issues-creator
Skill0 starsChanged 52 days agoNo licenceView on GitHub
Deep, multi-agent codebase audit that hunts for bugs, security issues, feature gaps, UI/UX problems, and stale documentation, then produces a prioritized, deduplicated DRAFT issue list for the user to review before anything is filed on GitHub. Use this whenever the user wants a thorough/deep review or audit of a codebase, asks to "find bugs", "find issues", "review this codebase with a fine tooth comb", "what's missing from this project", "find security problems", "audit the UI", "check for stale docs", or wants a comprehensive pass before a release -- even if they don't name the skill directly. This is a heavy, multi-agent operation (it runs a Workflow that fans out several reviewer subagents plus a verification pass) -- not for a quick one-off bug check on a single file, which should just be handled directly. Always presents a draft for approval; never files a GitHub issue without the user explicitly signing off on that specific batch.
In this repository3 other files it gives its agents.
- autopilotSkill
- explain-diffSkill
- github-cleanupSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
2
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.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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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.