goldeneye-pc-port: GoldenEye 007 (N64, 1997) as a native PC game for Windows, Linux, Steam Deck and macOS

by jkdansereau · Agent Tool · ★ 280

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

How to install goldeneye-pc-port

git clone https://github.com/jkdansereau/goldeneye-pc-port.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 64/100

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. Full audit →

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.

agentic-developmentcclaude-codedecompilationgame-portinggoldeneyegoldeneye-007linuxmacosmips

Quick Facts

Stars280
Forks27
LanguageC
CategoryAgent Tool
LicenseMIT
Quality Score64/100
Open Issues25
Last Updated2026-10-21
Created2026-08-30
Platformsclaude-code
Est. Tokens~23k

About goldeneye-pc-port

GoldenEye 007 PC Port GoldenEye 007 (Nintendo 64, 1997) as a native PC game for Windows, Linux and Steam Deck. Native widescreen, a steady 60 fps, 2–4 player split-screen, mouse and keyboard or a modern controller, rebindable controls and an in-game options menu.

Compatible Skills

These tools work well together with goldeneye-pc-port for enhanced workflows:

  • auto-re-agent — semantic(0.16)+complementary+rare_topics+similar_pop+shared_platform (50%)
  • agent-infra — semantic(0.15)+complementary+rare_topics+similar_pop+shared_platform (50%)
  • open-reverselab — semantic(0.22)+complementary+similar_pop+shared_platform (48%)

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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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