리서치·학습 스킬

Search the web and fetch page contents with Keenable AI — web search, page extraction, point-in-time queries, prompt-driven content extraction. Use when searching the web for current information, fetching webpage contents, extracting specific data from URLs, or researching topics with citations. Triggers on: web search, search the web, fetch page, read URL, find pages about, current information, Keenable, keen search, research with sources, look up, latest news, find information, check online, what is, who is.

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Searches the web through Exa 3.0 with six Python scripts—lite/auto/deep search with category, domain, and date filters, Dynamic Highlights, structured output with field-level citations, clean extraction from known URLs, cited answers, and the async Agent API for list building, enrichment, and multi-hop research. This skill should be used for any web search, fact lookup, current events, finding papers, companies, people, or news, reading known URLs, questions that need cited answers, building lists of entities that match criteria, or enriching records with web data. It is the default web search tool (built-in WebSearch is blocked). Triggers on search the web, look up, find pages, research with citations, Exa, list of companies, enrich.

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Unified web and social media search across 5 providers (Brave, Tavily, Xpoz, Exa, Firecrawl). Auto-routes queries to the best API, supports parallel multi-provider search with deduplication, AI-generated answers, and social media search. Triggers on web search, find news, search Twitter, search Reddit, social media search, multi-provider search, omnisearch, research news, OSINT, search for, look up, find information about.

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Investigate datasets and resolve entities using OpenPlanter methodology — cross-reference heterogeneous sources, build Admiralty/ACH confidence-tiered evidence chains, and apply OSINT tradecraft. Triggers on entity resolution, cross-reference datasets, evidence chain, OSINT investigation, structured investigation.

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Scrape pages locally with anti-bot bypass, TLS impersonation, and adaptive element tracking — no API keys, no cloud. Handles Cloudflare protection, CSS/XPath element extraction, and survives site redesigns. Complements firecrawl (cloud) with 100% local execution. Triggers on Cloudflare bypass, anti-bot scraping, stealth fetch, local scraping, Scrapling.

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Identify the source of an image or video frame — TV show episode, movie scene, geographic location, or original publication. This skill should be used when the user asks to identify where an image is from, trace a screenshot back to its source, geolocate a photo, find what show or movie a frame is from, or do a reverse image search. Chains Google Vision, Picarta geolocation, and Gemini in parallel with graceful degradation. Triggers on: reverse image search, identify source, what show is this, where was this taken, trace image, identify video, what movie, which episode, geolocate photo, image source.

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Scrape web pages to clean markdown using Firecrawl v2 — handles JS-heavy pages, site crawls, URL mapping, document parsing (PDF/DOCX/XLSX), LLM-powered extraction, autonomous agent scraping, and post-scrape browser interaction (Interact API). Free keyless mode available for search/scrape/parse/interact (no API key needed). Prefer over WebFetch for quality and completeness. Triggers on scrape URL, fetch page, crawl site, extract content, parse document, web to markdown, DeepWiki, Firecrawl.

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Scaffold and run Karpathy-style autoresearch loops in any git repo. This skill should be used when setting up autonomous code improvement, generating adversarial eval harnesses, running hypothesis-implement-eval-keep/discard loops, or checking autoresearch progress. Triggers on "autoresearch", "autonomous improvement", "eval loop", "hypothesis loop", "self-improvement loop".

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Specialized research and analysis prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for data analysis, policy research, systematic evidence review, investigative research, and research-specific deliverables. Use when building prompts for quantitative data interpretation, evidence-based policy recommendations, literature reviews, causal analysis, hypothesis-driven investigation, or briefing documents. Trigger on: "prompt for data analysis," "research prompt," "policy brief prompt," "literature review prompt," "prompt for investigating," "systematic review prompt," "build a prompt for evidence synthesis," "causal analysis prompt," "briefing document prompt." Also use when user describes a research or analytical task and needs a structured Claude prompt for it. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or auditing Claude Projects (use rootnode-project-audit if available).

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为中文微信公众号文章分析内容并生成清晰易懂、真实 3D 毛毡质感且图文一体的头条长封面、1:1 分享页封面与正文配图，同时给出每张正文图应插在哪个原句之后。用于用户提供文章、Markdown 或正文，并要求“公众号封面”“分享封面”“方形封面”“毛毡配图”“文章插图”“为正文配图”“只做封面”“生成 N 张配图”“推荐配图数量”等任务；未提供文章标题时默认根据核心命题生成合适的封面文字，正文图未指定画幅时默认 16:9，也支持用户明确指定 4:3 或 3:4，并使用少量毛毡文字帮助单图理解；适配方法论、教程、专业干货、商业科技、情感叙事、个人成长、生活方式、教育与母婴等内容。

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Generate format-controlled research reports with evidence tracking, citations, and iterative review. This skill should be used when users request a research report, literature review, market or industry analysis, competitive landscape, policy or technical brief, or require a strict report template and section formatting that a single deepresearch pass cannot reliably enforce.

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Analyzes research papers (PDF/arXiv URL) and converts them into executable code. Automatically activated upon requests for paper replication, algorithm implementation, or research reproduction. Responds to requests like "Implement this paper", "paper2code", "Convert paper to code".

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Transform academic papers into in-depth technical articles with multiple writing style options. Use the MinerU Cloud API for high-precision PDF parsing, automatically extracting images, tables, and formulas. Optional formula explanations and GitHub code analysis, generating Markdown and HTML formats.

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Use when studying Intel VT-x or AMD-V internals: VMCS and VMCB, EPT and NPT, VM exit handling, APIC virtualization, or building a minimal type-1 hypervisor. Not for running VMs: use qemu-kvm.

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Use when asked to create a multi-session learning plan. Returns a milestoned plan, practice, and review rubric. Don't use for tasks that require source or remote-system changes.

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Use when asked to review learning progress after a milestone. Not for engineering retrospectives, use engineering-retrospective. For agent-environment ones, use agent-environment-retrospective.

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Use when the learner is ready to apply cleared concepts in a real project. Not for exercises or quizzes: use drill.

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Use when the user wants a course, a learning workspace, or ongoing teaching across sessions, with cited lessons and retention-gated advancement. Not for one-off explanations: use explain-concept.

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Use when a user requests an engineering retrospective for a period. Not for agent-environment retrospectives: use agent-environment-retrospective. For learning milestones: use learning-retrospective.

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Use when asked to mine prior chats on a scheduled or watcher tick and maintain project memory. Not for remote, credential, publish, deploy, or irreversible mutation.

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Use when a verified non-trivial fix lands or existing solution docs need refresh. Not for unverified fixes.

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Help the user understand something well enough to explain it. Use when the user needs to understand something and it is not yet clear what kind of understanding they need.

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**雨哥（雨哥聊海外销售）出品**的海外销售公司背调与市场调研助手 —— 雨哥海外销售方法论金字塔「第 5 层 · 市场经营」的 AI Skill 化产物。基于「五看六定」经典市场分析框架（华为原版 + 雨哥改造版），输出标准化的市场调研报告，适用于客户拜访前调研、代理商签约前尽调、竞品季度跟踪、新市场进入决策、年度战略规划、面试求职准备等多种海外销售工作场景。 作者：雨哥 · 13 年海外销售实战 · 曾任普渡机器人亚太区负责人（年营收破亿 · 团队 5→50 人）· 海外销售培训头部 IP（累计付费客户 130+）。 触发方式：当用户说"帮我调研 XX 公司""做一下 XX 公司的背调""XX 公司的市场调研""调研 XX 在欧洲/北美/东南亚的市场""XX 公司客户拜访准备""XX 代理商尽调"时使用。 也在用户提到公司名 + 关键词（"调研""背调""尽调""市场分析""客户拜访""代理商谈判""竞品分析""go-to-market""GTM""面试""五看六定"）时主动触发。 This skill should be used proactively when a company research request is detected.

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実装タスクを受け取ったとき、5つの専門エージェントチームでコードベースを調査・議論し、 AI 間の合意形成を経て Plan Mode で実装計画を作成する。 不明点はユーザーに質問し、完璧な計画を立ててから実装する。 バグ修正・新機能・リファクタリング・設定変更など、あらゆる実装タスクに対応。 Use when: 実装を始める前に調査が必要なとき、影響範囲が不明なとき、 複数ファイルにまたがる変更をするとき、慎重に計画を立ててから実装したいとき。 Triggers: "調査してから実装", "team plan", "まず調査", "影響範囲を調べて", "計画を立ててから", "plan before implement", "コードベースを調査して実装", "research first", "調査→計画→実装"

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