Evolving Ai Agents
技能 已验证 活跃Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
To automate the improvement of AI agents by leveraging LLM-driven evolution, making agents smarter and more performant over time.
功能
- LLM-driven evolution of agent prompts, skills, and memory
- File-system based workspace contract managed via Git
- Iterative solve-observe-evolve cycles against benchmarks
- Pluggable interfaces for agents, benchmarks, and engines
- Built-in seed agents and benchmarks for common domains
使用场景
- Optimizing agent prompts and skills against measurable benchmarks
- Building self-improving agents with automated gating and rollback
- Evolving domain-specific tool usage and procedures
- Implementing automated agent evaluation loops
非目标
- Building multi-agent orchestration from scratch
- One-shot agent tasks with no iteration needed
- RAG pipeline optimization
- Prompt-only optimization without skill/memory evolution
安装
npx skills add Orchestra-Research/AI-Research-SKILLs通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
Flow Nexus Platform
100Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges
Chat Format
100Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
Oh My Claudecode
100Process-first advisor routing for Claude, Codex, or Gemini via `omc ask`, with artifact capture and no raw CLI assembly
Wrap Up Ritual
100End-of-session ritual that audits changes, runs quality checks, captures learnings, and produces a session summary. Use when saying "wrap up", "done for the day", "finish coding", or ending a coding session.
Project Development
100This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture, agent-assisted development, cost estimation, or choosing between LLM and traditional approaches.
Context Compression
100This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.