Thomaszhou22

Transform vague user requests into precise, high-quality prompts. Two engines: template matching (3300+ library) + LLM meta-prompting (host AI as optimizer). Only activates when the user explicitly turns it on.

拦截危险命令，防止账号被盗后执行破坏性操作

Use when the user asks to create, improve, fix, or audit a README.md file, score their README, document an open source project, or set up new project docs. Provides 8-dimension 0-100 quality scoring, 5-type template matrix (Library, CLI, App, Skill, Data), and pre-publish checklist. Do NOT use for API docs, wikis, inline code comments, or general technical writing.

Systematic self-reflection and iterative output refinement for AI agents. Based on Madaan et al. (2023) Self-Refine, Shinn et al. (2023) Reflexion, and Andrew Ng's Reflection pattern. Use when outputs need multi-round critique-and-improve cycles, when initial quality is insufficient, or when complex tasks benefit from self-correction.

Diagnose, fix, and prevent agent skill trigger failures. Use when a skill doesn't activate, when skills trigger incorrectly, when troubleshooting "skill not working" issues, when auditing skill descriptions for quality, when optimizing trigger accuracy, or when asked "why didn't my skill fire?". Also use proactively after installing new skills or when agent behavior seems to ignore available skills. Covers description optimization, YAML frontmatter validation, token budget analysis, conflict detection, and auto-remediation.

AI agents self-compose task-specific reasoning structures from seed modules. Based on Zhou et al. (2024) SELF-DISCOVER, NeurIPS. Use when facing novel reasoning tasks, when standard chain-of-thought is insufficient, or when the agent needs to discover its own problem-solving strategy.