story-refinery
Skill1 starsChanged 2 months agoNo licenceView on GitHub
Refine a backlog item into an implementation-ready package: multi-repo code evidence with file:line citations, locked design decisions, testable acceptance criteria with stable codes, a decision table whose completeness is checked, a condensed human description, a machine-readable agent brief, and subtasks decomposed per a configurable house profile. Reads the ticket's own labels first - a production finding refines differently from a feature - keeps the investigation for whoever implements it, and puts the result past blind critics before handover. Tracker-agnostic: Jira, GitHub, GitLab, Linear, Azure DevOps, or plain markdown. Use when a backlog item, ticket, epic or bug must be made ready before anyone implements it, or when a calling skill passes an item with --wishes. It refines; it never writes the implementation.
In this repository9 other files it gives its agents.
- book-writerSkill
- fan-outSkill
- gaiaSkill
- game-engine-guruSkill
- gauntlet-loopSkill
- mission-controlSkill
- physically-based-renderingSkill
- principal-architectSkill
- reasoning-matrixSkill
The file
Show its rules and text
We split this skill into the rules it gives an agent
26 rules. About 0 are common, 8 are distinctive, and 18 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
18
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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.

