Débogage structuré avec persistance

VérifiéSûr

Workflow de débogage scientifique avec isolation des sous-agents et suivi des investigations. Gère les points de contrôle et les continuations pour conserver le contexte.

Spar Skills Guide Bot
DeveloppementIntermédiaire
2002/06/2026
Claude Code
#debugging#scientific-method#workflow#subagent

Recommandé pour

Notre avis

Fournit un flux de travail de débogage structuré utilisant la méthode scientifique, avec persistance de session et isolation des sous-agents pour gérer la consommation de contexte.

Points forts

  • Analyse systématique des causes racines via la méthode scientifique.
  • Isolation des sous-agents pour préserver le contexte principal de l'utilisateur.
  • Gestion des points de contrôle et continuation des investigations longues.
  • Traçabilité complète des hypothèses et des tests.

Limites

  • Nécessite l'infrastructure de sous-agents (spécifique à Claude Code).
  • Surcharge liée au lancement multiple d'agents.
  • Non adapté aux problèmes triviaux ou rapides à résoudre.
Quand l'utiliser

Utilisez-le pour déboguer des problèmes complexes nécessitant une investigation systématique et pouvant consommer beaucoup de contexte.

Quand l'éviter

Évitez-le pour des bugs simples qui peuvent être corrigés directement sans analyse approfondie.

Analyse de sécurité

Sûr
Score qualité90/100

The skill defines a debugging workflow using non-destructive Bash commands (ls for file listing), spawning subagents for investigation, and gathering user symptoms. No harmful operations like network exfiltration, destructive filesystem commands, or disabling safety are instructed.

Aucun point d'attention détecté

Exemples

Debug a failing API endpoint
gsd-debug My API endpoint /users returns 500 error intermittently
Investigate memory leak in Node.js app
gsd-debug Memory increases steadily over time in production, never drops
Resolve build failure with cryptic error
gsd-debug Webpack build fails with 'Module not found' error only on CI

name: gsd-debug description: Structured debugging workflow with session persistence and investigation tracking allowed-tools: Task, Read, Edit, Bash argument-hint: [issue]

<objective> Debug issues using scientific method with subagent isolation.

Orchestrator role: Gather symptoms, spawn gsd-debugger agent, handle checkpoints, spawn continuations.

Why subagent: Investigation burns context fast (reading files, forming hypotheses, testing). Fresh 200k context per investigation. Main context stays lean for user interaction. </objective>

<context> User's issue: $ARGUMENTS

Check for active sessions:

ls .planning/debug/*.md 2>/dev/null | grep -v resolved | head -5
</context> <process>

1. Check Active Sessions

If active sessions exist AND no $ARGUMENTS:

  • List sessions with status, hypothesis, next action
  • User picks number to resume OR describes new issue

If $ARGUMENTS provided OR user describes new issue:

  • Continue to symptom gathering

2. Gather Symptoms (if new issue)

Use AskUserQuestion for each:

  1. Expected behavior - What should happen?
  2. Actual behavior - What happens instead?
  3. Error messages - Any errors? (paste or describe)
  4. Timeline - When did this start? Ever worked?
  5. Reproduction - How do you trigger it?

After all gathered, confirm ready to investigate.

3. Spawn gsd-debugger Agent

Fill prompt and spawn:

<objective>
Investigate issue: {slug}

**Summary:** {trigger}
</objective>

<symptoms>
expected: {expected}
actual: {actual}
errors: {errors}
reproduction: {reproduction}
timeline: {timeline}
</symptoms>

<mode>
symptoms_prefilled: true
goal: find_and_fix
</mode>

<debug_file>
Create: .planning/debug/{slug}.md
</debug_file>
Task(
  prompt=filled_prompt,
  subagent_type="gsd-debugger",
  description="Debug {slug}"
)

4. Handle Agent Return

If ## ROOT CAUSE FOUND:

  • Display root cause and evidence summary
  • Offer options:
    • "Fix now" - spawn fix subagent
    • "Plan fix" - suggest {{COMMAND_PREFIX}}plan-phase --gaps
    • "Manual fix" - done

If ## CHECKPOINT REACHED:

  • Present checkpoint details to user
  • Get user response
  • Spawn continuation agent (see step 5)

If ## INVESTIGATION INCONCLUSIVE:

  • Show what was checked and eliminated
  • Offer options:
    • "Continue investigating" - spawn new agent with additional context
    • "Manual investigation" - done
    • "Add more context" - gather more symptoms, spawn again

5. Spawn Continuation Agent (After Checkpoint)

When user responds to checkpoint, spawn fresh agent:

<objective>
Continue debugging {slug}. Evidence is in the debug file.
</objective>

<prior_state>
Debug file: @.planning/debug/{slug}.md
</prior_state>

<checkpoint_response>
**Type:** {checkpoint_type}
**Response:** {user_response}
</checkpoint_response>

<mode>
goal: find_and_fix
</mode>
Task(
  prompt=continuation_prompt,
  subagent_type="gsd-debugger",
  description="Continue debug {slug}"
)
</process>

<success_criteria>

  • [ ] Active sessions checked
  • [ ] Symptoms gathered (if new)
  • [ ] gsd-debugger spawned with context
  • [ ] Checkpoints handled correctly
  • [ ] Root cause confirmed before fixing </success_criteria>
Skills similaires