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Found 57 Skills
Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcp__memory knowledge graph for persistent facts
Interact with the SlipBox semantic knowledge engine and read notes from PrivateBox. Use when capturing ideas, searching notes, browsing your knowledge graph, or running semantic analysis passes (link, cluster, tension).
Route durable graph-building requests into one honest mode: assistant-native install, local Python build, incremental refresh, graph query follow-up, or a graphify-style structural fallback for markdown-heavy corpora. Use when the user wants `GRAPH_REPORT.md`, `graph.json`, `graph.html`, repo/corpus relationship tracing, mixed code+docs+asset graphing, or graph-backed architecture understanding that should persist across sessions. Route simple locate/reference work to `codebase-search`, narrative knowledge-base work to `llm-wiki`, and project-memory handoff to `opencontext`.
Use when you need to generate an onboarding guide for new team members joining a project
Use when implementing agent memory, persisting state across sessions, building knowledge graphs, tracking entities, or asking about "agent memory", "knowledge graph", "entity memory", "vector stores", "temporal knowledge", "cross-session persistence"
Knowledge graph-based text replacement using Terraphim hooks. Intercepts commands and text to apply transformations defined in the knowledge graph. Works with Claude Code PreToolUse hooks and Git prepare-commit-msg hooks.
Task management via Basic Memory schemas: create, track, and resume structured tasks that survive context compaction. Uses BM's schema system for uniform notes queryable through the knowledge graph.
Persistent memory architecture for AI agents across sessions. Episodic memory (past events), procedural memory (learned skills), semantic memory (knowledge graph), short-term memory (active context). Use when implementing cross-session persistence, skill learning, context preservation, personalization, or building truly adaptive AI systems with long-term memory.
Research an external subject using web search, synthesize findings into a structured Basic Memory entity. Use when asked to research a company, person, technology, or topic — or when a bare name or URL is provided that implies a research request.
High-performance code intelligence MCP server that indexes codebases into knowledge graphs for structural queries, call traces, and architecture analysis
Maintains persistent codebase knowledge across sessions through a structured knowledge graph stored in a local Obsidian vault (.doctrack/). Use this skill whenever you have just made meaningful code changes (new features, modified components, refactoring, bug fixes) to update the project's documentation. Also use it when the user asks to document code, update docs, sync documentation, initialize documentation for an existing project, or when you want to understand the existing codebase structure at the start of a session. This skill should be used proactively after any significant code modification — don't wait for the user to ask. If you changed code, update the docs. Think of it as your long-term memory system: read before working, write after changing. Also use this when a user says "doctrack init", "doctrack refresh", "refresh docs", "update docs", "sync docs", "initialize docs", "document this project", or wants to bootstrap documentation for a codebase that has no .doctrack/ vault yet.
Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships