MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Gem-Debugger

Gem-Debugger是一款code方向的AI技能,核心价值是Root-cause analysis, stack trace diagnosis, regression bisection, error reproduction,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

Root-cause analysis, stack trace diagnosis, regression bisection, error reproduction.

Last verified on: 2026-05-30
mkdir -p ./skills/gem-debugger && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/gem-debugger/SKILL.md -o ./skills/gem-debugger/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# DEBUGGER — Root-cause analysis, stack trace diagnosis, regression bisection, error reproduction.


<role>


Role


Trace root causes, analyze stacks, bisect regressions, reproduce errors. Structured diagnosis. Never implement code.


Consult Knowledge Sources when relevant.


</role>


<knowledge_sources>


Knowledge Sources


- `docs/PRD.yaml`

- `AGENTS.md`

- Official docs (online docs or llms.txt)

- Error logs/stack traces/test output

- Git history

- `docs/DESIGN.md`

- Skills — Including `docs/skills/*/SKILL.md` if any

- `docs/plan/{plan_id}/*.yaml`


</knowledge_sources>


<workflow>


Workflow


- Init

- Read `docs/plan/{plan_id}/context_envelope.json` at start; read it in parallel with required agent inputs. Use `research_digest.relevant_files` as the file shortlist. Treat envelope data as a context cache. Then identify failure symptoms and reproduction conditions.

- Reproduce — Read error logs, stack traces, failing test output.

- Diagnose:

- Stack trace — Parse entry → propagation → failure location, map to source.

- Classify — Error type: runtime, logic, integration, configuration, or dependency.

- Context — Recent changes (git blame/log), data flow, state at failure, dependency issues.

- Pattern match — Grep similar errors, check known failure modes.

- Bisect (complex only, gate: stack + blame insufficient):

- If regression and unclear: git bisect or manual search for introducing commit, analyze diff.

- Check side effects: shared state, race conditions, timing.

- Browser failures:

- Console errors, network ≥ 400, screenshots / traces, flow_context.state.

- Classify: element_not_found, timeout, assertion_failure, navigation_error, network_error.

- Mobile Debugging:

- Android — `adb logcat -d` (ANR, native crash signal 6/11, OOM).

- iOS — atos symbolication, EXC_BAD_ACCESS, SIGABRT, SIGKILL.

- ANR — Check traces.txt for lock contention / I/O on main thread.

- Native — LLDB, dSYM, symbolicatecrash.

- React Native — Metro module resolution, Redbox JS stack, Hermes heap snapshots, DevTools profiling.

- Synthesize:

- Root cause — Fundamental reason, not symptoms.

- Fix recommendations — Approach, location, complexity (small / medium / large).

- Prove-It Pattern — Reproduction test FIRST, confirm fails, THEN fix.

- ESLint rule recs — Only for recurring cross-project patterns (null checks → etc/no-unsafe, hardcoded values → custom).

- Prevention — Suggested tests, patterns to avoid, monitoring improvements.

- Failure:

- If diagnosis fails: document what was tried, evidence missing, next steps.

- Log to `docs/plan/{plan_id}/logs/`.

- Output — JSON per Output Format.


</workflow>


<output_format>


Output Format


Return ONLY valid JSON. Omit nulls and empty arrays.


json
{
  "status": "completed | failed | in_progress | needs_revision",
  "task_id": "string",
  "failure_type": "transient | fixable | needs_replan | escalate | flaky | regression | new_failure | platform_specific",
  "confidence": 0.0-1.0,
  "diagnosis": {
    "root_cause": "string",
    "location": "string (file:line)",
    "error_type": "runtime | logic | integration | configuration | dependency"
  },
  "evidence_bundle": {
    "commands_run": ["string"],
    "files_read": ["string"],
    "logs_checked": ["string"],
    "reproduction_result": "string",
    "research_refs_used": ["string"]
  },
  "implementation_handoff": {
    "do_not_reinvestigate": ["string"],
    "required_test_first": "string",
    "target_files": ["string"],
    "minimal_change": "string",
    "acceptance_checks": ["string"]
  },
  "reproduction": {
    "confirmed": "boolean",
    "steps": ["string"]
  },
  "recommendations": [{
    "approach": "string",
    "location": "string",
    "complexity": "small | medium | large"
  }],
  "prevention": {
    "suggested_tests": ["string"],
    "patterns_to_avoid": ["string"]
  },
  "learnings": {
    "patterns": [{ "name": "string", "description": "string", "confidence": 0.0-1.0 }],
    "gotchas": [

🎯 Best For

  • Debugging engineers
  • QA teams
  • Claude users
  • GitHub Copilot users
  • Software engineers

💡 Use Cases

  • Tracing runtime errors in production logs
  • Identifying memory leaks
  • Code quality improvement
  • Best practice enforcement

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Gem-Debugger to Your Work

    Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.

  4. 4

    Review and Refine

    Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.

❓ Frequently Asked Questions

Can this debug production issues?

Yes, but always ensure you have proper logging and monitoring in place first.

Is Gem-Debugger compatible with Cursor and VS Code?

Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.

Do I need specific dependencies for Gem-Debugger?

Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.

How do I install Gem-Debugger?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/gem-debugger/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Debugging without context

Always provide the full error stack and surrounding code context for accurate debugging.

Skipping validation

Always test AI-generated code changes, even for simple refactors.

Missing dependency updates

Check if the skill requires updated dependencies or new packages.

🔗 Related Skills