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Found 9 Skills
Complete Hindsight documentation for AI agents. Use this to learn about Hindsight architecture, APIs, configuration, and best practices.
Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, or implementing the dialectic chat endpoint for AI agents.
Hybrid memory strategy combining OpenClaw's built-in QMD vector memory with Graphiti temporal knowledge graph. Use for all memory recall requests.
Obsidian vault management combining qmd (search) and notesmd-cli (CRUD). No Obsidian app needed. Use for: (1) searching notes with keyword, semantic, or hybrid search, (2) creating/editing/moving/deleting notes, (3) daily journaling, (4) frontmatter management, (5) backlink discovery, (6) AI agent memory workflows, (7) vault automation and scripting. Triggers: obsidian vault, obsidian notes, vault search, note management, daily notes, agent memory, knowledge base, markdown vault.
Trigger Scenarios: (1) Explicit memory requests – remember, record, don't forget, pay attention next time, form rules, generate summaries/record documents; (2) Correction and modification – note, incorrect, wrong, it should be, change to, replace with, don't, also need, missing; (3) Preference expression – I prefer, in the future, it's better, suggest, my habit, I usually; (4) Global specifications – unified, all, every, any, each, every time, all, uniformly; (5) Conversation end settlement – when the conversation ends naturally or the topic switches. Convert users' corrections, preferences and rules into structured memory files to improve the output quality of subsequent conversations.
Advanced memory operations reference. Basic patterns (profile loading, simple recall/remember) are in project instructions. Consult this skill for background writes, memory versioning, complex queries, edge cases, session scoping, retention management, type-safe results, proactive memory hints, GitHub access detection, and ops priority ordering.
Comprehensive skill for Graphiti and Zep - temporal knowledge graph framework for AI agents with dynamic context engineering
ALWAYS ACTIVE — Persistent memory protocol. You MUST save decisions, conventions, bugs, and discoveries to engram proactively. Do NOT wait for the user to ask.
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.