reef 是做什么的?
用于自我改进型Agent的持续学习基础设施
用于自我改进型Agent的持续学习基础设施
Reef 是一个持续学习基础设施,专为需要自我改进的 AI Agent 设计。它提供了一种机制,使 Agent 能够从历史交互和环境中积累经验,逐步优化其行为与决策能力,而无需手动重新训练或更新规则。该技能支持多种主流 Agent 平台(如 Claude Code、Cursor 等),通过标准化的接口与存储后端(如文件系统、数据库)集成,允许 Agent 自动记录、回顾并从中学习。其核心思想是构建一个轻量级的“经验库”,Agent 在每次任务后可将关键观察、成功策略或失败教训持久化,并在后续任务中自动检索相关经验以辅助决策。对于开发者而言,Reef 降低了实现 Agent 持续学习的门槛,使其能够像人类一样在实践中不断成长,适用于需要长期运行、自适应优化的复杂工作流。
npx skills add Human-Agent-Society/reef用于自我改进型Agent的持续学习基础设施
Extract frames or short clips from videos using ffmpeg.
Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.
Use when testing, fixing, or extending the OpenClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.
Obra superpowers agent skills.
See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...
End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.