Hindsight Memory Skill
技能 已验证 活跃Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user)
To empower AI agents with persistent memory, enabling them to learn from past tasks and user interactions to provide more informed and personalized assistance.
功能
- Store structured facts and entities
- Recall relevant context proactively
- Reflect on memories to synthesize insights
- Support for multiple LLM providers
- Local and Dockerized deployment options
使用场景
- Personalizing AI chatbots with user-specific memories
- Enabling AI agents to learn from past task outcomes and failures
- Automating complex tasks by recalling previous successful procedures
- Building AI employees that adapt behavior based on user feedback
非目标
- Simply recalling raw conversation history
- Acting as a generic knowledge base without learning
- Replacing core LLM functionality
- Managing project files or code
工作流
- Configure Hindsight daemon (CLI or Docker)
- Retain memories with rich context
- Recall relevant memories before tasks
- Reflect on memories for deeper insights
实践
- Memory management
- Agent learning
- Data structuring
先决条件
- Hindsight embed CLI configured
- LLM provider and API key
安装
npx skills add vectorize-io/hindsight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
Orchestrate
100Wire Commands, Agents, and Skills together for complex features. Use when building features that need research, planning, and implementation phases.
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.
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.
TradeMemory Protocol
100Evolution Engine 的领域知识 — 支持 LLM 从原始 OHLCV 数据中自主发现策略。涵盖生成-回测-选择-进化循环、向量化回测、样本外验证和策略梯度。在发现交易模式、运行回测、进化策略或审查进化日志时使用。由“evolve”、“discover patterns”、“backtest”、“evolution”、“strategy generation”、“candidate strategy”触发。
Learner Skill
99Extract a learned skill from the current conversation
Agentdb Memory Patterns
99Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.