「memory」の検索結果

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.

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.

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。

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".

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。

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.

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。

Manage persistent memory for autonomous coding. Use when storing/retrieving knowledge, managing Graphiti integration, persisting learnings, or accessing episodic memory.

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。

Two-layer memory system with Dream-managed knowledge files.

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。

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.

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。

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.

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。

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.

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。

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.

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。

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.

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。

Use when planning, scaffolding, validating, or packaging Claude skills inside Advanced Memory MCP.

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。

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。

Curate the project's knowledge base and memory in .unikit/memory/ — the indexed reference library of coding standards and framework usage patterns. ALWAYS use this skill whenever the user adds knowledge from a source: a URL, a file, a folder path, a PDF, an article, or a book — any path/link/document is a strong signal for the knowledge base, not a quick rule. Also use it to research or document how the project uses a framework, or to add a researched core/stack knowledge entry. Sub-commands by phrasing: "migrate the rules" or "promote rules" runs migrate-rules; "optimize the knowledge base" or "optimize the rules" runs optimise (extract large sections into references); "validate" re-syncs RULES_INDEX.md; "--module <id>" targets a module. For a quick one-line convention or correction typed as a prompt ("always do X", "never use Y", "remember this") with no source, use /unikit-rules; for architecture decisions use ARCHITECTURE.md.

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。

Orchestrate the remote rules registry — the shared registry (often connected as a local folder or git repo) that the project's rules are published to and pulled from. Three modes, picked from the user's wording: create scaffolds a new local rules registry ("create a local rules registry", "init a rules registry"); update migrates/promotes local knowledge-base rules from .unikit/memory/ into the remote registry with automatic semver bumps ("migrate local rules to the remote registry", "promote my rules to the registry", "push local rules", "publish local rules", "update the rules registry"); sync pulls registry updates back into memory ("sync the rules registry", "pull registry updates"). Pass "--module <id>" to scope to one knowledge-base module. This skill ONLY orchestrates the CLI (`unikit-ai rules *`) — it never edits .unikit.json or .unikit/memory/ sources directly.

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。

Maintain SESSION-STATE.md, the durable within-session memory that survives context compaction. Use when a constraint, settled decision, existing-infrastructure (survey) finding, or open thread must persist across a long session so it is not re-derived or re-litigated. Triggers on /state, "remember this for the session", "record this constraint/decision", or after surveying what already exists. For cross-session/personal memory use .claude/memory/ on Claude or native memories on Codex; for repo-derivable facts, do not record at all.

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。

Complete zero-dependency memory system for AI agents — file-based architecture, daily notes, long-term curation, context management, heartbeat integration, and memory hygiene. No APIs, no databases, no external tools. Works with any agent framework.

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。

Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory

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。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.

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。

JSON-canonical session memory in .meowkit/memory (Markdown is a generated view). Capture learnings, patterns, costs; loads at Orient, persists at Reflect. NOT for retrospectives (mk:retro).

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。

Capture, gate, query, and render long-term project knowledge through the gated `mewkit wiki` subsystem. Use to create a wiki, propose/approve candidates (scanner-gated), hand off a skill's terminal artifact as a scanned candidate, recall context, search the FTS index, or list pages. Agents may only PROPOSE candidates; canonical pages are written only via human approve. NOT for short-lived JSON memory (see mk:memory); NOT for fetching external sources (see mk:wiki-research); NOT for HTML snapshots (see mk:wiki-render).

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。

Search cloud memory backup when local results are insufficient. Use when memory_search returns few/no results for a topic you know was discussed before. Cloud preserves everything - even memories that were deleted or compacted locally.

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。

What you can build and do with an always-on AI agent that has episodic memory. Covers concrete product ideas, workflows, emergent capabilities from persistence plus memory, and real-world examples of deployed persistent agents. Helps you go from "I have the architecture" to "here's what it actually does for me." Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).

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。

Implement 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.

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。