agentleFS
nassaj2011nassaj2011/claude-skills-for-chatgpt/skills/backend-debugging/SKILL.md

backend-debugging

Skill0 starsChanged 23 days agoMITView on GitHub

Investigate backend defects using reproducible evidence, traces, hypotheses, binary isolation and regression tests. Use for crashes, incorrect business logic, intermittent failures, or API incidents.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Verify rollback and monitor relapse signals.

    line 13
  2. Distinctive:

    Do not claim the bug is fixed solely because a patch compiled.

    line 16

≈ “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

Evidence-driven backend debugging

  1. Record the exact symptom, environment, versions, affected endpoints and last known good state; separate user report from verified behavior.
  2. Find an independently reproducible minimal case using safe test data. Capture structured logs, correlation IDs, stack traces and relevant request IDs; redact secrets.
  3. Draft competing hypotheses ordered by evidence and blast radius. Inspect recent diffs, transaction boundaries, config drift, queue timing and dependency failures.
  4. Instrument narrowly; never replace evidence with guesses or massive logging of production personal data.
  5. Isolate a root cause by a controlled test, diff, breakpoint, replay or focused probe. Distinguish root cause from triggering symptom.
  6. Implement the smallest corrective patch, add a regression test that failed before and passes after, and inspect adjacent failure paths.
  7. Verify rollback and monitor relapse signals. When evidence is missing, report the most efficient next experiment.

Output: symptom, reproduction, evidence, ruled-out hypotheses, cause confidence, patch, before/after test result and unresolved risks. Do not claim the bug is fixed solely because a patch compiled.

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.