No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Prysai · Codex Skill · ★ 439
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Prysai-LLM-Playbook safe to install? View the security audit →
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. You are working toward a bounded task card, a checked result record, and one transfer attempt; these are targets, not measured outcomes. The repository is the auditable source, not the recommended first screen. Start the LLM foundation route · Try the optional five-minute practice · Read the full English guide · Open the optional Codex boundary chapter
| Stars | 439 |
| Forks | 8 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 61.8826737424732/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-16 |
| Created | 2026-08-09 |
| Platforms | claude-code, codex, gemini, python |
| Est. Tokens | ~18k |
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Prysai-LLM-Playbook is An evidence-led, eight-locale LLM playbook: a transferable core, the Codex flagship track, and adapters for ChatGPT, Claude Code, Gemini, DeepSeek, and Grok.. It is categorized as a Codex Skill with 439 GitHub stars.
Prysai-LLM-Playbook is primarily written in Python. It covers topics such as agent-workflows, ai, ai-agents.
You can find installation instructions and usage details in the Prysai-LLM-Playbook GitHub repository at github.com/Prysai/Prysai-LLM-Playbook. The project has 439 stars and 8 forks, indicating an active community.
The top alternatives to Prysai-LLM-Playbook on Agent Skills Hub include free-ai-resources-x, gateway, agnix. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: