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/ai-logic-scannerWrote 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/ai-logic-scanner)<a href="https://agentmods.dev/skills/marcosd4h/deepextractruntime/ai-logic-scanner"><img src="https://agentmods.dev/badge/skills/marcosd4h/deepextractruntime/ai-logic-scanner.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 | $0.00081 | $0.02789 |
| Opus 5 | $0.00041 | $0.01394 |
| Sonnet 5 | $0.00016 | $0.00558 |
| Haiku 4.5 | $0.00008 | $0.00279 |
Grade A, and why
ai-logic-scanner 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Logic Scanner
Purpose
Find exploitable logic vulnerabilities in Windows PE binaries using LLM-driven code analysis instead of regex pattern matching. The scanner builds a cross-module callgraph from attacker-reachable entry points, classifies every node (MUST_READ / KNOWN_API / TELEMETRY / LIBRARY), and delivers code to the AI agent in depth-level batches. The agent analyzes each batch for authentication, authorization, state management, and trust boundary flaws, returns taint-guided requests for deeper functions, and reads deeper code itself via Shell -- iterating until max depth or taint termination.
This is NOT a pattern scanner. All vulnerability detection decisions are made by the LLM agent using adversarial prompting, invariant decomposition, and type-specific specialist knowledge. A separate skeptic agent independently verifies each finding.
When to Use
- Scan a module for logic vulnerabilities (auth bypass, state machine errors, confused deputy, privilege escalation)
- Analyze a specific RPC handler or COM method for authorization gaps
- Deep analysis of a call chain from an entry point to privileged operations
- AI-driven vulnerability research on decompiled Windows service binaries
- When static analysis produces too many false positives or misses subtle logic bugs that require understanding control flow and state
When NOT to Use
- Memory corruption (buffer overflows, integer overflows, UAF) -- use
ai-memory-corruption-scanner or
/memory-scan - Code lifting or rewriting -- use code-lifter
- General function explanation -- use re-analyst or
/explain
Data Sources
SQLite Databases (primary)
Individual analysis DBs in extracted_dbs/ provide the raw data:
functions.decompiled_code-- Hex-Rays decompiled C (read on demand)functions.assembly_code-- x64 assembly ground truth (read on demand)functions.simple_outbound_xrefs-- Callgraph edgesfunctions.function_signature-- Parameter types and namesfile_info.exports-- Exported functionsfile_info.entry_point-- PE entry points
What ships with it
6 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.
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 · 273 lines · 81 tokens per session scan A 0e0732146560
ai-logic-scanner is a skill published in the GitHub repository marcosd4h/DeepExtractRuntime (19 stars, last pushed 4mo ago), licensed MIT. It adds 81 tokens to every session and 2,789 once invoked, about $0.0004 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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