Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/marcosd4h/DeepExtractRuntimenpx agentmods add skills/marcosd4h/deepextractruntime/generate-re-reportWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/marcosd4h/deepextractruntime/generate-re-report)<a href="https://agentmods.dev/skills/marcosd4h/deepextractruntime/generate-re-report"><img src="https://agentmods.dev/badge/skills/marcosd4h/deepextractruntime/generate-re-report.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00096 | $0.02635 |
| Opus 5 | $0.00048 | $0.01318 |
| Sonnet 5 | $0.00019 | $0.00527 |
| Haiku 4.5 | $0.00010 | $0.00264 |
Grade A, and why
generate-re-report 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 8d 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 — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate RE Report
Purpose
Generate synthesized reverse engineering reports from DeepExtractIDA analysis databases. Unlike raw data dumps (file_info.md/file_info.json), this skill cross-correlates data, computes derived metrics, and produces actionable guidance -- the report you'd write manually after hours with the binary, generated in seconds.
This is per-module analysis. Each report covers one binary. The report is a living document that can be regenerated as analysis progresses.
When NOT to Use
- Deep security analysis of a specific function -- use security-dossier or taint-analysis
- Classifying functions by purpose with interest scoring -- use classify-functions
- Scanning for specific vulnerability classes -- use ai-memory-corruption-scanner or ai-logic-scanner
- Lifting or rewriting decompiled code -- use the code-lifter agent
Data Sources
Reports are generated from individual analysis databases (extracted_dbs/{module}_{hash}.db). These contain per-function data (assembly, decompiled code, xrefs, strings, loops, globals, stack frames, analysis errors) plus binary-level metadata (imports, exports, sections, security features, Rich header, TLS callbacks, load config).
Finding a Module DB
Reuse the decompiled-code-extractor skill's find_module_db.py:
python .claude/skills/decompiled-code-extractor/scripts/find_module_db.py --list
python .claude/skills/decompiled-code-extractor/scripts/find_module_db.py appinfo.dll
Quick Cross-Dimensional Search
To search across function names, signatures, strings, APIs, classes, and exports in one call:
python .claude/helpers/unified_search.py <db_path> --query "SearchTerm"
python .claude/helpers/unified_search.py <db_path> --query "SearchTerm" --json
Utility Scripts
Pre-built scripts in scripts/ handle all analysis and report generation. Run from the workspace root.
generate_report.py -- Full Report (Start Here)
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- README.md 7.1 KB
- reference.md 6.9 KB
- scripts/_common.py 6.7 KB runs code
- scripts/analyze_complexity.py 12 KB runs code
- scripts/analyze_decompilation_quality.py 11 KB runs code
- scripts/analyze_imports.py 9.1 KB runs code
- scripts/analyze_strings.py 6.9 KB runs code
- scripts/analyze_topology.py 12 KB runs code
- scripts/generate_report.py 23 KB runs code
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.
- 8d ago First seen · 275 lines · 96 tokens per session scan A 47800a3ac134
generate-re-report is a skill published in the GitHub repository marcosd4h/DeepExtractRuntime (19 stars, last pushed 4mo ago), licensed MIT. It adds 96 tokens to every session and 2,635 once invoked, about $0.0005 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.
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