kunpeng-skill: Powerful multimodal distillation toolkit that transforms high-value insights from repositories, websites, UIs

by hufeng173 · Codex Skill · ★ 54

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

Security audit verdict: SAFE · quality 58/100

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.

agent-skillsclaude-codecodexhermes-agentknowledge-distillationopencodeppt-designpresentation-designproduct-planningui-design

Quick Facts

Stars54
Forks5
LanguagePython
CategoryCodex Skill
LicenseApache-2.0
Quality Score58/100
Last Updated2026-09-04
Created2026-08-06
Platformsbrowser, claude-code, codex, python
Est. Tokens~13k

About kunpeng-skill

Kunpeng Skill A local-first, multi-source distillation Skill that runs inside mainstream AI agent environments such as Codex, Claude Code, WorkBuddy, OpenCode, and Hermes. Install it in the agent you already use, then invoke it with natural-language requests to turn real source material into reusable methods and execution-ready specifications.

Compatible Skills

These tools work well together with kunpeng-skill for enhanced workflows:

  • design-skill-os — semantic(0.40)+complementary+rare_topics+same_lang+similar_pop+shared_platform (69%)
  • Mck-ppt-design-skill — semantic(0.25)+complementary+rare_topics+same_lang+similar_pop+shared_platform (63%)

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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