mindforge-debug

A debugging process that keeps investigation notes across context resets, which are points where an AI session starts with less of its earlier conversation available. It gathers symptoms, forms hypotheses, and can delegate investigation to a separate debugging agent.

In plain words
What is it for?
Use it to investigate a new or ongoing software problem, record expected and actual behavior, track hypotheses, and continue from an existing debugging session.
Why use it?
It helps preserve what has already been learned during a long debugging session. This reduces repeated file reading and keeps the main conversation focused on decisions with you.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/sairam0424/mindforge/mindforge-debug
Any agent
npx skills add sairam0424/MindForge --skill mindforge-debug
Clone the repo
git clone --depth 1 https://github.com/sairam0424/MindForge

Made for: Claude Code, Codex.

Per session 13 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 948 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 86% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00013 $0.00948
Opus 5 $0.00006 $0.00474
Sonnet 5 $0.00003 $0.00190
Haiku 4.5 $0.00001 $0.00095

Measured yesterday against content hash 962a0600a736, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mindforge-debug scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

86% identical to thrunt-debug — 27 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agent/skills/mindforge-debug/SKILL.md · 164 lines

How it starts

The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Check for active sessions:

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

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}"
)

Read the full file on GitHub · 164 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 164 lines · 13 tokens per session scan A 962a0600a736

Subscribe to this mod's changes

mindforge-debug is a skill published in the GitHub repository sairam0424/MindForge (0 stars, last pushed 2d ago), licensed MIT. It adds 13 tokens to every session and 948 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to thrunt-debug, differing in 27 lines, and is treated as a copy.