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 skills add mattmre/EVOKORE-MCP-PUBLIC --skill reverse-engineering-company-systemgit clone --depth 1 https://github.com/mattmre/EVOKORE-MCP-PUBLICWrote 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/mattmre/evokore-mcp-public/reverse-engineering-company-system)<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/reverse-engineering-company-system"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/reverse-engineering-company-system/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/skills/mattmre/evokore-mcp-public/reverse-engineering-company-system"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/reverse-engineering-company-system.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 | $0.00045 | $0.00903 |
| Opus 5 | $0.00023 | $0.00451 |
| Sonnet 5 | $0.00009 | $0.00181 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade A, and why
reverse-engineering-company-system 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 11d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reverse Engineering Company System
Use this skill when reverse engineering needs to behave like a practiced research organization instead of a sequence of disconnected tool invocations. The goal is additive output: static recon informs semantic recovery, semantic recovery sharpens debugger plans, dynamic evidence hardens capability claims, and every session leaves behind reusable memory.
Core Operating Model
- Intake and segmentation — classify the target and decide whether the main lane is native, .NET, JVM/Android, firmware, or suspicious-sample triage.
- Static recon — imports, exports, strings, entry points, subsystem map, priority function queue.
- Semantic recovery — decompilation, xrefs, renaming, typing, comments, structure recovery, certainty scoring.
- Dynamic hypothesis testing — x64dbg / WinDbg / pwndbg-style flows, Qiling or other emulator help when execution context matters.
- Capability synthesis — threat behavior, ATT&CK framing, persistence / C2 / credential / anti-analysis claims when relevant.
- Learning capture — evidence, unresolved questions, automation candidates, persona/workflow improvements.
EVOKORE-Native Building Blocks
- Panel:
orch-panel reuses the Reverse Engineering Panel for unknown binaries, semantic campaigns, debugger planning, and workflow refinement. - Workflow:
panel-of-experts/workflows/reverse-engineering-analysis.json - Research workflow:
panel-of-experts/workflows/reverse-engineering-repo-research.json - Learning loop:
panel-of-experts/workflows/reverse-engineering-improvement-loop.json
Complementary Skill Stack
Start from the skill that matches the phase:
unknown-binary-onboarding— establish the first analysis charter and choose tool lanes.ghidra-core-recon— map the binary before interpreting it.ghidra-function-analysis— recover subsystem semantics.semantic-recovery-campaign— run naming/type cleanup as a disciplined campaign.debugger-driven-analysis— resolve runtime-only ambiguity.malware-triage-workflow— escalate suspicious behavior into capability-oriented triage.reverse-engineering-improvement-loop— convert session evidence into better future workflows.
What ships with it
3 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.
- 11d ago First seen · 81 lines · 45 tokens per session scan A c6b9b0144edb
reverse-engineering-company-system is a skill published in the GitHub repository mattmre/EVOKORE-MCP-PUBLIC (3 stars, last pushed 3mo ago), licensed MIT. It adds 45 tokens to every session and 903 once invoked, about $0.0002 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 skills, from other repositories
ts-debug
TypeScript/Node debugging expert. Use when the user needs to debug, profile, or trace TypeScript or Node.js code — e.g. "how do I debug this", "find the memory leak", "why is this slow", "add a breakpoint", "profile this function", "why won't the process exit".
py-debug
Python debugging expert. Use when the user needs to debug, profile, or trace Python code — e.g. "how do I debug this", "find the memory leak", "why is this slow", "add a breakpoint", "profile this function".
Subagent Driven Development
Orchestrate specialized, autonomous AI agents to execute parallel subtasks with strict boundaries.
agent-orchestrator
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
python-code-quality
Code quality checks, linting, formatting, and type checking commands for the Agent Framework Python codebase. Use this when running checks, fixing lint errors, or troubleshooting CI failures.
deep-analysis
Performs focused, depth-first investigation of specific reverse engineering questions through iterative analysis and database improvement. Answers questions like "What does this function do?", "Does this use crypto?", "What's the C2 address?", "Fix types in this function". Makes incremental improvements (renaming…