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概要と使いどころ
Expert guide for WebAssembly (WASM) and Edge Computing. Covers WASI preview 2, Spin/Fermyon, Cloudflare Workers WASM, and high-performance browser computing / Panduan ahli untuk WebAssembly (WASM) dan Edge Computing. Mencakup WASI preview 2, Spin/Fermyon, Cloudflare Workers WASM, dan komputasi performa tinggi di browser.
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Expert guide for Local Small Language Models (SLMs) and Edge AI execution — WebLLM, Transformers.js v3, ONNX Runtime Web, WebGPU, and zero-latency local embeddings / Panduan ahli SLM lokal dan AI edge di browser.
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Expert guide for Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop.
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AI 口播配音：用免费的 edge-tts（微软 Edge 在线语音）为带货短视频逐句生成配音，自动去首尾静音、统计每句时长、缓存复用，可选音色/语速/音调，并给出口播稿改写规则。Use when the user needs a voice-over, 配音, 口播, 旁白, TTS, text-to-speech, AI 主播声音, or wants to turn a script into audio for 抖音 / TikTok / 视频号 videos.
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Manages the research/ knowledge base of verified tool and library research entries via knowledge-explorer.py. Commands — list (browse by category or SDLC layer), fetch-github (draft entry from GitHub repo), add (validate and route to category dir), update-append (append dated revision), generate-descriptions (repair missing or low-quality descriptions), migrate (convert inline-header entries to frontmatter). Use when browsing KB topics, adding or updating research entries, fetching GitHub repo metadata, generating descriptions in parallel via Haiku subagents, or migrating old-format entries to skill-spec frontmatter.
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 knowledge synthesizer specializing in extracting insights from multi-agent interactions, identifying patterns, and building collective intelligence. Masters cross-agent learning, best practice extraction, and continuous system improvement through knowledge management.
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Use when the user brings an external source worth keeping — a URL, a paper, a tweet/thread, a blog post, a docs page — and wants it pulled into the second brain. Fetches the source into the git-ignored capture inbox, admits it to the committed wiki through the admission policy, then refreshes the knowledge graph so it joins the rest of your thinking. Trigger on "/graph-ingest", "ingest this url", "add this paper to my knowledge", "add this tweet/page to my knowledge", "capture this source".
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。
Maintainer-only. Use when re-vendoring vendor-skills/BMAD/ against a newer BMAD-METHOD commit — "update BMAD", "re-vendor BMAD", "bump the BMAD pin". Not part of the Hedgehog discipline a consuming project copies; this only applies to the Hedgehog repo itself.
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Builds currency hedging models with rolling forward programs, option-based strategies, and cross-hedge analysis for international portfolios. Use when designing hedge programs, analyzing hedge ratios, or evaluating FX protection costs.
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Structures interest rate hedging programs with swap analysis, cap/floor evaluation, and hedge accounting documentation. Use when designing rate hedges, comparing hedging instruments, or analyzing hedge effectiveness.
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Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running agents.
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Initialise a project for knowledge base use. Appends the [Knowledge Base] section to the project's CLAUDE.md, creates the library/ directory structure, optionally seeds with three example library files from the Agentic SDLC research, and reports next steps. Run once after installing sdlc-knowledge-base.
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Rebalances context across Memory, Custom Instructions, knowledge files, and User Preferences in Claude Projects. Audits Memory for redundancy, staleness, and misplacement; prescribes optimization including Codification of stable Memory patterns into explicit User Preferences or Project CI rules. Use when user says "optimize my memory," "what should be in my memory," "trim my knowledge files," "reduce my context usage," "rebalance my project," "what should be in my preferences vs memory," or asks whether something belongs in Memory, a knowledge file, or User Preferences. Also trigger on symptom-phrased: "my project feels bloated," "Claude keeps forgetting things," "my context window keeps hitting limits." Activate whenever Memory-layer balance is the primary concern. Do NOT use for full Project audits (use rootnode-project-audit if available), single-prompt evaluation (use rootnode-prompt-validation if available), or global-layer audits that don't touch Project Memory (use rootnode-global-audit if available).
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Analyzes Claude Project context budget under the automatic-RAG-by-window model: knowledge files vs. threshold-exempt overhead (Skills, MCPs, CI, Memory). Two modes: Quick Diagnostic and Full Budget Audit. Use when user says "check my context budget," "how much context am I using," "is my project too big," "optimize my token usage," "tier my files," "optimize for RAG," "improve retrieval quality," "should I keep compressing," "am I over-compressing," "should I accept RAG mode." Also trigger on context pressure symptoms: "Claude forgets my instructions," "responses getting generic," "content not found in my knowledge files." Also use when a project audit scores Knowledge Architecture ≤ 3. Do NOT use for content placement decisions (use rootnode-memory-optimization if available), full project audits (use rootnode-project-audit if available), or behavioral tuning (use rootnode-behavioral-tuning if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.
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Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.
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How to write a Grafema batch-mode plugin that reads the graph and filesystem, then writes new edges/nodes directly to RFDB. Use when: (1) need to add project-specific edges that generic analyzers can't produce, (2) need to read config files (JSON/YAML) and trace values into code, (3) implementing framework-specific resolvers (Django settings, Express routes, pipeline configs). Covers: grafema.config.yaml plugin config, RFDB field naming (src/dst NOT source/target), addEdges/addNodes API, RFDBServerBackend connection pattern.
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 research artifacts to a vault or knowledge base, formatting source citations, saving synthesized connections from a writing project, or enriching a knowledge base with produced content.
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Efficiently accumulate and retrieve competition knowledge in the knowledge/ stock layer (atomic pages + INDEX). Distill reusable insights from the daily-report flow and promote them, deduplicating and keeping things current. Use when an insight emerges, for weekly cleanup, or to recall past knowledge.
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 AI knowledge management systems. TRIGGERS - Use when user needs help with ai-knowledge-management related 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。
Build AI knowledge base creation systems. TRIGGERS - Use when user needs help with ai-knowledge-base-builder related 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。
Deploy AI models to edge devices. TRIGGERS - Use when user needs help with ai-edge-deployment related 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。
Makers 开发专家团部署 Skill — 将前端/全栈项目部署到 EdgeOne Makers。 包含 CLI 安装、登录鉴权、一键部署、JSON 输出解析、URL 铁律。 触发词：部署、发布、上线、发版、deploy、publish、go live、 重新部署、deploy to EdgeOne。 当执行 edgeone makers deploy 命令时必须加载此 skill。
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Makers 开发专家团主理人 Skill — 协调前端、后端、AI Agent 三位专业成员， 在 EdgeOne Makers 平台上完成 Web 全栈开发与部署任务。 触发词：全栈开发、搭建并部署、帮我做一个 Web 应用、EdgeOne Makers 项目、 在 EdgeOne 上开发、一站式交付。
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 knowledge graph for autonomous coding. Use when storing relationships, querying connected knowledge, building project understanding, or maintaining semantic 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。