Df:debug

Orchestrator role: Gather symptoms, spawn df-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.

Check for active sessions:

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

0. Initialize Context

INIT=$(node ~/.claude/devflow/bin/df-tools.cjs state load)

Extract commit_docs from init JSON. Resolve debugger model:

DEBUGGER_MODEL=$(node ~/.claude/devflow/bin/df-tools.cjs resolve-model df-debugger --raw)

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)

Step 2a: What happens? (structured)

Use AskUserQuestion:

• header: "Symptom"
• question: "What happens when the issue occurs?"
• multiSelect: false
• options:
  • "Nothing happens" — Expected action produces no result
  • "Wrong output" — Something happens but it's incorrect
  • "Error displayed" — An error message or crash occurs
  • "Partial success" — Some parts work, others don't

Step 2b: Impact (structured)

Use AskUserQuestion:

• header: "Impact"
• question: "How does this affect your workflow?"
• multiSelect: false
• options:
  • "Completely blocked" — Cannot continue without fixing this
  • "Workaround exists" — Can work around it but it's painful
  • "Cosmetic issue" — Functionality works, appearance is wrong
  • "Intermittent" — Sometimes works, sometimes doesn't

Step 2c: Expected behavior (freeform)

Ask inline: "What should happen instead? Describe the expected behavior."

Step 2d: Error details (freeform)

Ask inline: "Any error messages? Paste them or describe what you see."

Step 2e: Reproduction (freeform)

Ask inline: "How do you trigger this? What steps reproduce the issue?"

After all gathered, confirm ready to investigate.

3. Spawn df-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="df-debugger",
  model="{debugger_model}",
  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 /df:plan-objective --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="df-debugger",
  model="{debugger_model}",
  description="Continue debug {slug}"
)