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 semantic-recovery-campaigngit 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/semantic-recovery-campaign)<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/semantic-recovery-campaign"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/semantic-recovery-campaign/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/semantic-recovery-campaign"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/semantic-recovery-campaign.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 | $0.00047 | $0.00515 |
| Opus 5 | $0.00023 | $0.00258 |
| Sonnet 5 | $0.00009 | $0.00103 |
| Haiku 4.5 | $0.00005 | $0.00052 |
Grade A, and why
semantic-recovery-campaign 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 12d 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
Semantic Recovery Campaign
Use this skill when the goal is no longer "find something interesting" but "make a subsystem understandable and durable for future analysis."
Campaign Rules
- Work subsystem-first, not function-random.
- Promote names and types only when evidence can defend them.
- Keep confidence levels explicit: confirmed, likely, speculative.
- Batch related renames and type updates so the codebase becomes more coherent with each pass.
- Stop and escalate to dynamic validation if the same uncertainty keeps blocking semantic cleanup.
Workflow
- Define the subsystem scope: networking, config, crypto, installer, UI bridge, anti-analysis, and so on.
- Pick representative functions and recover their real role using:
- callers / callees
- xrefs to and from
- strings and constants
- structure and API evidence
- Create a local glossary:
- subsystem terms
- recovered struct or class names
- prefixes or naming conventions
- Apply durable edits:
- function names
- variable names
- prototypes
- local types
- comments for branch purpose, not narration
- Record what is still blocking confidence.
Best Pairings
ghidra-function-analysisfor each representative routineghidra-rename-and-retypefor durable semantic editsdebugger-driven-analysiswhen runtime proof is requiredorch-panel reinsemantic-recoverymode for high-stakes conclusions
Deliverable Format
- subsystem summary
- naming glossary
- confirmed edits
- speculative edges
- next proof steps
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.
- 12d ago First seen · 63 lines · 47 tokens per session scan A d8cb54b3fbb8
semantic-recovery-campaign is a skill published in the GitHub repository mattmre/EVOKORE-MCP-PUBLIC (3 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 515 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".
wakaru
Turn minified, bundled, or transpiled JavaScript back into readable modules. Use when you encounter unreadable production JS — a webpack/esbuild/Metro/Rollup bundle, a minified vendor script, Babel/TypeScript/SWC-transpiled output, or a single mangled .js file — and need to read, audit, debug it, or recover a…
ctf-rev
Solve CTF reverse engineering challenges using systematic analysis to find flags, keys, or passwords. Use for crackmes, binary bombs, key validators, obfuscated code, algorithm recovery, or any challenge requiring program comprehension to extract hidden information.
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…
pyghidra-scripting
Write and run Python (PyGhidra) code inside the Ghidra session that ReVa's MCP server is already attached to, using the five ReVa scripting tools — run-script, list-scripts, read-script, write-script, edit-script. Use this whenever the user asks to execute Python against the current program, reach for the Ghidra Flat…