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概要と使いどころ
Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running agents.
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Use when you need semantic retrieval from perpetual memory at query time. Parses query intent, searches vector store, ranks by relevance and recency, and injects context. Complements memory-search.cjs with the perpetual_memory LanceDB table.
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Reflective Memory. Use when working with memory, reflection, context management, or intent tracking. Provides the reflective memory practice — how and when to use keep tools for capturing insights, tracking commitments, and maintaining awareness.
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Generates a structured Project Brief — a comprehensive markdown document that extracts goals, architecture, knowledge file inventory, Custom Instructions summary, Memory contents, current state, ecosystem position, and key decisions from a Claude Project. Briefs serve as uploadable context documents: add one to any other Project for immediate deep awareness of the source Project's purpose, architecture, and progress. Use when user says "create a brief," "brief this project," "extract project context," "generate a project summary for another project," "I need to share this project's context," "prepare this project for cross-project reference," or "document this project." Also use when the user is preparing to work across Projects and needs portable context. Do NOT use for session handoffs, project audits, or Memory optimization (use rootnode-session-handoff, rootnode-project-audit, or rootnode-memory-optimization respectively, if available).
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Use when analyzing warp divergence, memory coalescing, shared memory bank conflicts, cache behavior, atomics, or occupancy tradeoffs on NVIDIA and AMD GPUs. Not for tool commands: use cuda-profiling.
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Decide what to PERSIST in your data structures (graph nodes, DB tables, cache layouts) based on the QUERIES you must answer, not the DEFINITIONS that justify the data. Definitions tell you logical identity; storage strategy is independent. Materializing every definitional component leads to O(N×M) blow-ups that masquerade as memory leaks. Use when: (1) designing a new graph-node type, edge type, DB table, or cache layout; (2) about to name a `subgraph` / `comprises` / `members` / `contains` collection on a parent entity; (3) memory growth proportional to (entities × per-entity-elements); (4) "memory leak" debugging that survives multiple targeted patches — the leak may be storage shape, not retention; (5) pre-implementation sketch includes "for each X, store all reached nodes"; (6) research/spec doc says "X = ⟨A, B, C⟩" and you're tempted to make it three storage edges per X.
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Turn travel or lifestyle photos into 4:5 memory-diary pages pairing real photography with a watercolor and colored-pencil interpretation of the same moment. Use for editorial memory archives and photo-illustration diaries.
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Design AI memory systems with context management. TRIGGERS - Use when user needs help with ai-memory-system related tasks.
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Design memory systems for AI agents. TRIGGERS - Use when user needs help with ai-agent-memory related tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Reflective pass over your memory — merge duplicates, fix stale facts, prune what no longer matters. Use when the user asks to clean up or review what you remember, or when memory has grown cluttered or contradictory.
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Import a memory export from another AI assistant (ChatGPT, Gemini, Claude, etc.) into this agent's memory — conversationally, additively, and with the pasted content treated as data. Use when the user wants to bring over what another assistant knows about them.
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Sets up a file-based memory system so Cowork always knows who you are, how you work, and where your projects stand. For eliminating repetitive re-introductions at session start, when building persistent context across multiple projects, or during initial Cowork onboarding. Use when the user says "claude doesn't remember me", "memory system", "persistent context", "why does it forget", "remember my preferences", "I keep re-explaining myself", or "how do I make Claude remember things".
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Use when implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.
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Manage persistent memory for autonomous coding. Use when storing/retrieving knowledge, managing Graphiti integration, persisting learnings, or accessing episodic memory.
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Searches for Memorie van Toelichting (explanatory memoranda) for a Dutch law and generates Gherkin test scenarios from legislature-intended examples. Use this skill proactively when: user wants MvT-derived BDD scenarios, mentions "memorie van toelichting", "MvT", "parlementaire stukken", "rekenvoorbeelden", or "kamerstukken" in the context of Dutch law. Activate automatically when a new law YAML file is created and the user discusses testing or validation.
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Two-layer memory system with Dream-managed knowledge files.
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Write structured legal memoranda following the Issue-Rule-Analysis-Conclusion (IRAC) framework. Suitable for internal legal opinions, advisory memos, and compliance assessments. Use when legal analysis needs to be documented for decision-makers. Do not use as a substitute for licensed legal advice on high-stakes matters.
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Memory forensics playbook using Volatility 2/3. Use when analyzing memory dumps for malware analysis, credential extraction, process investigation, code injection detection, and incident response timeline reconstruction.
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Carves injected PE images and position-independent shellcode from a memory dump or carved region, identifies the payload type, and prepares it for disassembly or emulation. Activates for requests to recover injected code, carve shellcode from memory, or reconstruct a dumped payload for analysis.
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Writes, reviews, and debugs idiomatic Rust code with memory safety and zero-cost abstractions. Implements ownership patterns, manages lifetimes, designs trait hierarchies, builds async applications with tokio, and structures error handling with Result/Option. Use when building Rust applications, solving ownership or borrowing issues, designing trait-based APIs, implementing async/await concurrency, creating FFI bindings, or optimizing for performance and memory safety. Invoke for Rust, Cargo, ownership, borrowing, lifetimes, async Rust, tokio, zero-cost abstractions, memory safety, systems programming.
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Design memory for AI agents — short-term context, long-term stores, retrieval strategies, and memory-augmented reasoning. Use when an agent must remember facts, learn preferences, or stay coherent across sessions.
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Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.
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Use when planning, scaffolding, validating, or packaging Claude skills inside Advanced Memory MCP.
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Persistent memory patterns for moflo agents — session memory, long-term knowledge, pattern learning, and cross-session context via moflo's node:sqlite + HNSW vector store. Use when building stateful agents or assistants that need to remember across runs.
日本語の概要は準備中です。原文の説明を表示しています。