Agent skill

bugfix

End-to-end bugfix workflow — analyze environment (local Docker or cloud production), collect evidence, prepare bug report, plan fix, apply appropriate engineer role, implement and verify the fix.

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npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/bugfix-avav25-ai-assets-2

SKILL.md

Bugfix

End-to-end workflow for investigating, diagnosing, and fixing bugs. Orchestrates environment analysis (/analyze-local or /analyze-prod), evidence collection, bug report preparation, fix planning, and implementation by the appropriate engineering role.

1. Receive Bug Report

Gather the bug description from the user:

  • What is the expected behavior?
  • What is the actual behavior? (error message, wrong output, crash, performance issue)
  • Steps to reproduce (if known)
  • Environment: local (Docker) or production (cloud K8s)?
  • When did it start? (after deploy, config change, or unknown)
  • Severity: P1 (outage), P2 (degraded), P3 (minor), P4 (cosmetic)

If the user provides partial information, extract what you can and fill gaps during analysis. For P1/P2 — skip detailed intake and proceed immediately to Step 2.

2. Analyze Environment

Based on the environment, delegate to the appropriate analysis sub-workflow:

Environment Sub-workflow When to use
Local Docker / Docker Compose /analyze-local Bug observed in local dev environment
Cloud production (GKE/AKS/EKS, managed DB) /analyze-prod Bug observed in production or staging
Code-only (no infra) Skip to Step 3 Bug is in application logic, no environment analysis needed

Run the sub-workflow. It will:

  • Apply the appropriate diagnostic role (Agent(sre-engineer) or Agent(devops-engineer))
  • Collect environment snapshot (container/pod status, logs, metrics, networking)
  • Identify infrastructure-level issues
  • Present diagnosis

If the sub-workflow resolves the issue (e.g., container restart, config fix) — skip to Step 8.

If the root cause is in application code — continue to Step 3 with the collected evidence.

3. Detect Stack and Apply Engineer Role

Determine the affected service's tech stack and apply the appropriate engineering role:

  1. Read project's CLAUDE.md — tech stack declaration
  2. Scan project filespackage.json, pom.xml, requirements.txt, go.mod, etc.
  3. Check evidence from Step 2 — which service's logs show the error? What language is the stack trace in?

Role assignment:

Stack Signal Role
Next.js, React, TypeScript, .tsx files Agent(frontend-engineer)
Spring Boot, Java, .java files Agent(java-engineer)
FastAPI, Python, .py files Agent(python-engineer)
Terraform, Dockerfile, Helm, CI/CD Agent(devops-engineer)
ML model, training pipeline, inference Agent(ml-engineer)
Multiple stacks affected Apply all relevant roles
Unknown / general Agent(software-engineer)

Announce the applied role(s) to the user.

4. Collect Evidence

Gather all evidence into a structured collection. Combine data from Step 2 (environment analysis) with code-level investigation:

<evidence_checklist>

  • Error output: Full error message, stack trace, exit code
  • Logs: Relevant log entries (timestamped) from the affected service
  • Reproduction: Can you reproduce locally? Consistent or intermittent?
  • Scope: Which endpoints / features / components are affected?
  • Timeline: When did it start? What changed? (recent commits, deploys, config)
  • Data: Specific inputs that trigger the bug (request payload, user ID, dataset)
  • Metrics (if from prod): Error rate, latency change, affected user count
  • Related code: Files, functions, lines most likely involved </evidence_checklist>

Use the applied role's debugging methodology to trace the bug:

  • Read error stack traces to identify the failing code path
  • Search codebase for related functions, error handlers, and data flows
  • Check recent changes (git log -n 20 --oneline) for potential regressions
  • Identify the minimal reproduction case

5. Prepare Bug Report

Compile findings into a structured bug report. Present to the user for review:

## Bug Report

### Summary
[One sentence: what is broken]

### Severity: [P1 | P2 | P3 | P4]

### Environment
- **Type**: [local | production]
- **Service**: [name]
- **Stack**: [detected] | **Role**: [applied]

### Expected Behavior
[What should happen]

### Actual Behavior
[What actually happens — include error messages]

### Steps to Reproduce
1. [step]
2. [step]
3. [observed error]

### Evidence
- **Stack trace**: [snippet or reference]
- **Logs**: [relevant entries]
- **Metrics**: [if applicable]
- **Affected code**: [file:line references]

### Root Cause Analysis
[Technical explanation of why the bug occurs]

### Affected Components
- [file/module 1] — [how it's affected]
- [file/module 2] — [how it's affected]

Wait for user confirmation before proceeding to fix.

6. Plan the Fix

Create an ordered fix plan following the applied role's guidelines:

  1. Root cause fix — address the actual cause, not symptoms
  2. Minimal change — smallest change that fixes the bug without side effects
  3. Regression test — test that would have caught this bug
  4. Related fixes — any adjacent issues discovered during investigation

Present the plan:

## Fix Plan

### Root Cause
[What caused the bug — one sentence]

### Changes
1. [file] — [what to change and why]
2. [file] — [what to change and why]
...

### Tests
1. [test file] — [test that reproduces and verifies the fix]
2. [test file] — [regression test for edge cases]

### Risk Assessment
- **Blast radius**: [which features could be affected by this change]
- **Rollback**: [how to revert if the fix causes issues]

Wait for user approval before implementing.

7. Implement the Fix

Execute the approved plan step by step.

For each change:

  1. State what you are about to do (file, change summary)
  2. Implement the fix following the applied role's code quality standards
  3. Verify the code compiles/parses without errors

Then write tests:

  1. Regression test: Reproduces the original bug — must fail without fix, pass with fix
  2. Edge case tests: Cover related scenarios discovered during investigation
  3. Run the full test suite to catch regressions

Rules:

  • Fix the root cause, not the symptom
  • Minimal, focused changes — do not refactor unrelated code
  • Follow existing code patterns and conventions
  • No new warnings or linter errors
  • If the fix is more complex than expected — stop and discuss with the user

8. Verify the Fix

Verify the bug is resolved in the appropriate environment:

For local bugs:

  • Run the reproduction steps — confirm the bug no longer occurs
  • Run the full test suite — all tests pass (new + existing)
  • If Docker-based: rebuild and verify with /analyze-local (optional)

For production bugs:

  • Verify the fix locally first
  • Deploy through normal CI/CD pipeline (do NOT hotfix production directly)
  • After deploy: re-run relevant checks from /analyze-prod to confirm resolution
  • Monitor error rates and SLIs for regression

Verification checklist:

  • Original bug no longer reproduces
  • Regression test passes
  • Full test suite passes (no new failures)
  • No new warnings or linter errors
  • No unrelated files modified
  • Code follows project conventions and role guidelines

If any check fails — fix and re-verify.

9. Summary

Present the completed bugfix:

  • Bug: one-sentence summary
  • Severity: P1–P4
  • Environment: local / production
  • Role(s) applied: which roles were used
  • Root cause: technical explanation
  • Fix: what was changed (files, brief description)
  • Tests added: count and description
  • Verification: how it was confirmed fixed
  • Prevention: recommendations to avoid similar bugs (e.g., add validation, improve error handling, add monitoring)

Integration

  • Sub-workflows: /analyze-local, /analyze-prod (environment diagnostics)
  • Follow-up: /run-tests (verify fix), /pre-commit (quality gate), /create-pr (submit fix)
  • Skills: code-review skill (review fix), testing-procedures skill (test strategy)

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