Prysai-LLM-Playbook: An evidence-led, eight-locale LLM playbook: a transferable core, the Codex flagship track, and adapters

by Prysai · Codex Skill · ★ 437

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

Security audit verdict: SAFE · quality 62/100

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.

agent-workflowsaiai-agentsai-safetycodexlearning-resourcesllmprompt-engineering

Quick Facts

Stars437
Forks8
LanguagePython
CategoryCodex Skill
Quality Score62/100
Open Issues2
Last Updated2026-09-23
Created2026-08-09
Platformsclaude-code, codex, gemini, python
Est. Tokens~18k

About Prysai-LLM-Playbook

Prysai LLM Playbook — From First Task to Reliable Work Learn what an LLM can and cannot establish, make one bounded request, inspect the answer, and carry the method to a new task before choosing a platform. Languages: English Français New here? Open the guided reading site — start the five-unit LLM foundation route. You do not need Codex, Git, a terminal, or a private file to begin.

Compatible Skills

These tools work well together with Prysai-LLM-Playbook for enhanced workflows:

  • SKILLS_All-in-one — semantic(0.22)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • meihua-yishu — semantic(0.27)+complementary+same_lang+similar_pop+shared_platform (60%)
  • codeArbiter — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (59%)
  • claude_code-multi-AI-MCP — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (58%)

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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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