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-improvement-loopgit 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-improvement-loop)<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/reverse-engineering-improvement-loop"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/reverse-engineering-improvement-loop/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-improvement-loop"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/reverse-engineering-improvement-loop.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 | $0.00037 | $0.00457 |
| Opus 5 | $0.00018 | $0.00229 |
| Sonnet 5 | $0.00007 | $0.00091 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
reverse-engineering-improvement-loop 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 9d 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.
What it actually says
Reverse Engineering Improvement Loop
This skill keeps reverse engineering from resetting to zero every session. It is the slim memory layer: keep only what future runs can reuse.
Run This After
- a major unknown binary onboarding session
- a subsystem naming campaign
- a debugger session that resolved a recurring ambiguity
- a malware triage pass that changed the investigation direction
- any milestone where the same confusion or workaround showed up more than once
Workflow
- Review the evidence bundle, not just the final narrative.
- Extract recurring friction:
- wrong first tool choice
- missing checklist
- repeated breakpoint pattern
- weak handoff artifact
- panel coverage gap
- Convert the friction into a concrete improvement target:
- new skill
- updated workflow
- persona refinement
- new evidence template
- rule or automation candidate
- Keep only reusable conclusions in persistent memory.
- Feed the rest into normal session output and move on.
EVOKORE Hooks to Use
session-replayevidence-capturepersistent-narrativesimprovement-cyclessession-wrap
Good Output
- one durable lesson with proof
- one workflow change or skill candidate
- one memory update that future sessions should inherit
Anti-Pattern
Do not dump full transcripts into memory. If a future analyst cannot act on it quickly, it does not belong in the persistent layer.
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.
- 9d ago First seen · 64 lines · 37 tokens per session scan A 5d89f5849686
reverse-engineering-improvement-loop is a skill published in the GitHub repository mattmre/EVOKORE-MCP-PUBLIC (3 stars, last pushed 3mo ago), licensed MIT. It adds 37 tokens to every session and 457 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.
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