Hindsight Memory Skill
Skill Verifiziert AktivStore 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.
Funktionen
- 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
Anwendungsfälle
- 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
Nicht-Ziele
- 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
Praktiken
- Memory management
- Agent learning
- Data structuring
Voraussetzungen
- Hindsight embed CLI configured
- LLM provider and API key
Installation
npx skills add vectorize-io/hindsightFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
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
100Domänenwissen für die Evolution Engine — LLM-gestützte autonome Strategieentdeckung aus rohen OHLCV-Daten. Behandelt die Schleife Generieren-Backtesten-Auswählen-Entwickeln, vektorisiertes Backtesting, Out-of-Sample-Validierung und Strategiegraduierung. Verwenden Sie es beim Entdecken von Handelspatterns, Ausführen von Backtests, Entwickeln von Strategien oder Überprüfen von Evolutionsprotokollen. Löst aus bei "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.