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Generate academic research proposals for PhD applications. Use when user asks to "write a research proposal", "create PhD proposal", "generate research plan", "撰写研究计划", "写博士申请", "doctoral proposal", or mentions specific research topics for PhD application. Supports STEM, humanities, and social sciences with field-specific adaptations. Follows Nature Reviews-style academic writing conventions. Supports both English and Chinese output based on user preference.

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Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

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Meta/Facebook/Instagram Ads campaign structure analysis detecting creative fatigue, audience overlap, scaling opportunities, and iOS tracking verification issues. Use when pasting Meta account data, analyzing Facebook ad performance, reviewing Instagram campaigns, or requesting audit of social advertising spend. Platform: Meta.

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Research a topic across the web, GitHub, and X/Twitter with DeepAPI and produce a concise Markdown report. Use for varied-search or requests to compare web coverage, relevant repositories, and social discussion.

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Optimize title tags, meta descriptions, Open Graph, and Twitter cards for maximum click-through rate. Generates multiple A/B test variations with character counting and SERP preview. Use when asked to "optimize title tag", "write meta description", "improve CTR", "Open Graph tags", "fix my meta tags", "social media preview", "my click rate is low", "title optimization", or any meta tag task.

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Reaches web pages the user's browser cannot open: WAF-blocked and bot-scored hosts, platform-native readers (Chinese and social platforms), archive snapshots with provenance, and a browser you own for stealth or throwaway profiles. Not for plain search, an unblocked static fetch, or a page the user's browser can open.

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Competitor research and intelligence skill. Takes a user's company (with optional seed competitor URLs), auto-discovers additional competitors via Browserbase Search API, deeply researches each using a 4-lane pattern (marketing surface, external signal, public benchmarks, strategic diff vs the user's company), and compiles the results into an HTML report with four views: overview, per-competitor deep dive, side-by-side feature/pricing matrix, and a chronological mentions feed (news, reviews, social, comparison pages, and public benchmarks). Use when the user wants to: (1) analyze competitors, (2) build a competitive matrix, (3) extract competitor pricing / features, (4) find comparison pages and online mentions of competitors, (5) surface public benchmarks. Triggers: "competitor analysis", "analyze competitors", "competitive intel", "competitor research", "competitor pricing", "feature comparison", "price comparison", "find comparisons", "who's comparing us", "competitor mentions", "competitor benchmarks".

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Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

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Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.

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Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.

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Use when the user wants to evaluate a creator, influencer, or brand audience using public profile signals, TikTok audience demographics, follower/following data, comments, geography, language, and content fit. Helps judge sponsorship and market fit.

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Backend and infra for a project via Cohesivity (cohesivity.ai). Provisions Postgres, hosting and deploys, auth and social login, realtime websockets, an agent-native email inbox, object and vector storage, Redis, cron, and AI model APIs (OpenAI, Anthropic, Deepgram, Exa) through one HTTP API. Use when a valid .cohesivity file exists, a Cohesivity MCP is available, the user names Cohesivity, or the project needs a backend or any of these services and no competing provider was chosen.

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Marketing command center. Email campaigns (Klaviyo), paid ads (Meta/Google), analytics (GA4), SEO, and social media metrics. One dashboard for all marketing channels.

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Publish and schedule content to 15 social platforms through one Upload-Post API call: TikTok, Instagram, YouTube, LinkedIn, Facebook, X, Threads, Pinterest, Bluesky, Reddit, Discord, Telegram, Mastodon, WordPress, and Google Business Profile. Use when the user wants to post or schedule a video, photo carousel, text, or document across several platforms at once, check upload status, pull analytics, or run Instagram comment-to-DM funnels.

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Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

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小红书内容总入口：生成标题、正文、caption、hashtags，以及 3-9 张图文卡片或短视频分镜，覆盖素材分析、卖点评估、去 AI 味和质检。 当用户说“写/做小红书笔记、小红书图文/种草/文案、出一套卡片、小红书视频”时使用。 整套笔记用本 SKILL；仅渲染卡片用 card-xiaohongshu；其他平台的通用文案用 social-content。

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通用多平台社媒内容（单条/兜底）：钩子文案、正文、标签策略和互动引导，主打**涨粉/互动/内容运营**， 支持微博/抖音/B站/知乎/公众号/X 等；平台不确定或要多平台一次成稿时的默认选择。 当用户说"写条微博""发个帖子""社媒文案""发什么内容""帮我写个帖""涨粉内容""互动内容"时使用。 边界：**小红书整套笔记/图文/种草**找 xhs-note-creator；**带货转化/卖点/详情页**找 copywriting； **严格套 PAS/AIDA 框架排版**找 post-formatter。

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。

生成月度社媒效果复盘报告，分析小红书、抖音、B站、微博等平台的内容表现，输出下月可执行建议。 当用户说"月度复盘""效果复盘""这个月表现""内容复盘""运营总结""下月建议""月报"时使用。 和 skill-publish-analytics 的区别：analytics 从发布日志做四维归因，本 SKILL 做跨平台组合级月度复盘。

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分析发布日志数据，从发布时间、标签效果、内容类型、粉丝增长四个维度归因内容表现，输出可执行的优化建议。 当用户说"发布数据分析""归因分析""什么时间发好""标签效果""内容表现分析""发布日志分析"时使用。 和 skill-social-performance-review 的区别：本 SKILL 从发布日志做四维归因，review 做跨平台月度组合复盘。

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对社媒帖子草稿进行互动潜力评分，基于历史表现数据输出结构化评分卡。 当用户说"帖子打分""评分""这条能火吗""发布前评估""内容质量分""评分卡""草稿评估"时使用。 和 skill-topic-evaluator 的区别：evaluator 评还没做的选题潜力，本 SKILL 评已写好的草稿质量； 和 skill-social-performance-review 的区别：本 SKILL 评单条草稿（发布前），review 做月度组合复盘（发布后）。

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长篇小说/网文连载创作：从世界观、人设和三级大纲写到逐章正文，并用文件化状态维护伏笔、前情和跨章一致性。 当用户说“写小说/网文/盐选故事、连载、续写下一章、小说大纲、人物设定、世界观、黄金三章”时使用。 通用文章用 article-outline/social-content；单段改风格或润色用 style-transfer/text-polisher。

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国内带货转化营销文案：提炼卖点并产出种草、信息流广告、活动促销、电商详情页或落地页的标题、正文和 CTA。 当用户说“写种草/广告/活动/促销/详情页/落地页文案、提炼卖点、广告语”时使用。 整套小红书笔记用 xhs-note-creator；涨粉互动内容用 social-content；严格套 PAS/AIDA 等框架用 post-formatter。

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Provides open source intelligence techniques for CTF challenges. Use when gathering information from public sources, social media, geolocation, DNS records, username enumeration, reverse image search, Google dorking, Wayback Machine, Tor relays, FEC filings, or identifying unknown data like hashes and coordinates.

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Fetch normalized stock sentiment across Reddit, X.com, financial news, and Polymarket from the Adanos Finance API: buzz score, bullish percentage, mention or trade counts, and trend. Use this skill whenever the user asks how much a stock is being discussed or how hot it is on social platforms, how many Reddit mentions or Polymarket bets a company has, whether Reddit, X, and news agree, or wants to compare stock buzz and sentiment across tickers. Read-only: it never places trades or modifies anything.

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。