「memory」の検索結果

Performance profiling, benchmarking, and optimization. Use when: slow operations, regressions, memory pressure, release validation. Skip when: early prototyping, documentation, configuration-only changes.

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。

SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting

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 the SPARC Specification phase — gather requirements, define acceptance criteria, identify constraints, and store the spec in 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。

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。

Orchestrate multi-phase deep research with web search, memory retrieval, pattern matching, and synthesis into structured findings

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。

Build a graph-structured dossier on a seed entity via parallel fan-out + recursive expansion across web, memory, knowledge-graph, codebase, ADR index, and git intel

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 `@metaharness/darwin evolve {repo}` to mutate a harness's seven policy surfaces (planner/contextBuilder/reviewer/retryPolicy/toolPolicy/memoryPolicy/scorePolicy), sandbox-score each variant, and promote only measured wins. The model is frozen; the harness evolves. Closes the loop ADR-150 opens (score+genome describe; evolve changes). Degrades gracefully when @metaharness/darwin is absent (ADR-150 + ADR-153 architectural constraints).

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。

Synthesize research findings from memory into structured reports with evidence grading, contradiction resolution, and actionable recommendations

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。

Composite Phase-2 audit worker (ADR-150). Bundles harness oia-manifest + threat-model + mcp-scan into one timestamped audit record stored in the `metaharness-audit` memory namespace. Designed for cron-scheduled drift detection.

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。

5-dimension harness readiness scorecard from `metaharness score {path}`. Returns harnessFit / compileConfidence / taskCoverage / toolSafety / memoryUsefulness + estCostPerRunUsd + scaffoldReady. Pure-read; subprocess invocation; degrades gracefully when MetaHarness is absent (ADR-150 architectural constraint).

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。

Stores approved context for one AI team and searches it later, with provenance. Use when the user asks to remember a decision, constraint, or fact for the team, or to recall what the team already knows. Memory is separate for each team and tenant.

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。

Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

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。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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。

Ruflo is a multi-agent orchestration platform for AI coding agents (Claude Code, Cursor, Codex, Copilot, Gemini, Amp, +12 more). Use this skill when the user wants to (1) install/init ruflo in a project, (2) run multi-agent swarms with hierarchical coordination, (3) use ruflo's 314+ MCP tools for memory, routing, hooks, sub-agents, or workflows, (4) check ruflo status/version/doctor health, or (5) discover which of ruflo's 30+ plugins fits their task.

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 coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development 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。

Vector search via embeddings_* (large-scale HNSW) and ruvllm_hnsw_* (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction

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 reviewing navigation performance, browser lifecycle events, or resume-from-memory behavior. Check the actual browser lifecycle and restore path, not only static code 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。

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.

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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。

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。

Create, write, or manage Meta ads and assets: ad copy, campaigns, spend, reports, audiences, catalogs, product feeds, feed refresh schedules, experiments, and policy. Always load for any request to create or write an ad, or to advertise a product or service, even when no platform is named; this includes sensitive or restricted categories. Always load for any question asking what a Meta, Facebook, or Instagram advertising policy means, allows, prohibits, or requires, including a standalone policy-definition question with no account context. Those questions must use ads_policy_tool, never browser search or memory. Load for a specifically named catalog or feed with an upload or refresh-schedule request; use Ads reads to resolve ownership before writing. Generic unnamed feeds need context. Whether an image, claim or piece of copy may be used in an ad is ALWAYS a Meta Ads task — 'can I use this in an ad', 'is it allowed', 'is this against policy', and any rights, likeness, celebrity, logo or trademark question about advertising with an image, including a follow-up about one just generated. Those are ads-policy questions, not general legal ones. An ad request uses the campaign workflow unless explicitly only an image or organic post.

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 questions about a specific flight's departure or arrival time, including "when's my flight?" and confirmation of remembered times, plus flight status, delays, and cancellations. Verify the exact dated flight before answering; memory identifies the itinerary but does not verify its current schedule. Use FlightAware to monitor operational changes for an upcoming booked flight when the user directly asks for ongoing monitoring.

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。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the 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。