agentleFS
itsofficialadiitsofficialadi/boundiff/.agents/skills/bug-fix-verification/SKILL.md

bug-fix-verification

Skill0 starsChanged 23 days agoMITView on GitHub

Reproduce a reported bug, establish meaningful regression coverage when feasible, apply a minimal fix, and verify it. Use for debugging and regression work; not for general features or read-only audit.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    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:

    Regression: When practical, add or adapt a test that fails for the reported bug before the fix.

    line 17
  2. Distinctive:

    Verify: Run the reproduction or regression again, then relevant adjacent checks.

    line 19
  3. Distinctive:

    Inspect the diff: Check that tests meaningfully cover the defect and that no incidental files or abstractions were introduced.

    line 20
  4. Distinctive:

    If reproduction is impossible due to missing data or environment, do not fabricate a failure.

    line 25
  5. Distinctive:

    If a new test is artificial or disproportionately costly, explain why and use the most credible available verification instead.

    line 26
  6. Distinctive:

    Do not silently fix a separate issue discovered while debugging.

    line 28

≈ “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

Bug-fix verification

Objective

Convert a bug report into a small, demonstrably improved behavior without mistaking green tests for universal correctness.

Steps

  1. Frame. Record observed symptom, expected behavior, relevant inputs, and scope. Distinguish user report from directly reproduced evidence.
  2. Inspect. Locate responsible code, relevant configuration, and nearby tests. Look for alternative explanations before editing.
  3. Reproduce. Attempt the smallest repeatable reproduction in a safe environment. Capture exact failing command, assertion, or observable result.
  4. Regression. When practical, add or adapt a test that fails for the reported bug before the fix. Avoid contrived tests that only encode the implementation.
  5. Fix narrowly. Change the minimum necessary behavior. Preserve public contracts, validation, security checks, and unrelated behavior.
  6. Verify. Run the reproduction or regression again, then relevant adjacent checks. Record outcomes and whether the pre-fix failure was confirmed.
  7. Inspect the diff. Check that tests meaningfully cover the defect and that no incidental files or abstractions were introduced.
  8. Report. State reproduction status, evidence before and after, remaining coverage gaps, and any unreproduced conditions.

Decision rules

  • If reproduction is impossible due to missing data or environment, do not fabricate a failure. Document the blocker, use a reasoned alternative check if available, and qualify the result.
  • If a new test is artificial or disproportionately costly, explain why and use the most credible available verification instead.
  • For destructive, production, credential, or security-sensitive reproduction, stop or use a disposable/authorized environment.
  • Do not silently fix a separate issue discovered while debugging.

Output

Provide symptom, reproduction, fix, tests/checks, and residual uncertainty. Distinguish not reproduced, failed before/passed after, and merely passed after.

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.