skill-author
Skill114 starsChanged 5 months agoMITView on GitHub
Package a task the user repeats into a reusable SKILL.md skill folder, so it runs the same every time instead of depending on memory. Use when someone re-explains the same task to an AI assistant on a schedule. Meta-skill — works for any recurring task in any field.
23 other projects use a version of this.
Adopted by
Most recently taken up by
- himkt/cafleettheir own version, first seen Nov 8, 2026
- pankaj-mahaur/Agent-Daemontheir own version, first seen Nov 6, 2026
- educlopez/mallardtheir own version, first seen Nov 1, 2026
- mandasTeam/skill-authortheir own version, first seen Nov 1, 2026
- Lethe044/agentskill-kittheir own version, first seen Nov 1, 2026
- manusco/resonancetheir own version, first seen Oct 22, 2026
Other versions23 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 23
- generative-computing/mellea★ 1.9ksame name
- aqm857886159/Nomi★ 550same name
- robium-ai/robium★ 93same name
- himkt/cafleet★ 56same name
- Lethe044/agentskill-kit★ 48same name
- manusco/resonance★ 38same name
- Zhonghao1995/agentic-swmm-workflow★ 31same name
- felvieira/claude-skills-fv★ 27same name
- peter-tu-zynkr/zynkr-skill-builder★ 21same name
- ChilliCream/agent-skills★ 16same name
13 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository6 other files it gives its agents.
- connector-setupSkill
- context-brainSkill
- orchestratorSkill
- pipeline-builderSkill
- prompt-architectSkill
- work-engineSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.
0
Common
Most of the 18 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 51
Write the description carefully — it's what an agent reads to decide *when* to use the skill, so name the task and the trigger plainly.
- Distinctive: line 55
Run the new skill against a real brief.
“Common” is measured: kept by at least 6 in 10 of the 18 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.
Files
Skill Author
If you re-explain the same task every week, you're paying for it in time and in quality that swings with your memory. A skill packages the task once: when to run it, how the output gets made, what "done" looks like, and a couple of examples. This skill helps the user write one.
Role-agnostic: a recurring task is a recurring task, whether it's a weekly report, a candidate-screening summary, a design QA pass, or a release note.
Step 1 — Interview
Ask the user:
- What task do you repeat? Something they make on a cadence. If they name several, pick the highest-friction one.
- When should it run, and what does it need? The trigger and the inputs.
- How is it made? The steps, in order. Pull these out of them — they usually know but have never written it down.
- What does "done" look like? Sections, length, format, structure.
- Got examples? One or two of their best past versions to anchor quality.
Step 2 — Write the SKILL.md
Produce a standard Agent Skill folder: <skill-name>/SKILL.md with this shape:
---
name: <kebab-case-name>
description: <what it does + when to use it — this is how the agent decides to load it>
---
# <Title>
<one-line purpose>
## Trigger
<when to run · required inputs>
## Steps
1. <step>
2. <step>
...
## Output format
<what "done" is: sections, length, structure>
## Examples
<1–2 references, or links to example files>Write the description carefully — it's what an agent reads to decide when to use the skill, so name the task and the trigger plainly.
Step 3 — Dry-run
Run the new skill against a real brief. If the output drifts, the fix is usually a sharper step or a clearer "done" definition. Tighten, then save.
Step 4 — Hand-off
Tell the user where it lives (~/.claude/skills/<name>/ for Claude Code, or their agent's skills dir), and how to share it — a public GitHub repo is installable by others with npx skills add <owner>/<repo>.
Rules
- The
descriptionfield earns its keep — vague descriptions never get loaded. - Number the steps so output is identical whether the user is calm or rushed.
- Anchor on real examples, not adjectives.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
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.

