Gsd Debug

---
name: gsd-debug
description: "Systematic debugging with persistent state across context resets"
---

<cursor_skill_adapter>
## A. Skill Invocation
- This skill is invoked when the user mentions `gsd-debug` or describes a task matching this skill.
- Treat all user text after the skill mention as `{{GSD_ARGS}}`.
- If no arguments are present, treat `{{GSD_ARGS}}` as empty.

## B. User Prompting
When the workflow needs user input, prompt the user conversationally:
- Present options as a numbered list in your response text
- Ask the user to reply with their choice
- For multi-select, ask for comma-separated numbers

## C. Tool Usage
Use these Cursor tools when executing GSD workflows:
- `Shell` for running commands (terminal operations)
- `StrReplace` for editing existing files
- `Read`, `Write`, `Glob`, `Grep`, `Task`, `WebSearch`, `WebFetch`, `TodoWrite` as needed

## D. Subagent Spawning
When the workflow needs to spawn a subagent:
- Use `Task(subagent_type="generalPurpose", ...)`
- The `model` parameter maps to Cursor's model options (e.g., "fast")
</cursor_skill_adapter>

<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>

<available_agent_types>
Valid GSD subagent types (use exact names — do not fall back to 'general-purpose'):
- gsd-debugger — Diagnoses and fixes issues
</available_agent_types>

<context>
User's issue: {{GSD_ARGS}}

Check for active sessions:
```bash
ls .planning/debug/*.md 2>/dev/null | grep -v resolved | head -5
```
</context>

<process>

## 0. Initialize Context

```bash
INIT=$(node "D:/code/Project/tablez-demo2-allByAi/.cursor/get-shit-done/bin/gsd-tools.cjs" state load)
if [[ "$INIT" == @file:* ]]; then INIT=$(cat "${INIT#@file:}"); fi
```

Extract `commit_docs` from init JSON. Resolve debugger model:
```bash
debugger_model=$(node "D:/code/Project/tablez-demo2-allByAi/.cursor/get-shit-done/bin/gsd-tools.cjs" resolve-model gsd-debugger --raw)
```

## 1. Check Active Sessions

If active sessions exist AND no {{GSD_ARGS}}:
- List sessions with status, hypothesis, next action
- User picks number to resume OR describes new issue

If {{GSD_ARGS}} provided OR user describes new issue:
- Continue to symptom gathering

## 2. Gather Symptoms (if new issue)

Use conversational prompting 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:

```markdown
<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",
  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 /gsd-plan-phase --gaps
  - "Manual fix" - done

**If `## CHECKPOINT REACHED`:**
- Present checkpoint details to user
- Get user response
- If checkpoint type is `human-verify`:
  - If user confirms fixed: continue so agent can finalize/resolve/archive
  - If user reports issues: continue so agent returns to investigation/fixing
- 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:

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

<prior_state>
<files_to_read>
- .planning/debug/{slug}.md (Debug session state)
</files_to_read>
</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",
  model="{debugger_model}",
  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>