811 件 ・ 関連度順
概要と使いどころ
Makes the agent reconsider a task it just handed back to the human and find a way to do it itself, checking memory, CLI tools, skills, MCP servers, and documentation in turn. Use when the user invokes `/you` or `$you`, or replies that the agent should do something itself rather than asking 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。
Capture, gate, and query long-term project knowledge via the gated wiki subsystem: propose candidates, recall, or search; agents propose, humans approve. NOT for short-lived memory (mk: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。
Runtime router for context decisions during a task — decide read-more / stop / ask / assume / delegate, pick a minimal read-set, and time compaction. Front-page mental model is Write / Select / Compress / Isolate; a lazy pattern index routes to 25 context-engineering patterns, one reference per decision. Use when context is near budget, a long session is degrading, you must choose what to read for a task, or you are deciding /compact vs /clear vs a sub-agent. NOT for structural .claude/ overhead audits (see mk:context-audit); NOT for owning persistence (see mk:memory); NOT for USD cost (see mk:budget).
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。
Store and semantically search agent memory with ONNX vector embeddings on a ruOS desktop, using the Ruflo CLI. Use to persist patterns/decisions across sessions and recall them by meaning rather than exact key.
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。
Look up how Claude Code itself works — hooks, skills, subagents, MCP, plugins, settings — from the official docs, so answers come from the current spec instead of 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。
Expert-level computer architecture covering CPU design, instruction sets, pipelining, cache hierarchy, memory systems, parallel architectures, and GPU computing.
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 find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.
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。
Sync reusable frontend decisions into persistent project documentation after UI work. Use when frontend changes introduce or modify component APIs, variants, design-system tokens, styling conventions, layout behavior, accessibility rules, interaction patterns, responsive behavior, or agent operating instructions; when the user asks to reflect decisions in components.md, design system docs, AGENTS.md, CLAUDE.md, GEMINI.md, or similar project memory files; or before finalizing frontend work where future consistency depends on decisions made during implementation.
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。
Um sistema híbrido de memória que oferece gerenciamento de conhecimento persistente e pesquisável para agentes de IA (Arquitetura, Padrões, Decisões).
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。
Semantic audit of the IDD instruction corpus for leaked session context, cross-file contradictions, fresh-memory completability gaps, automation blockers, and restatement-discipline drift. Use on request to audit .github/instructions, the issue-authoring skill bundle, and the installed agent entry files (CLAUDE.md, AGENTS.md, GEMINI.md, .github/copilot-instructions.md). Read-only — never edits files or mutates issues.
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。
Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, implementing the dialectic chat endpoint for AI agents, or wiring Honcho into bot frameworks (nanobot, openclaw, picoclaw, etc).
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 SolanaOS agent skill — install, configure, and operate the autonomous Solana trading runtime with Honcho v3 epistemological memory, multi-venue perp trading (Hyperliquid + Aster), on-chain intelligence with USD pricing, Telegram bot, gateway API, Tailscale mesh, hardware integration, and cross-session recall. Use when asked to install SolanaOS, query Solana blockchain data, manage wallets, run OODA trading loops, configure strategies, control BitAxe mining fleets, pair Seeker devices, or operate any SolanaOS runtime surface.
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 capturing or restoring a learner's persistent profile to personalize teaching 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。
Collective research memory for AI coding agents — search prior research before hitting the web, save findings back to the shared cache, view karma stats. Use when the user asks a library/API/framework question, before running WebSearch/WebFetch, or after deep web research so the next agent doesn't repay the cost. Talks directly to the hosted MCP endpoint via JSON-RPC — no local MCP server required.
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。
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple 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。
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
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。
Control FriendlyBet operating cost, free-tier viability, provider/API cost, GitHub Actions/Supabase/Vercel usage, documentation hygiene, company memory updates, playbook maintenance, and skill validation. Use when evaluating cost, operational burden, tooling, internal docs, company memory, or whether agent profiles should become full skills.
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 the agent needs recursive reasoning, episodic memory retrieval, internet learning, or world-model prediction. Mini-OpenAmer: the 2B hybrid Mamba core with 9 tools, running 24/7 on Damir's laptop.
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。
Digital forensics v3 — disk imaging, memory analysis, network forensics, chain of custody, legal
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。
Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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。
Use when: the agent captures a correction from the team lead, hits an unexpected error, discovers a capability gap, or finds a better practice. Logs to .learnings/ and promotes durable patterns to permanent project 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。
Cognitive systems skill for reasoning, memory, and agent behavior; use when designing agent workflows or mental-model tooling.
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。
Automated CLI-based parallel agent execution — spawn subagents via Gemini CLI, coordinate through MCP Memory, monitor progress, and run verification
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。