調査、論文、教育、資格学習（4,131 件）
概要と使いどころ
Researches a company from its URL and produces a Brand DNA file covering positioning, audience, competitors, voice, and messaging. Use when starting work with a new brand or customer.
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- Investigator OPSEC — threat-model who might notice you, control your attribution surface across IP, ASN, browser and TLS fingerprint, timing and logged-in accounts, separate research identity from… Use when setting up a research account, avoiding tipping off a subject, worrying about LinkedIn profile-view leakage, needing a burner phone or email, or hardening a research VM or browser profile
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Send one research question to as many independent LLMs as possible at once, each in its strongest reasoning mode with proof of that mode, then synthesize. Floor of 3 models, quorum of 4; late answers are collected later. Use for high-stakes questions where one model's view is not enough. Triggers: /hyper, hyper research, ask every LLM, max fan-out research.
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- Build a sourced, corroborated profile of a named individual from public records, social platforms, professional networks, court and property filings, licensing boards, patents, papers and obituaries — anchoring the name to a… Use when asked to find, identify, background-check or profile a person, verify someone's claimed employment or credentials, or locate a missing or hard-to-reach individual
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Researches what prompts people ask AI engines (ChatGPT, Gemini, Perplexity, Claude) about a product category and produces a prompts.csv artifact — a prioritized, strictly-schema'd list of the queries where the brand should be cited. Feeds the monitor workflow.
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ДР-ПОЛОСА — превратить наши дипресёрчи в 10 тизеров в день и разослать их по каналам лаборатории. Триггеры “/dr-teasers“, “/dr-lane“, “тизеры по ресёрчам“, “разошли дипресёрчи“, “10 тизеров“, “полоса дипресёрчей“, “post research teasers“, а также САМА ежедневная рутина “Claude DR Teasers Daily“
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Finds free backlink and brand mention opportunities across Hacker News, Quora, GitHub, directories, and niche communities. Outputs a prioritized action plan with draft responses ready to post. Use after brand research and content creation to amplify reach and AI engine citations.
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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".
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。
アーキテクチャ知識グラフ(memory MCP / memory.jsonl)の初期作成。「知識グラフを初期化して」「memory グラフを作って」「アーキテクチャグラフを登録して」といった依頼、またはプロジェクト初期構築完了後の仕上げとして使用する。コードベースを調査し、モジュール構成と依存関係を entities / relations として登録する。
Research any open-source repository end to end and answer the core question — SHOULD YOU contribute to it? — with a GO / NO-GO / GO-IF verdict backed by evidence: snapshot, momentum, contribution landscape (PR acceptance rates, outside-contributor merge rates, first-timer outcomes), issue health, governance, community sentiment, and a final go/no-go recommendation. Use when the user asks 'should I contribute to X', wants a before-I-contribute due-diligence check on a project, asks whether a project accepts outside PRs, wants contribution-health numbers like PR merge rates or stale-PR counts, or asks for a deep dive on an open-source codebase's health and dynamics before investing time in it.
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Use after substantial web projects or repeated design defects to convert evidence into reusable skill improvements through root-cause analysis, regression scenarios, and controlled proposals without silently changing the core OS.
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Remote-sensing literature search, citation verification, sensor/platform/dataset query strategy, citation file management (.ris/.bib/.enw conversion), and reference management (BibTeX, related articles, DOI/arXiv lookup) across CrossRef, arXiv, Scopus, ScienceDirect, Semantic Scholar, and domain web sources. Use when the user needs coordinated multi-step literature workflows for remote sensing, Earth observation, geospatial AI, GIScience, photogrammetry, SAR, hyperspectral imaging, LiDAR, satellite missions, benchmark datasets, land-cover mapping, change detection, object detection, segmentation, retrieval, or multimodal remote-sensing foundation models. Also trigger on general literature-search needs during academic writing even without the word "Nature", such as searching for papers/literature, doing a literature review, verifying a citation, converting citation files, and Chinese phrasings like 文献检索、查文献、找文献、 文献综述检索、查论文、引文核对、参考文献管理、文献去重.
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Build a complete but efficient Nature-style Chinese PPTX presentation from a remote-sensing, Earth-observation, geospatial AI, or broader scientific paper, preprint, PDF, article text, abstract, figure legends, or reading notes. Use this skill whenever the user asks to make slides/PPT/PPTX for journal club, group meeting, paper sharing, thesis seminar, lab meeting, department report, or academic presentation from a remote-sensing research paper. It identifies the paper type and argument, selects only the figures needed for the story, writes Chinese slide content and speaker notes, creates the actual .pptx deck, and runs an explicit self-review/corrective revision loop focused on figure quality, text overflow prevention, and non-template visual design before delivery. Also trigger on general academic-presentation requests even without the word "Nature", such as turning a paper into slides, building an academic talk deck, conference/defense presentations, and Chinese phrasings like 论文做PPT、遥感论文汇报、组会PPT、文献汇报、学术汇报、做幻灯片、讲paper、读书报告PPT.
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 provides a URL from AMD / ROCm official sources (github.com/AMD/*, github.com/ROCm/*, github.com/ROCmSoftwarePlatform/*, rocm.docs.amd.com, amd.com/…/rocm, gpuopen.com). Prefer this over generic GitHub for official ROCm URLs. Generates a hands-on tutorial-following project with no collaborator roles — only the user and the Instructor.
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WooCommerce store operations through NV oOS Pro — product research, catalog cross-referencing, inventory lookups, and Shopify or Flowhub comparisons where integrations are configured.
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Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
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Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
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Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.
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Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy
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Use when the user asks to "codify how our founder sounds", "build a founder voice dossier", or "set our content pillars"; runs an 80%-extraction interview over the user's OWN posts, emails, and decks (never competitor scraping, never an invented persona) and produces the versioned voice record — a per-platform register map (incl. 小红书/微信公众号), banned phrases, per-context disclosure lines (the ECHO C2 upstream), a few-shot bank built exclusively from own posts, and 3-5 content pillars with Estimated %-allocations — submitted via memory/events/channels.ndjson for channel-registry to store as voice-dossier.md, the record every Craft-phase skill reads first. Not for the brand-level voice canon — use brand-language-codifier; not for audience/persona research — use audience-mapper. 声音档案/品牌语气/创始人语气/内容支柱/披露声明
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Use when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs target and benchmark, platform/influencer/content rankings, engagement-quality and sentiment reads, conversion-attribution breakdowns, and ranked learnings. Not for dollar-level return math — use roi-calculator. 达人营销效果分析/投放复盘
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Plan, analyze and synthesize customer research into pains, jobs, triggers, objections, language, segments and evidence-backed marketing implications.
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Create a narrated editorial explainer from a topic, presenter, or photo using high-contrast paper-collage keyframes, motion, audio, and captions.
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Use when evaluating, critiquing, transforming, or generating a Master's Informatics thesis idea, research gap, novelty, research question, experiment, AI/ML/CV/NLP/LLM/Edge-AI method, or when distinguishing R&D research from software implementation using the Frascati Manual 2015.
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。