811 件 ・ 関連度順
概要と使いどころ
Use when the user asks Codex to read or write Odysseus data (todos, email, calendar, memory, documents) or to launch/monitor/stop a Cookbook model-serve task through the scoped Codex Agent API. Requires ODYSSEUS_URL and ODYSSEUS_API_TOKEN.
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 user asks Claude Code to read or write Odysseus data (todos, email, calendar, memory, documents) or to launch/monitor/stop a Cookbook model-serve task through the scoped Claude Agent API. Requires ODYSSEUS_URL and ODYSSEUS_API_TOKEN.
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 RVF (Ruflo Vector Format) files for portable agent memory and cross-platform transfer
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。
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent 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。
Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.
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。
Bring what Claude Code or Codex already remembers or has configured for this user into Muse Code — their memory notes and their MCP servers — whenever the user mentions things Claude Code or Codex remembers or knows about them, an MCP tool or server they had there that is missing here, or asks to migrate, import, or copy that setup; call read_skill for bundled:migrate before reading any foreign file. Rules and skills are already covered by /rules import and `muse skills import --from claude|codex`; point there. Do NOT use for session transcripts (resume-claude or resume-codex for Claude Code/Codex continuation, including recovery of unfinished work with or without a handle; import for explicit /import requests or continuation from other agents or unnamed artifacts), for Muse settings unrelated to migration (manage-settings), or when Claude Code or Codex is only mentioned in passing.
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。
Handle explicit /import requests for read-only transcript recovery and a resume checkpoint, or continue work from other coding agents and unnamed artifacts. For Claude Code or Codex continuation, including recovery of unfinished work with or without a session ID or path, use resume-claude or resume-codex. For memory notes or MCP servers, use migrate.
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 past conversations in the agent's history log.
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。
Detects host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable.
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。
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.
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。
Route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.
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 for authorized security assessment of LLM applications and AI agents, including prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.
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。
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
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。
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
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。
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
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。
Retrieve and apply OpenViking Experience memories through the Agent runtime's generic OpenViking search and read tools. Use before or during executable, multi-step, or tool-based work such as coding, file or data changes, configuration, deployment, workflow execution, and failure recovery when prior operational guidance could improve reliability. Do not use for casual chat or simple factual questions.
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 for any CopilotKit question — adding it to an app, chat UI, frontend or server tools, generative UI, shared state, human-in-the-loop, agent frameworks (LangGraph, CrewAI, Mastra, ADK, PydanticAI, and others), the runtime, Intelligence, threads, voice, or diagnosing something that is not working. Do not answer from memory: this skill exists to point you at the current documentation and source, both of which you can read.
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。
Turn your latest Granola call into next steps (owner, team, due date, and the Linear issue that already tracks each one) and post them to Slack, using cognee memory of your calls, Linear issues and Gmail inbox. Use when someone asks for the next steps or action items of their latest call. Runs locally; reads Gmail read-only; posts to Slack only when Slack is set up.
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 working with cognee's session memory or improve() — storing conversation turns, agent traces and feedback with session_id, bridging sessions into the permanent graph, reading an ImproveResult, understanding why an improve stage was skipped, already_completed or lock_held, or tuning the IMPROVE_* settings.
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。
Add persistent graph-based memory via mnemon. Agents recall past context before responding and remember insights after each turn.
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。
Add keyless GBrain memory to an existing agent, or connect a hosted brain while preserving identity and configuration
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。
End-to-end discipline for turning any large data source (audio libraries, email takeouts, document corpora, chat exports, API dumps) into brain pages at scale. The lifecycle spine: SCHEMA → ACCESS → TRIAL → EVALUATE → IMPROVE → CODIFY → TEST → SKILLIFY → BULK → MONITOR. State is tracked in a durable JSON manifest (see MANIFEST-PATTERN.md) so any crash, session boundary, or subagent fan-out resumes from ground truth 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。
Show what happened in recent past sessions on this project as a clean timeline. Use when the user asks "what did we do last time", "session history", "past sessions", or wants an overview of previous work.
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。
Save a correction or hard-won rule as a confidence-weighted lesson that resurfaces before similar work. Use when the user corrects your approach, says "learn this", "always" or "never do X", or you notice yourself repeating a past mistake.
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。