<capability>" --json

If a close match exists, update or optimize it instead of making a near-duplicate. The skillify step also runs this check and will refuse with a suggestion unless you pass --new.

Step 1 — Synthesize a draft

Write a draft JSON file. The eval scaffold MUST contain real, scorable tasks (not a stub) — pick a scorer per task: exact/normalized/code-exec/success-signal for deterministic outputs, agent-judge (with a rubric) for prose/judgment skills.

{
  "name": "release-notes",
  "description": "Draft release notes from a git log range.",
  "body": "# release-notes\n\n...instructions...\n",
  "tools": ["Bash"],
  "scripts": [{ "path": "scripts/changelog.sh", "content": "#!/usr/bin/env bash\n..." }],
  "eval": {
    "skill": "release-notes",
    "tasks": [
      { "id": "happy", "input": "v1.0..v1.1", "scorer": "agent-judge", "rubric": "Groups changes by type; no raw SHAs; user-facing tone." }
    ]
  },
  "smokeTest": ["bash", "scripts/changelog.sh", "--help"]
}

Step 2 — Stage (with the human approval gate)

scripts/skillify.sh --draft draft.json            # stage; smoke test skipped unless authorized
scripts/skillify.sh --draft draft.json --allow-exec   # stage AND run the smoke test in the sandbox

Only pass --allow-exec after the user has reviewed the generated scripts — a freshly synthesized skill is trusted: false, and running its code is a deliberate, user-authorized step. Show the staged SKILL.md and scripts to the user and get explicit approval before committing.

Step 3 — Commit

scripts/skillify.sh --commit <name> [--global] [--agent <name>]

This installs the skill (trusted: false) and clears the draft. Staged drafts persist across sessions — resume with --list-drafts then --commit.

Safety

  • The smoke-test/exec gate is yours to honor: never pass --allow-exec without fresh user authorization for the specific scripts.
  • Do not commit a skill whose smoke test failed; fix the draft and re-stage.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.