flameox: Runtime evidence that helps agents trace, profile, and burn down hotspots in application and native code, GPU

by morluto · MCP Server · ★ 220

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install flameox

npx flameox@latest setup

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

benchmarkingcoding-agentscordisdebuggingdeveloper-toolsdshdsh-plugingpu-profilinglocal-firstmcp

Quick Facts

Stars220
Forks21
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score66/100
Open Issues2
Last Updated2026-10-26
Created2026-07-25
Platformsmcp, python
Est. Tokens~15k

About flameox

flameox Bounded local runtime evidence for coding agents. Flameox coordinates profilers, benchmark tools, trace processors, and direct local targets. It gives an agent a short path from an explicit native artifact or live command to bounded evidence, while keeping preservation optional. Version 0.2 is a clean break.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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