image-cockpit-for-codex-workflows: Local cockpit for Codex imagegen, pixel art, image editing, animation, and sprite-sheet workflows

by dreiachse-cyber · Codex Skill · ★ 296

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

Security audit verdict: SAFE · quality 67/100

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.

codexgame-devimage-generationlocal-firstpixel-artreactsprite-sheetvite

Quick Facts

Stars296
Forks39
LanguageTypeScript
CategoryCodex Skill
LicenseMIT
Quality Score67/100
Last Updated2026-07-28
Created2026-06-23
Platformscodex, node
Est. Tokens~16k

About image-cockpit-for-codex-workflows

Image Cockpit for Codex Workflows Local image production cockpit for Codex-era workflows. This project is unofficial and not affiliated with OpenAI. It is a local workspace for generating pixel art, then turning selected pixel-art assets into animation frames and sprite sheets. Product Boundary Image Cockpit is designed to run on a local machine where Codex is installed.

Compatible Skills

These tools work well together with image-cockpit-for-codex-workflows for enhanced workflows:

  • sprite-gen — semantic(0.41)+complementary+rare_topics+similar_pop+shared_platform (68%)
  • mcp-image — semantic(0.27)+complementary+same_lang+similar_pop+shared_platform (59%)
  • age-of-agents — semantic(0.55)+rare_topics+same_lang+similar_pop+shared_platform (59%)

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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:

View on GitHub → Browse Codex Skill tools