Hindsight Memory Skill
Skill Verified ActiveStore 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.
Features
- 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
Use Cases
- 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
Non-Goals
- Simply recalling raw conversation history
- Acting as a generic knowledge base without learning
- Replacing core LLM functionality
- Managing project files or code
Workflow
- Configure Hindsight daemon (CLI or Docker)
- Retain memories with rich context
- Recall relevant memories before tasks
- Reflect on memories for deeper insights
Practices
- Memory management
- Agent learning
- Data structuring
Prerequisites
- Hindsight embed CLI configured
- LLM provider and API key
Installation
npx skills add vectorize-io/hindsightRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
Similar Extensions
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
100Domain knowledge for the Evolution Engine — LLM-powered autonomous strategy discovery from raw OHLCV data. Covers the generate-backtest-select-evolve loop, vectorized backtesting, out-of-sample validation, and strategy graduation. Use when discovering trading patterns, running backtests, evolving strategies, or reviewing evolution logs. Triggers on "evolve", "discover patterns", "backtest", "evolution", "strategy generation", "candidate strategy".
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.