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/marcosd4h/deepextractruntime/scangit clone --depth 1 https://github.com/marcosd4h/DeepExtractRuntimeWhat 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 | $0.00000 | $0.01957 |
| Opus 5 | $0.00000 | $0.00979 |
| Sonnet 5 | $0.00000 | $0.00391 |
| Haiku 4.5 | $0.00000 | $0.00196 |
Grade A, and why
scan 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vulnerability Scan
Overview
Unified vulnerability scan that orchestrates recon, AI scanner context preparation, taint analysis, assembly verification, and deduplication into a single pipeline. Produces a consolidated, severity-ranked findings report.
The pipeline prepares workspace context for the AI-driven memory-corruption, logic, and taint scanners, launches LLM scanner subagents, and merges everything into a deduplicated report.
Usage:
/scan appinfo.dll-- full scan (recon + AI context + taint + verify + report)/scan appinfo.dll --top 15-- analyze top 15 entry points/scan appinfo.dll --taint-only-- delegate to/taint(AI-driven taint scanner)/scan appinfo.dll --memory-only-- delegate to/memory-scan(AI-driven scanner)/scan appinfo.dll --logic-only-- delegate to/ai-logical-bug-scan(AI-driven scanner)/scan appinfo.dll <function>-- all detectors on a specific function/scan appinfo.dll --auto-audit-- after scanning, automatically audit the top 3 CRITICAL/HIGH findings/scan appinfo.dll --no-cache-- bypass cached results for all scanners
IMPORTANT: Execution Model
This is an execute-immediately command. Run the full pipeline and deliver the completed report. Use the grind loop for large modules where all phases cannot complete in one pass.
Workspace Protocol
This command orchestrates many analysis steps:
- Create
.claude/workspace/<module>_scan_<timestamp>/. - Store per-phase results in
<run_dir>/<phase>/results.json. - Use
<run_dir>/manifest.jsonto track completed/failed phases. - Keep only summaries in context; read full findings from results.json on demand.
Execution Context
IMPORTANT: Any inline Python that imports
helpers.*must run withcd <workspace>/.claude(so the.claude/directory is onsys.path), not from the workspace root. Script invocations likepython .claude/skills/.../script.pycan be run from the workspace root because those scripts manage their own path setup.
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 · 158 lines · 0 tokens per session scan A 300ec0bed37c
scan is a command published in the GitHub repository marcosd4h/DeepExtractRuntime (20 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,957 tokens. 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.