「deep sea」の検索結果

Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.

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Plan and execute a reversible migration from another search system or legacy index design to Algolia. Use when translating schemas, queries, relevance behavior, or production traffic. Trigger with "migrate to Algolia", "Algolia cutover", or "search engine migration".

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Claude-native deep research using DAG-based query planning, parallel subagent execution, and gap-driven iteration. No external API needed.

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Use Parallel CLI for web search, URL extraction, deep research, structured data enrichment, entity discovery, and recurring web monitoring. Best for requests that explicitly need current web evidence, academic-source discovery, repeated entity lookups, exhaustive reports, or ongoing change tracking.

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向现有调研outline补充items（调研对象）。

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。

向现有调研outline补充字段定义。

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。

对目标话题进行初步调研，生成调研outline。用于学术调研、benchmark调研、技术选型等场景。

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。

Korean and French screenwriting practice (韩国/法国编剧方法) — from the international masterclass; Oh Seung-uk (Christmas in August, The Shameless; field research in disguise, "write the emotion", first act and last act first, spaces as skeleton, reading to hostile listeners, surviving producers), Choi Seok-hwan (King and the Clown; genre as promise to the audience, "story = deepening crisis + intensifying desire", two reversals and bittersweet endings, the producer/director/writer trio), and Frédéric Auburtin (Paris je t'aime; dialogue last, Italian-style table reads, "12 lines not 13", dignity for characters, originals born from commissions, editing by emotion). Use when working in a Korean-style commercial drama or genre film, a French auteur/commission context, when planning field research, when a producer relationship is the problem, or when you need the Korean "three live together" development model or the French "emotion first, structure second" editing approach.

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Answer questions using the Arkon knowledge base. Searches wiki first, drills into raw sources only when needed. Supports quick, standard, and deep modes. Triggers on: what do we know about, find in KB, look up, query:, arkon query, search the wiki, tell me about, based on the KB.

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Clones and audits competitor repositories into evidence-based intelligence with file:line citations. Use to track competitors, add a competitor repo, review competitor code, compare capabilities, build a competitor landscape, or check for code updates: 竞品分析 / 竞品 / competitor scan / analyze competitor / compare with X / latest competitor code. Not for market research without code (use deep-research).

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Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

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Deep competitive intelligence for any market. Analyzes competitors' products, pricing, customer sentiment, GTM strategy, and growth signals using real web data. Produces battle cards, pricing landscape, and feature matrix. Use when the user wants to understand their competitive landscape, analyze competitors, compare products in a market, or research who they're competing against. Triggers for "who are my competitors", "competitive analysis", "competitor research", "battle cards", "pricing comparison", "competitor pricing", "market players", "competitive intelligence", "competitive landscape", "who else is in this space", "competitive moat", or any request to profile, compare, or map competitors in a category. Works standalone — no prior startup-design session needed.

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Best practices for systematic research, source evaluation, and evidence gathering

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Produce a research report from the vault: scope the question against existing pages, name the gaps, and write findings with citations, disagreements and limits. Use this skill when the user asks for a research report, a deep write-up on a topic, or wants to understand a subject thoroughly using what they have collected. Do NOT use for a quick answer from the vault, for drafting an article in the user's voice, or for summarising a single source.

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Arbitrator research tool for investor-State dispute settlement (ISDS). The tool can summarize arbitrators' experience, with deliverables grounded in primary documents; it can also help find arbitrators based on the issues faced in a particular case. Use when the user asks about an arbitrator ("tell me about arbitrator X", "X, Y and Z"), wants help choosing or shortlisting arbitrators for a case, or asks about appointments, dissents, issue track records (e.g. FET, expropriation), challenge risk, double-hatting, or tribunal composition. Profiles are grounded in the UNCTAD ISDS Navigator dataset plus targeted live lookups (ICSID); deep verification retrieves the actual decisions. Sibling of isds-research.

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When you want to monitor known sources on a schedule and feed the good stuff into your second brain. Configure sources once (YouTube, RSS/newsletters, subreddits, Hacker News, Bluesky, Mastodon, X, LinkedIn, keyword searches); each run fetches only what's new, scores it against your stated focus, writes one digest to the vault, and auto-captures high-signal items into raw/. Everything else waits in the digest until you promote it. Modes — run, digest, promote, add / sources / pause, doctor, schedule (daily launchd or systemd job). Triggers on "/radar," "run my radar," "check my sources," "what's new from my sources," "add a source," "monitor this channel," "watch this subreddit," "track this account," "daily digest," "promote item 4," "radar doctor." Complements second-brain (radar fills raw/, second-brain compiles it) and deep-research (standing surveillance vs a one-off dive).

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When you want multiple expert perspectives on a founder/operator question — a simulated board of advisors (Jason Fried, Elon Musk, Jeff Bezos, Jensen Huang, Bob Iger, Paul Graham, Naval Ravikant, Sara Blakely). Bring a real decision — "should I raise prices?", "hire my first employee?", "raise or bootstrap?", "kill this project?" — and the council weighs in through documented frameworks, surfaces disagreements, and synthesizes a recommendation. Also use for 'maker council,' 'board of advisors,' 'what would Bezos do,' 'what would Jason Fried say,' 'channel Naval,' 'ask the council,' or how a famous founder would approach a problem. Optional live-research pass via deep-research / watch-video / last30days. Marketing questions go to marketing-skills' marketing-council. Archives to ~/.config/makerskills/maker-council/archive/. To commit to a direction, hand off to decide.

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When you want to pressure-test a potential new business, product, or side project against the serial-founder filter. Not "marketing ideas for a product" (that's marketing-skills:marketing-ideas) — this is "should this business exist + can you win it." Runs the idea through a structured framework (problem, audience, wedge, monetization, moat, portfolio fit, distribution, energy fit, opportunity cost), checks domain availability via /domain, optionally triggers /deep-research for market validation, and outputs a viability brief: build / sleep on it / pass. Archives every idea to ~/.config/makerskills/business-brainstorm/archive/ so past work is searchable. Triggers on "/business-brainstorm," "/brainstorm," "new business idea," "should I build X," "pressure test this idea," "validate this idea," "is X a good business," "what about a [type] for [audience]."

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Deep market analysis for iOS/macOS apps including market sizing (TAM/SAM/SOM), growth trends, market maturity, entry barriers, distribution channels, and revenue potential. Use when user asks for market research, market size, market opportunity, growth potential, TAM/SAM/SOM, or market trends.

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Define a brand's target audience with deep personas, psychographics, and ICP (Ideal Customer Profile). Use when the user says "target audience", "ideal customer", "customer persona", "audience persona", "ICP", "who is our customer", "define our audience", "audience research", "buyer persona", "who should we be targeting", "audience profile", "customer profile", "who buys this", or needs to understand their audience more deeply before investing in brand or marketing work.

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Use when the user needs to understand customers deeply enough to write copy and pick targeting — consumer versus shopper, JTBD, layered persona, internal monologue, pain map, objection list, journey map, and a customer language bank pulled from real reviews and DMs. Trigger on 'customer insight', 'buyer persona', 'who is my customer', 'what do they actually want', 'jobs to be done', 'why do they hesitate before buying'. Also use when the user cannot explain who the product is for. Not for — intake questions when an agency signs a new client, see `20-client-intake-brief-global`; paid audience targeting setup, see `51-audience-research-global`; competitor study, see `08-competitor-research-global`.

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Cyber threat intelligence and OSINT analysis toolkit. Runs structured investigations and delivers analyst-grade intelligence products with sourced, trust-scored findings. Use for OSINT and CTI cases, digital-footprint and exposure review, domain/subdomain/DNS/certificate recon, web-infrastructure pivoting (favicon hashes, tracker IDs, TLS certs, phishing-kit fingerprinting, campaign clustering), username/email/phone enumeration, breach and infostealer-log triage, image forensics, geolocation, crypto-wallet and IBAN/bank-account tracing, darknet search, M365/Azure and SaaS tenant recon, China/Sinophone recon (ICP filings, PRC corporate registries, Baidu/FOFA/Quake/ZoomEye), vulnerability and ransomware lookup, threat modeling, PII redaction, and structured reporting. Commands include /case, /sweep, /query, /webpivot, /username, /phone, /email-deep, /breach-deep, /icp, /cn-corp, /iban, /stealer-log, /exposure, /threat-model, /report, /brief, /redact, /apikeys.

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Audit one research Workspace for declared Unit outputs and Pipeline target Artifacts, writing `output/CONTRACT_REPORT.md`; use for mid-Run coverage snapshots or final delivery completeness, not deep provenance integrity.

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深度研究代理系统社区版，对复杂主题进行多轮迭代的网络搜索和综合分析。使用场景：当用户需要对特定主题进行深入、全面的研究时，特别是需要基于最新网络信息生成详细分析报告的情况。支持多轮迭代搜索、智能搜索方向调整和综合分析报告生成。本skill是一个工作流描述文档，没有执行脚本，依赖byted-web-search skill进行网页搜索。

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。