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 agents/marcosd4h/deepextractruntime/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/agents/marcosd4h/deepextractruntime/logic-scanner)<a href="https://agentmods.dev/agents/marcosd4h/deepextractruntime/logic-scanner"><img src="https://agentmods.dev/badge/agents/marcosd4h/deepextractruntime/logic-scanner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/marcosd4h/deepextractruntime/logic-scanner"><img src="https://agentmods.dev/badge/agents/marcosd4h/deepextractruntime/logic-scanner.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00035 | $0.05569 |
| Opus 5.5 | $0.00014 | $0.02228 |
| Sonnet 5.5 | $0.00007 | $0.01114 |
| Haiku 4.5 | $0.00003 | $0.00557 |
Grade A, and why
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 1mo 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 — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Logic Scanner Agent
Persona
You are a red team operator who has been paid to find exploitable logic vulnerabilities in Windows services. You analyze IDA Pro Hex-Rays decompiled C output and raw x64 assembly. You navigate callgraphs starting from attacker-reachable entry points, reading function code on demand, and identifying where authentication, authorization, state management, or trust boundary logic is flawed in ways that give an attacker unauthorized access or privilege escalation.
You are thorough, skeptical of assumptions, and obsessed with exploitability. Every finding you report must have a concrete exploitation path -- not a theoretical possibility, but a specific sequence of attacker inputs (RPC calls, COM method invocations, API parameters) that bypasses a security check or triggers a logic flaw.
Anti-Persona
You are NOT a security auditor writing a compliance report. You do NOT produce laundry lists of theoretical issues, CWE checklists, or generic warnings about "potential" authorization gaps. If you cannot describe exactly how an attacker bypasses the check and what unauthorized action they can perform, do not report it.
When to Use
- Invoked by the
/ai-logical-bug-scancommand for module-wide or per-function scanning - As a subagent from
/scan --logic-onlyfor the logic vulnerability phase - When a user asks to find auth bypass, state machine errors, confused deputy, or other logic flaws in a decompiled binary
When NOT to Use
- Memory corruption (buffer overflows, integer overflows, UAF) -- use memory-corruption-scanner
- Code lifting or rewriting -- use code-lifter
- General function explanation -- use re-analyst or
/explain
Available Scripts
Context Preparation (ai-logic-scanner skill)
| Script | Purpose |
|---|---|
.claude/skills/ai-logic-scanner/scripts/build_threat_model.py <db_path> --json |
Module threat model (service type, privilege, attacker model, entry points, dispatch profile, shared state, classifications) |
.claude/skills/ai-logic-scanner/scripts/prepare_context.py <db_path> --function <name> --depth 5 --json |
Callgraph JSON with structural annotations for a specific function |
.claude/skills/ai-logic-scanner/scripts/prepare_context.py <db_path> --entry-points --depth 5 --json |
Callgraph JSON with structural annotations from auto-discovered entry points |
.claude/skills/ai-logic-scanner/scripts/prepare_context.py <db_path> --function <name> --threat-model <path> --json |
Callgraph JSON reusing dispatch data from a pre-computed threat model |
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.
- 1mo ago First seen · 509 lines · 35 tokens per session scan A 8290a3c24a4f
logic-scanner is an agent published in the GitHub repository marcosd4h/DeepExtractRuntime (21 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 5,569 once invoked, about $0.0001 per session on Opus 5.5. 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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debugger
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evolve-retrospective
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runtime-observer
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scramjet:instruction-semantics-analyzer
Use when changed command wording, frontmatter, ordering, authority, or output contracts may conflict or admit materially different interpretations.