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/leei1337/phantom-neural-cortex/debuggit clone --depth 1 https://github.com/LEEI1337/phantom-neural-cortexWhat 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 | $0.00006 | $0.00152 |
| Opus 5 | $0.00003 | $0.00076 |
| Sonnet 5 | $0.00001 | $0.00030 |
| Haiku 4.5 | $0.00001 | $0.00015 |
Grade A, and why
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.
What it actually says
Debug Task
Please help debug the issue with this systematic approach:
1. Problem Analysis
- Describe the observed behavior
- Identify expected behavior
- Review error messages and stack traces
- Identify affected components
2. Root Cause Investigation
- Analyze code logic
- Check data flow
- Review recent changes
- Identify potential causes
3. Hypothesis Testing
- Propose possible solutions
- Explain reasoning
- Consider side effects
- Prioritize by likelihood
4. Solution Implementation
- Provide fix with explanation
- Add error handling
- Include preventive measures
- Suggest tests to prevent regression
Please provide step-by-step analysis and recommendations.
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 · 34 lines · 6 tokens per session scan A 5d9e5739958b
debug is a command published in the GitHub repository LEEI1337/phantom-neural-cortex (5 stars, last pushed 5mo ago), licensed MIT. It adds 6 tokens to every session and 152 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
cleanup-back-to-main
Go back to main and clean up the branch.
spec-design
Create comprehensive technical design for a specification.
notebook-query
Query the notebook knowledge base (SQLite) built by /agy:notebook — precise, grounded, cited. Ask in natural language ("sum the amounts by category", "which docs mention 'Acme Corp'", "build a project timeline") or pass raw SQL. Read-only. Use this when you need exact aggregates/lookups across a document corpus…
notebook-status
Check the progress of a /agy:notebook sweep (for long document sets run with --background). Reports % complete, done/pending/failed counts, elapsed time and a rough ETA, and which documents are still pending — so you can resume. Read-only, no agy.
status
Show the current status of the Claude Code Router server.
tldr
Re-apply TLDR rules for this turn (verdict first, no filler).