kata-debug

Debug issues using scientific method with subagent isolation.

Orchestrator role: Gather symptoms, spawn kata-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.

User's issue: $ARGUMENTS

Check for active sessions:

find .planning/debug -maxdepth 1 -name "*.md" 2>/dev/null | grep -v resolved | head -5

0. Resolve Model Profile

Read model profile for agent spawning:

MODEL_PROFILE=$(node scripts/kata-lib.cjs read-config "model_profile" "balanced")

Default to "balanced" if not set.

Model lookup table:

Agent	quality	balanced	budget
kata-debugger	opus	sonnet	sonnet

Store resolved model for use in Task calls below.

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. Read Instruction Files

Before spawning agents, read agent instructions using the Read tool:

• references/debugger-instructions.md (relative to skill base directory) — store as debugger_instructions_content

4. Spawn 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="<agent-instructions>\n{debugger_instructions_content}\n</agent-instructions>\n\n" + filled_prompt,
  subagent_type="general-purpose",
  model="{debugger_model}",
  description="Debug {slug}"
)

5. Handle Agent Return

If ## ROOT CAUSE FOUND:

• Display root cause and evidence summary
• Offer options:
  • "Fix now" - spawn fix subagent
  • "Plan fix" - suggest /kata-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

6. 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="<agent-instructions>\n{debugger_instructions_content}\n</agent-instructions>\n\n" + continuation_prompt,
  subagent_type="general-purpose",
  model="{debugger_model}",
  description="Continue debug {slug}"
)

<success_criteria>

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