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Mindforge debug

Skill sairam0424/MindForge/plugins/mindforge/skills/mindforge-debug

MindForge: The Enterprise Agentic Framework for Claude Code & Antigravity. High-performance autonomous execution, wave-parallelism, and multi-tier governance for production-grade AI engineering.

Install
npx -y skills add sairam0424/MindForge --skill mindforge-debug

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Systematic debugging with persistent state across context resets

SKILL.md

3.7 KB, 929 tokens by cl100k_base, as published. Nobody here has run it

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

Orchestrator role: Gather symptoms, spawn mindforge-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>

0. Initialize Context

INIT=$(node ".agent/bin/mindforge-tools.cjs" state load)
if [[ "$INIT" == @file:* ]]; then INIT=$(cat "${INIT#@file:}"); fi

Extract commit_docs from init JSON. Resolve debugger model:

debugger_model=$(node ".agent/bin/mindforge-tools.cjs" resolve-model mindforge-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)

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 mindforge-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="mindforge-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 /mindforge-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:

<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="mindforge-debugger",
  model="{debugger_model}",
  description="Continue debug {slug}"
)
</process>

<success_criteria>

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

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most debug triage skills give in 929 tokens

Counted across 839 of the 1,149 authors here whose files we hold, read 2026-08-07

  • Investigate root cause before proposing any fixin 102 of 839, across 67 files
  • Read error messages completelyin 89 of 839, across 49 files
  • Create a failing test case before fixingin 84 of 839, across 46 files
  • Reproduce the issue consistentlyin 82 of 839, across 41 files
  • Change one variable at a timein 82 of 839, across 42 files
  • Check recent changesin 74 of 839, across 36 files
  • Write the regression test before fixingin 74 of 839, across 40 files
  • Fix the root cause not the symptomin 60 of 839, across 45 files
  • Implement a single fix at a timein 59 of 839, across 20 files
  • Trace data flow backward to the sourcein 50 of 839, across 20 files
  • Remove all debug instrumentationin 49 of 839, across 13 files
  • Form a single hypothesisin 48 of 839, across 18 files

Said here and by no other author read

  • load tool state via helper script
  • resolve debugger model via helper script
  • gather symptoms using AskUserQuestion
  • spawn debugger agent with gathered context
  • display root cause summary when found
  • spawn continuation agent after checkpoint

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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