agentleFS
Sign inStart free
sscodeaisscodeai/agency-agents-ja/.claude/skills/debug-issue/skill.md

Debug Issue

Skill10 starsChanged 6 months agoMITView on GitHub

Systematically debug issues using graph-powered code navigation

4 other projects use a version of this, 2 of them edited.

Adopted by

Most recently taken up by

  • Other versions4 projects use a version of this.

    Word for word · 2

    1. nilsteampassnet/TeamPass★ 1.9k100% same
    2. dorian-akhs/skills★ 0100% same

    Edited · 2

    1. microsoft/Microsoft365DSC★ 2.5k100% same
    2. theexperiencecompany/gaia★ 325100% same

    Wrote their own version · 0

    None found.

    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 repository4 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 3 are distinctive, and 7 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 7

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use the knowledge graph to systematically trace and debug issues.

    line 8
  2. Very specific:

    Use semantic_search_nodes to find code related to the issue.

    line 12
  3. Very specific:

    Use query_graph with callers_of and callees_of to trace call chains.

    line 13
  4. Very specific:

    Use get_flow to see full execution paths through suspected areas.

    line 14
  5. Very specific:

    Run detect_changes to check if recent changes caused the issue.

    line 15
  6. Very specific:

    Use get_impact_radius on suspected files to see what else is affected.

    line 16
  7. Distinctive:

    Check both callers and callees to understand the full context.

    line 20
  8. Distinctive:

    Look at affected flows to find the entry point that triggers the bug.

    line 21
  9. Very specific:

    ALWAYS start with get_minimal_context(task="<your task>") before any other graph tool.

    line 25
  10. Very specific:

    Use detail_level="minimal" on all calls.

    line 26

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
skill.md

Debug Issue

Use the knowledge graph to systematically trace and debug issues.

Steps

  1. Use semantic_search_nodes to find code related to the issue.
  2. Use query_graph with callers_of and callees_of to trace call chains.
  3. Use get_flow to see full execution paths through suspected areas.
  4. Run detect_changes to check if recent changes caused the issue.
  5. Use get_impact_radius on suspected files to see what else is affected.

Tips

  • Check both callers and callees to understand the full context.
  • Look at affected flows to find the entry point that triggers the bug.
  • Recent changes are the most common source of new issues.

Token Efficiency Rules

  • ALWAYS start with get_minimal_context(task="<your task>") before any other graph tool.
  • Use detail_level="minimal" on all calls. Only escalate to "standard" when minimal is insufficient.
  • Target: complete any review/debug/refactor task in ≤5 tool calls and ≤800 total output tokens.

Download now (ZIP)

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.

Check your setup, free