2,445 件 ・ 関連度順
概要と使いどころ
Manage persistent memory across sessions. Use when asked to remember, recall, forget, or review what was stored. Covers long-term facts (MEMORY.md), daily session logs, search, and memory housekeeping.
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 the ChromaDB vector database that stores the ecosystem's persistent memory. Use when user asks to check memory storage, backup memory, search stored entries, delete entries, or reset the vector database. Do NOT use for general question answering about past sessions (use the memory skill for that).
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。
Universal Digital Immortality Engine — Distill anyone or anything you care about into an interactive digital soul. 给灵魂开个 GitHub。 Trigger whenever user wants to: - Create, distill, or awaken a Relic, digital soul, persona, or clone from chat logs, memories, or voice ("create a relic", "digital twin of my grandma", "preserve memory of pet/team", "帮我唤醒/蒸馏/做个Relic", "保存/纪念XX的记忆", "Relicを作りたい/記憶を保存"). - Talk to, chat with, or remember a loved one, pet, mentor, or companion ("talk to grandma", "chat with my cat", "跟奶奶聊天", "召唤咪咪", "おばあちゃんと話したい"). - Preserve or distill workplace history, team culture, or expert knowledge ("archive team culture", "distill business expert", "飞书/Slack协作记忆蒸馏"). Supports: Humans, Pets, Relationships, Teams, Places, Moments, Public figures, Domain experts.
Systems programming from memory management through networking. Covers memory models (stack vs heap, manual allocation, garbage collection, ownership/borrowing), concurrency (threads, mutexes, channels, async/await, actor model, data races vs race conditions), operating system concepts (processes, virtual memory, page tables, system calls, file descriptors, signals), compilation (lexing, parsing, code generation, linking, static vs dynamic libraries), networking fundamentals (TCP/IP, sockets, HTTP, DNS, TLS), and the hardware-software boundary (caches, cache lines, false sharing, memory-mapped I/O). Use when working with low-level code, diagnosing system-level bugs, understanding performance characteristics, or bridging between high-level languages and machine 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。
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. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
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。
Automatic init the first time Claude acts in an incident or alerts channel — one named like #inc-…, #incident-…, #sev0-…/#sev1-…, or whose recent messages are pages or alerts from PagerDuty, Opsgenie, incident.io, Datadog, Sentry or Grafana, or relayed by people (talk about being paged is not an alert) — that isn't already a team's oncall / monitoring channel. Run once per channel, on first contact (just invited, first @-mention, channel just created), before doing what was asked: load the oncall memory oncall setup wrote to shared workspace memory, pick the owning team's section, work out which monitoring / paging / code tools Claude can reach and which are missing, post one short zero-context message, note findings in this channel's memory, then hand off to `incident-investigate` if something was asked — or immediately, unasked, when the channel was plainly opened for a live outage. Works with no oncall memory (offers setup once). Also on "load the oncall setup here" / "init this incident".
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 MemoryBench to run repeatable conversational memory and RAG benchmarks across providers, datasets, judge models, checkpoints, and structured reports.
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。
Master memory forensics techniques including memory acquisition, process analysis, and artifact extraction using Volatility and related tools. Use when analyzing memory dumps, investigating incidents, or performing malware analysis from RAM captures.
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。
Comprehensive health check across CC auto-memory and agent-studio memory systems — detects orphans, duplicates, staleness, and threshold violations
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。
Audits and optimizes the five global Claude layers (User Preferences, Styles, Global Memory, Skills, MCP Connectors) using the Global Layer Scorecard (six dimensions, anchored 1-5 rubrics). Detects eight cross-layer failure modes and produces evolutionary recommendations (Promotion, Demotion, Codification, Skill Extraction). Use when user says "audit my global setup," "optimize my preferences," "review my Claude configuration," "check my cross-project setup," "are my preferences working," "clean up my global memory," or "what should be in my preferences vs my project." Also use when a user has 3+ Projects and wants to improve their shared foundation. Do NOT use for single-Project audits, Project Memory optimization, or full-stack audits (use rootnode-project-audit, rootnode-memory-optimization, or rootnode-full-stack-audit respectively, if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.
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. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
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。
Systematically add a new memory-system reference to Auto_Memory's reference hub. Use when analyzing a new agent memory system, adding reference/<system>/details.md, saving prompts, or updating reference/hub/technique_map.json.
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。
Bypass the Windows Antimalware Scan Interface (AMSI) using memory patching, reflection, and obfuscation techniques. Execute undetected PowerShell, VBScript, JScript, and .NET assemblies in-memory without triggering Microsoft Defender or third-party AV/EDR solutions. Use this skill during Red Team engagements when loading offensive tools (Mimikatz, Rubeus, SharpHound) in memory on defended Windows endpoints.
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。
Extracts an embedded malware configuration (C2 hosts, ports, campaign IDs, keys) from a process memory dump by locating decrypted config structures and decoding common obfuscation layers. Activates for requests to extract malware config from a memory dump, recover C2 settings from a running sample, or decode an in-memory configuration block.
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。
Rebuilds a usable PE file from a memory dump by fixing the section alignment (virtual-to-raw), correcting the entry point, and preparing for import table reconstruction. Activates for requests to rebuild a dumped PE, fix a memory-dumped executable, or convert a process dump into an analyzable file.
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。
Verify, customize, or opt out of moflo's AUTOMATIC durable-learning sharing across git worktrees / Conductor workspaces on one machine. As of the worktree-auto-sharing change this is on by default — learnings converge across a repo's worktrees with no setup. Use when the user asks "is memory shared across my worktrees?", "where are my worktree learnings stored?", "turn off worktree memory sharing", "share learnings across my separate clones", or "point my worktrees at a custom store". For a team or cross-machine (git-tracked) sharing, use /memory-team instead.
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。
JSON-canonical session memory. Use when capturing session learnings, extracting patterns, or tracking costs — persists to .meowkit/memory JSON stores; matching Markdown files are generated views. Activates during Phase 0 (Orient) to load context and Phase 6 (Reflect) to persist it. NOT for weekly-cadence engineering retrospectives (see mk:retro); NOT for arbitrary file storage.
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。
Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).
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。
You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.
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。
Knowledge graph and smart memory management using graphify + Obsidian-inspired patterns. Use when: setting up a knowledge graph, managing memory health, cross-linking notes, compiling wiki pages from scattered notes, adding structured frontmatter, or running memory health checks. Triggers on: 'knowledge graph', 'graphify', 'wiki', 'cross-link', 'memory health', 'frontmatter', 'compile notes', 'wikilinks'.
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。
Provide universal memory capabilities for different AI agents and development frameworks, unifying the capture, retrieval, and management of user, project, and task context.
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. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
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。