agentleFS
alexanderpinoalexanderpino/skills/story-refinery/SKILL.md

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.

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
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.