「arc」の検索結果

Apply source-aware mechanical-engineering judgment to research, analysis, coding, writing, teaching, research identity, and release work. Use for thermal-fluid systems, heat transfer, fluid mechanics, thermodynamics, HVAC, energy systems, turbomachinery, piping, multiphase flow, experiments, correlations, CFD, reduced-order models, AI/ML, uncertainty, engineering datasets, literature reviews, citations, manuscripts, reviewer revisions, Overleaf packages, figures, proposals, research software, reproducibility, public releases, engineering teaching materials, or a research-project logo and visual identity.

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Deep research on technical topics using EXA tools with intelligent two-tier caching. Use when user asks to research a topic, investigate best practices, look up information, find patterns, or explore architectures. Also invoked by /research command. Triggers: "research", "look up", "investigate", "deep dive", "find information about", "what are best practices for", "how do others implement".

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Use when designing new high-level system architecture, reviewing existing designs, or making architectural decisions. Invoke to create architecture diagrams, write Architecture Decision Records (ADRs), evaluate technology trade-offs, design component interactions, and plan for scalability. Use for system design, architecture review, microservices structuring, ADR authoring, scalability planning, and infrastructure pattern selection — distinct from code-level design patterns or database-only design tasks.

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Use this skill when designing a product catalog taxonomy, planning attribute strategy, or determining search and navigation structure for a B2B or B2C/D2C Commerce store. Triggers on: taxonomy design questions, attribute planning, search index strategy, category navigation structure, or merchandising hierarchy decisions. NOT for wiring the catalog itself — WebStoreCatalog, entitlement policies, product assignment — use admin/commerce-product-catalog. NOT for configuring searchable or facetable attributes, sort rules and index rebuilds — use apex/commerce-search-customization.

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Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.

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SEO & GEO (Generative Engine Optimization) for websites. Analyze keywords, generate schema markup, optimize for AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude) and traditional search (Google, Bing). Use when user wants to improve search visibility, search optimization, search ranking, AI visibility, ChatGPT ranking, Google AI Overview, indexing, JSON-LD, meta tags, or keyword research.

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Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use this skill when designing clean architecture for a new microservice, when refactoring a monolith to use bounded contexts, when implementing hexagonal or onion architecture patterns, or when debugging dependency cycles between application layers.

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Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.

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Use this skill when users need to search academic papers, download research documents, extract citations, or gather scholarly information. Triggers include: requests to "find papers on", "search research about", "download academic articles", "get citations for", or any request involving academic databases like arXiv, PubMed, Semantic Scholar, or Google Scholar. Also use for literature reviews, bibliography generation, and research discovery. Requires OpenClawCLI installation from clawhub.ai.

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决策调研 / Decision-Driven Research：当用户面对一个具体决策需要找信息时使用——「有没有现成方案」 「怎么接入 X 平台」「这个技术可行吗」「业界怎么做 Y」「选 A 还是 B」「桌面端应该怎么定位」 「高级版和基础版怎么拉开差异」「这个产品方向对不对」。 核心行为：先框定研究层级和问题类型，再锚定决策问题，枚举竞争假设，主动找反对证据， 用排除逻辑给出有立场的结论。支持技术选型、产品策略、商业判断、竞品定位等所有需要做决定的调研。 可用中文唤起：「帮我调研」「有没有现成方案」「这个怎么接」「技术上可行吗」「帮我选一个」 「这个产品方向对不对」「我们应该怎么定位」「行业怎么做」。 与 research-topic-compiler 的边界：需要最终选择、推荐、排除理由、置信度和颠覆条件时用这个； 如果目标是长期学习、候选池沉淀或 Research Project，用 research-topic-compiler，再把 Candidate Backlog 交回本 Skill 做最终决策。 只有发现一个会实质改变结论、可研究且可关闭的证据 gap 时，才向 research-topic-compiler 发出精确 Return Request。 不用于：问题还没定义清楚时（先用 ai-collaboration-calibration L4-fuzzy 脑暴）。

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Use when a decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).

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[Architecture] Use when auditing the ENTIRE project architecture and production readiness in one pass — bundles architecture-review + architecture-scalability-review + production-readiness-review at project or diff scope, then synthesizes one consolidated Architecture Health Report.

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Europe PMC RESTful Web Service 多源文献聚合检索原子 skill。**使用本 skill 进行医学文献广召回 / RAG 知识库构建 / 跨库聚合 / 预印本扫描 / 全文链接发现**。一次调用同时召回 PubMed (MED) + PMC + AGRICOLA + CABI + 预印本(bioRxiv / medRxiv / ChemRxiv / Research Square)+ NCBI Bookshelf + 专利,自动按 PMID/PMCID/DOI 三级去重,返回完整元数据(全文 OA 链接、citations、grants 资助信息)。**定位:广召回 · 多源 · RAG 友好**(与 pubmed-eutils 互补 — 后者做单源精准官方检索 + Clinical Queries;本 skill 做"撒网"式多源聚合)。触发词:Europe PMC / EuropePMC / 多源文献聚合 / 广召回 / RAG 召回 / RAG 知识库 / 预印本搜索 / preprint search / bioRxiv / medRxiv / ChemRxiv / DOI 检索 / DOI lookup / 全文链接 / full-text links / OA 全文 / PMCID / 灰色文献 / grey literature / 跨库去重 / 资助检索 / grant search / NIH funded / Wellcome funded / citations metadata。**不做**:NCBI E-utilities 直检(→ pubmed-eutils)、临床试验注册(→ clinical-trials-v2)、全文 XML 抽取(→ bioc-fulltext-fetch)、实体识别(→ pubtator-entity-search)、证据分级(→ medical-evidence-grading)、参考文献终态写入(→ evidence-appendix-sync)。

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Find niches, topics, audiences, products, channels, and content angles via deep web search. Use whenever Ahmad says "find me a niche", "hunt niches in X", "what's an underserved audience for Y", "find similar sites/videos to <example>", "research the X market", "what are people building in Y space", "/niche-hunter X", or any phrasing that asks for niche / market / topic / audience / product / channel discovery across the open web. Trusts domain + keyword search; chains web_search → web_fetch / web_extract / web_crawl + reddit_search + search_youtube + github_repo_intel. NEVER uses awesome-list shortcuts. Output shape is FLEXIBLE — list, table, report, suggestions, or normal prose — picked from how Ahmad phrased the ask.

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Master software architect specializing in modern architecture patterns, clean architecture, microservices, event-driven systems, and DDD. Reviews system designs and code changes for architectural integrity, scalability, and maintainability. Use PROACTIVELY for architectural decisions.

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Decision protocol for strategic work: recall the knowledge vault, run a gap analysis, emit a deep-research prompt for external tools, then synthesize the returned reports into a decision memo. Use before any new product, feature, market, pricing, GTM or architecture call, where local recall alone is not enough. Triggers: "/alfa-search-recall-deepresearch", "alpha protocol", "R+DR".

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Perform quick web searches, local business lookups, and URL content extraction. Covers web_search for general queries, brave_local_search for location-based discovery, and run_crawl4ai_job for deep page crawling. Use when fact-checking, trend spotting, doing competitor analysis, looking up local businesses, extracting page content, or performing content research that doesn't need multi-step deep research.

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Perform comprehensive, multi-source research on any topic using AI-powered web search and analysis. Covers deep_research for broad discovery and semantic_content_search for site-specific knowledge retrieval. Use when you need to research brands, markets, competitors, or trends with citations — before creating content, planning campaigns, or making strategic decisions.

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Use when the user asks to "design our change narrative", "build the old-world-to-new-game story arc", or "frame the shift our category is undergoing"; produces a Raskin-style strategic narrative arc — old world → the undeniable shift → winners vs losers under the new rules → the promised land → proof the promised land is real — derived from the positioning truth set and the belief/objection map, feeding the TALE Architecture arc sub-item. Every proof beat is labeled Measured / User-provided / [needs source] and this skill never adjudicates a claim. Not for the message house hierarchy — use message-system-architect; not for the pitch deck narrative — use pitch-narrative-builder; not for claim substantiation — use offer-claims-registry. 变革叙事弧/旧世界/应许之地/战略叙事结构

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Search academic references for a manuscript and export them to a verified refs CSV, then optionally hand off to cec-sheet. Uses Codex CLI as the search driver, but Codex calls a bundled deterministic PubMed tool (scripts/pubmed_search.py) instead of writing its own E-utilities code — bibliography is fetched live from PubMed so PMIDs are never fabricated. Triggers: "論文検索", "文献検索", "文脈検索", "コンテキスト検索", "原稿の引用を探して", "refs.csv", "codex-refs", "/codex-refs".

Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use this skill when designing clean architecture for a new microservice, when refactoring a monolith to use bounded contexts, when implementing hexagonal or onion architecture patterns, or when debugging dependency cycles between application layers.

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Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.

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Search the documents uploaded for the CURRENT task/row (e.g. an Agentic Table row's attached files) via the `unique-cli uploaded-search "<query>"` command, with the same per-turn citation tracking as `unique-cli search` so cited facts render as `<sup>N</sup>` footnotes and clickable reference chips on the Unique platform. ALWAYS use this skill when the user refers to documents they uploaded/attached to this task, or when you need facts from the task's own attached files. These uploaded files are scoped to the chat, NOT to a knowledge-base folder, so they will NEVER appear in `unique-cli search` results no matter the folder or metadata filters. Use `unique-cli search` for the knowledge base and this command for the task's uploaded documents; the two are complementary and citation numbering is shared across them within a turn.

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Search the Unique AI Platform knowledge base using the unique-cli search command, with automatic per-turn citation tracking so cited facts render as clickable reference chips and `<sup>N</sup>` footnotes on the Unique platform. Use whenever the user asks to find, search, or query documents or content on Unique, including filtering by folder or metadata. Also covers `unique-cli read <cont_id>` for reading the full indexed text of a document when its content ID is already known. NOTE: This search uses combined vector + full-text indexing. Excel (.xlsx/.xls), CSV (.csv), and image files are NOT full-text indexed, so they will not appear in search results. To locate these file types, use the unique-cli-file-management skill instead (browse folders with `unique-cli ls` to find them by name).

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。