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.
npx agentmods add commands/geoloeg-ist/agents-reverse-engineer/debuggit clone --depth 1 https://github.com/GeoloeG-IsT/agents-reverse-engineerWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00014 | $0.00961 |
| Opus 5 | $0.00007 | $0.00481 |
| Sonnet 5 | $0.00003 | $0.00192 |
| Haiku 4.5 | $0.00001 | $0.00096 |
Grade A, and why
gsd:debug scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrator role: Gather symptoms, spawn gsd-debugger agent, handle checkpoints, spawn continuations.
Why subagent: Investigation burns context fast (reading files, forming hypotheses, testing). Fresh 200k context per investigation. Main context stays lean for user interaction.
Check for active sessions:
ls .planning/debug/*.md 2>/dev/null | grep -v resolved | head -5
0. Resolve Model Profile
Read model profile for agent spawning:
MODEL_PROFILE=$(cat .planning/config.json 2>/dev/null | grep -o '"model_profile"[[:space:]]*:[[:space:]]*"[^"]*"' | grep -o '"[^"]*"$' | tr -d '"' || echo "balanced")
Default to "balanced" if not set.
Model lookup table:
| Agent | quality | balanced | budget |
|---|---|---|---|
| gsd-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:
- Expected behavior - What should happen?
- Actual behavior - What happens instead?
- Error messages - Any errors? (paste or describe)
- Timeline - When did this start? Ever worked?
- Reproduction - How do you trigger it?
After all gathered, confirm ready to investigate.
3. Spawn gsd-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>
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.
- 2d ago First seen · 170 lines · 14 tokens per session scan A f519b4889173
gsd:debug is a command published in the GitHub repository GeoloeG-IsT/agents-reverse-engineer (20 stars, last pushed 25d ago), licensed MIT. It adds 14 tokens to every session and 961 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
auto-reason
Subjective self-refinement with blind judging, Borda aggregation, and provider-agnostic model routing.
deep-research
Deep research — produce a cited decision-grade research report and artifact bundle.
goals
Goal-oriented mission entrypoint — set, view, or drive long-running objectives through the mission system.
launch-worker
Manually launch headless workers against one or more GitHub issues.
check-routines
Check and safely repair aidevops routine scheduler health.
local-permissions-check
Audit local host/runtime permissions for aidevops on macOS, Linux, Windows, and WSL.