paranoia-ai-system-evolver
bydy-2026· 7 skills
用于升级 AI 系统、agent workflow、Codex skill、prompt、memory、RAG、tool routing、schema、eval set 或 feedback loop；也用于把 AI 工作单从指令单升级为意图单，并对研究、检索、测试和 AI 对话做 VOI 决策门审计。需要 Intent Work Order、WOOP 任务准入、决策对象、VOI/EVPI/EVSI、UL（Uncertainty Ladder，不确定性阶梯）、OODA、eval、Human Gate、versioning 与 rollback 的受控演化时使用。Use when controlled AI system evolution or a decision-oriented information audit is needed.
How to install paranoia-ai-system-evolver?
dy-2026/gamedesignos/paranoia-ai-system-evolver
$npx -y skills add dy-2026/gamedesignos --skill paranoia-ai-system-evolverInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/dy-2026/gamedesignos" --skill "dy-2026/gamedesignos/paranoia-ai-system-evolver"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/dy-2026/gamedesignos" that are relevant to the current task. Run `npx skills add "https://github.com/dy-2026/gamedesignos"` and select the relevant skills, then follow their instructions.
Related
6 picksType
- azure-aiUse for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR.Jul 2026
- ai-research-exploreRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates.Jul 2026
- ai-research-reproductionRigor Reproduce compatible skill slug for README-first deep learning repository reproduction.Jul 2026
- explore-codeRigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories.Jul 2026
- run-trainRigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup…Jul 2026
- explore-runRigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories.Jul 2026
