「synthesis」の検索結果

Discover ordinary public webpages with LLM-optimized Tavily search results. Use when no specific URL is known and the user needs a quick public-web lookup, articles, recent news, sources, or domain-filtered results. This is the default Tavily leaf for simple public-web discovery. Do not use when the user already supplied the target URL (use tavily-extract), wants URLs under one known site (tavily-map), wants many pages' content (tavily-crawl), needs a full cited synthesis (tavily-research), or needs named platforms, login state, platform-native content, or cross-platform collection (Agent Reach).

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Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.

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Produce comprehensive synthesis with explicit citations from multiple ordinary public-web sources via the Tavily CLI. Use for detailed reports, comparisons, market or literature research, and questions whose answer requires multi-source web evidence rather than a quick lookup. Do not use for simple discovery (tavily-search), one known URL (tavily-extract), one-site URL discovery or bulk extraction (tavily-map/crawl), or research centered on named social, community, career, code, media, authenticated, or cross-platform sources (Agent Reach). Takes 30-120 seconds.

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Route ordinary public-web information tasks to the correct Tavily CLI operation. Use when the task concerns publicly accessible webpages and needs web discovery, clean extraction, URL mapping, same-site crawling, or public-web-only research, but the exact Tavily operation is not yet clear or several operations must be sequenced. Prefer the specific leaf skill when intent is clear: tavily-search for discovery without a URL, tavily-extract for known URLs, tavily-map for URL/site-structure discovery, tavily-crawl for content from many pages on one site, tavily-research for cited multi-source synthesis, and tavily-dynamic-search for programmatic filtering with context isolation. Do not use for named social/community/media/career/code platforms, login or cookie-dependent content, platform-native discussions, or cross-platform collection; use Agent Reach there. Do not use for local files, Git operations, deployments, or ordinary code editing.

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创造性发散（DMN+CEN协同等效）。遇到思路瓶颈时触发，从认知体系中随机抽取「表面不相关」的跨领域概念，强制寻找结构相似性，生成非常规解法候选列表。触发词：「没有思路」「换个角度想」「帮我发散一下」「有什么不同的方向」「反常识的方案」「跨领域看这个问题」「想不到解法」。

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AI voice generation, text-to-speech, and voice synthesis via inference.sh CLI. Models: Kokoro TTS, DIA, Chatterbox, Higgs, VibeVoice for natural speech. Capabilities: multiple voices, emotions, accents, long-form narration, conversation. Use for: voiceovers, audiobooks, podcasts, video nar...

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。

Standard Operating Procedures for web research, documentation lookup, evidence synthesis with citations.

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 user interviews, observations, surveys, support tickets, reviews, or feedback into traceable themes, insights, tensions, opportunities, and research gaps; use when evidence must inform product or design decisions.

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Guidance on writing high-quality requirements: the requirement quality characteristics and the EARS (Easy Approach to Requirements Syntax) patterns — ubiquitous, event-driven, state-driven, optional-feature, unwanted-behaviour — for unambiguous, singular, verifiable requirements, plus defect/anti-pattern review and verification & traceability. Use when authoring or reviewing requirements. Original synthesis citing EARS (Mavin et al.) and NASA public-domain guidance; not a reproduction of ISO/IEC/IEEE 29148 or the EARS paper.

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Knowledge base from the FAA Human Factors Design Standard (HF-STD-001B, 2016) — the FAA's consolidated human-factors / human-systems-integration design criteria for systems it manages, operates, or maintains. Use for requirement-level design rules on: general human-factors design principles; automation and function allocation; designing equipment for maintenance; displays, controls, and visual indicators; alarms, audio, and voice communications; the computer-human interface (information presentation, coding, interaction styles, windows, dialogue); keyboards, input devices, and workstation/workplace ergonomics; system security, personnel safety, environment, anthropometry, and user documentation; plus intended-use and tailoring guidance. It is a quantified, source-attributed synthesis (heavily citing MIL-STD-1472G, MIL-HDBK-759C, DOD-HFDG-ATCCS, NUREG-0700, NASA-STD-3000, ANSI, OSHA, ISO 9241). Complements nasa-hsi as a parallel HSI tradition from the civil-aviation domain. Scope limits: it is design criteria, NOT an HSI lifecycle/process model (use nasa-hsi, nasa-npr-7123, or dau-se-guidebook for process); it does NOT reproduce the third-party standards it cites; it is FAA-acquisition-oriented (ATC, towers, TRACONs, airway facilities), not a general consumer-UX guide; and it is thin on modern touchscreen/mobile, web, and AI-driven interface patterns and on quantitative human-reliability analysis.

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Knowledge base from the FAA Systems Engineering Manual (SEM) v1.0.1. Use for civil-agency systems engineering as the FAA practices it inside the Acquisition Management System (AMS) and the National Airspace System (NAS): the five-phase AMS lifecycle and its decision points; operational concept development and implementation-free functional analysis (ConOps, FFBD/N², CMLs); requirements analysis and architectural design synthesis (PRS→MRS, the PRD ratchet pPRD→iPRD→fPRD, RAM, ISEF/DoDAF views, CPRs); the seven technical management disciplines (Integrated Technical Management/SEMP, Interface Management/IRD-ICD, Risk-Issue-Opportunity, Configuration Management, SE Information Management, Decision Analysis, Verification & Validation); and the specialty disciplines (RMA via Service Threads/STLSC, Life Cycle Engineering, E3/Spectrum, Human Factors, Information Security via the NIST RMF, System Safety via the SMS, and Hazardous Materials/Environmental/EOSH). Scope limits: this is FAA-specific guidance, not policy — it is a tailorable menu that points outward to companion handbooks (FAA-HDBK-006A for RMA, the SMS Manual, the NIST 800-series) rather than restating generic SE theory; it is thin on detailed cost estimating, contracting/FAR law, software-development methodology, named anti-pattern catalogues, and any non-FAA or non-civil-aviation lifecycle. Source has no page numbers in the extracted text (~360 pp.).

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Komponiert video-synchronisierte Hintergrundmusik aus einem Storyline-JSON per lokaler Waveform-Synthese (numpy + ffmpeg). Stile: Chiptune, Ambient, Electronic. Deterministisch, offline, kein Cloud-Dienst.

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Summarises one sprint from the board export, sprint report and review or stand-up notes the user provides into a one-page DRAFT sprint review summary: the sprint goal as written, items delivered, items not delivered with the reasons the sources state, carry-over, impediments, metrics exactly as given, and numbered questions for the retrospective, every row referenced to a work item or note fragment and every missing fact marked UNKNOWN. Use when the user asks to "summarise the sprint", "write the sprint review", "what did we deliver this sprint", "list the carry-over and blockers" or "draft the sprint recap". Do not use for the retrospective write-up or themed lessons across several sprints, use lessons-learned-synthesis instead; for a weekly project status report, use project-status-tracker. Drafts for human review; never approves, authorises or signs off.

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Builds a DRAFT course outline from a training need and audience: learning objectives with an observable verb, condition and standard, modules in a stated order with a duration basis and sources, activities and an assessment plan aligned objective by objective, and a coverage table showing what each objective still lacks. Never sets a pass mark, declares learners competent or invents content. Use when the user asks to "build a course outline", "design a training programme for", "turn this need into modules and objectives", "draft the curriculum for this workshop" or "what should this training cover". Do not use for quiz items from finished content, use training-quiz-builder instead; for needs from surveys and manager notes, use training-needs-synthesis; for a new starter's first weeks, use onboarding-plan-builder. Drafts for human review; never approves, authorises or signs off.

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Drafts a blameless incident postmortem (summary, impact, timeline, detection and response, contributing factors as candidates for review, action items) from an incident channel export, ticket notes and any alert or change records, with UNKNOWN for every fact the inputs do not contain. Never assigns blame, names a root cause or re-rates severity. Use when the user asks to "write the postmortem for this incident", "build the incident timeline from the chat export", "draft the post-incident review" or "turn this ticket and channel log into an incident report". Do not use for a knowledge article from the resolved ticket, use knowledge-article-drafter instead; for project retrospectives, use lessons-learned-synthesis; for data-breach impact and notification questions, use data-incident-impact-brief. Drafts for human review; never approves, authorises or signs off.

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AI-first web search via Exa — semantic/neural ranking, category-scoped results, fresh content retrieval, and one-shot answer synthesis with citations. Use when the user needs current web information better ranked than general web search, or a grounded answer with sources.

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Turn any hypothesis, however fringe, into an argument map rather than a verdict: five independent lenses (consensus, skeptic, frontier, historian, experimental design), then a synthesis of for and against, evidence quality, a confidence tier and the cheapest decisive experiment. Triggers: "/research-swarm <hypothesis>", "map the arguments on X".

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 repeated themes from selected research conversations or notes.

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Simulate a Nature-style reviewer assessment from the referee perspective rather than an author rebuttal. Use when the user wants a pre-submission review, reviewer report, peer-review style critique, novelty/significance/technical soundness assessment, reviewer-style manuscript evaluation, 审稿人视角评估, 预审稿意见, or Nature reviewer report. Return 3 reviewer reports plus a cross-review synthesis, grounded only in the local Nature reviewer source basis. Also trigger on general pre-submission review requests during academic writing even without the word "Nature", such as getting a mock peer review for any journal, critiquing a draft as a reviewer would, assessing novelty/rigor before submission, and Chinese phrasings like 审稿人视角、模拟审稿、预审、帮我审一下论文、投稿前自审、审稿意见模拟、找论文问题.

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完成した差分・PR・検証証拠に残る Unknown（未確認の前提・調査されていない影響・ 不足している証拠）を横断合成する evidence-sufficiency のメタ観点。個別 defect の 検出は既存 skill へ委譲し、本 skill は「そのリスク種別を調査した証拠が残っているか」 の meta 評価のみを行う。通常は finding verification 後の generic 合成ステップとして、 明示的 Security Audit では SecurityAuditCoverage 専用 profile として report-only で実行する。 残存 Unknown は既存 Unknown Coverage 構造へ出力し、新しい語彙・schema は作らない。

Turn the current conversation into a spec and publish it to the project issue tracker: no interview, just synthesis of what you've already discussed.

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Coordinate FriendlyBet company-level strategy, CEO handoff, department orchestration, HR/agent-quality escalation, operating-model decisions, multi-agent synthesis, roadmap tradeoffs, decision rights, quality gates, and updates to the virtual company system. Use when Eyal asks to involve the company, CEO, leadership, strategy, operations, several departments, or named executive agents such as Strategy Director, Chief of Staff, or Operating System Lead.

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Use when working with Cdktf — cDK for Terraform (CDKTF) project and stack management. Covers project synthesis, provider generation, stack deployment, output inspection, and diff analysis using TypeScript, Python, Go, Java, or C#. Use when working with CDKTF projects, synthesizing Terraform configurations, deploying stacks, or debugging provider bindings.

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Run Tavily's multi-source research workflow for comparisons, market analysis, literature-oriented exploration, or detailed cited reports. Use only when bounded search and extraction are insufficient.

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。