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xiaohu-ip-studio
by xiaohuailabs
开源中文配图技能 + IP 角色库。用"挑认知锚点 → 现编隐喻 → 反 PPT 自检"的方法,为中文深度文/方法拆解生成由固定角色出演的正文配图(不是通用插画,不是样式库选风格)。自带 31 个原创 IP 角色(手绘线稿 15 + 谐音梗 meme 16),配图时按文章调性选一个角色出演,也可自建角色。当用户说"配图""正文配图""IP 配图""给这篇配图""隐喻配图""选个角色配图""配图 shot list"时使用。
computer-use
by NousResearch
Drive the user's desktop in the background — clicking, typing, scrolling, dragging — without stealing the cursor, keyboard focus, or switching virtual desktops / Spaces. Cross-platform: macOS, Windows, Linux. Works with any tool-capable model. Load this skill whenever the computer_use tool is available.
harness-codex-skill
by orange90
整理并结构化 Codex automation（自动化任务）在工作目录里留下的烂摊子。逐段判读 memory.md / AUTOMATION_REQUIREMENTS.md，把「用户要求」与「运行记录」分流到不同文件，将散落的长期要求合并进 ~/.codex/automations/<id>/automation.toml 的 prompt 并重新结构化，重建 single source of truth；同时通用地清理冗余 Python/Node 环境、缓存、tmp 快照、泄漏到根目录的数据 JSON 等可丢弃产物 （隔离到可恢复的回收目录，绝不硬删除）。当用户提到 Codex 自动化任务的工作目录混乱、屎山、收拾/整理 automation 产物、memory.md 太乱、把要求合并进 automation.toml、automation.toml 缺结构、 跑出一堆 venv/tmp/中间文件没清理时，务必使用本技能，即使用户没说出「skill」二字。
Full-empirical-analysis-skill
by brycewang-stanford
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".
Full-empirical-analysis-skill-Stata
by brycewang-stanford
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. 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 Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). Also covers two parallel domain modes that share the same 8-step scaffolding — Mode A — Epidemiology / public health (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via teffects ipw / teffects ipwra / teffects aipw / eltmle, Mendelian randomization via mrrobust (IVW / Egger / weighted median) and mregger / mrpresso, KM / Cox / AFT / RMST survival via sts / stcox / streg / strmst2, E-value sensitivity via evalue (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and Mode B — ML causal inference (DML via ddml / pdslasso, S/T/X/R/DR meta-learners via crforest and ddml interactive, causal forest via crforest / cforest, BART/BCF via bart / bartCause-style externals, CATE distribution + policy tree via crforest, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via pcalg / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".
Full-empirical-analysis-skill-R
by brycewang-stanford
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. 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 R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). Also covers two parallel domain modes that share the same 8-step scaffolding — Mode A — Epidemiology / public health (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via WeightIt / gfoRmula / tmle / ltmle, Mendelian randomization via MendelianRandomization / TwoSampleMR / MRPRESSO, KM / Cox / AFT / RMST survival via survival / survminer / flexsurv, E-value sensitivity via EValue, principal stratification — STROBE / TRIPOD reporting), and Mode B — ML causal inference (DML via DoubleML, S/T/X/R/DR meta-learners via causalweight / grf, causal forest via grf::causal_forest, BART/BCF via bartCause / bcf, matrix completion via MCPanel, CATE distribution + policy tree via policytree, off-policy evaluation, conformal causal via conformalInference / cfcausal, fairness audit via fairmodels, DAG learning via pcalg / bnlearn / LLM-assisted). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".
prd-convert-to-template
by DriftMrD
Converts PRDs in arbitrary formats into the repository's prd-template.md structure. Two runtime skills — document (file upload) and dialogue (conversation) — are selected automatically by the backend; see skill-document.md and skill-dialogue.md.
gmgn-swap
by GMGNAI
"[FINANCIAL EXECUTION] Buy and sell meme coins and crypto tokens on Solana, BSC, Base, or Ethereum — single swap, multi-wallet batch trading, limit orders, stop loss, take profit, trailing stop loss, trailing take profit via GMGN API. Requires explicit user confirmation. Use when user asks to buy, sell, or swap a token, trade from multiple wallets, set a limit order, stop loss, take profit, or check order status."
mindriver
by 503496348-ops
智脑星河 MindRiver — Agent上下文数据库。 文件系统范式管理记忆/资源/技能，分层加载减少91% token消耗，让Agent越用越聪明。
deviseur-azure
by tigercatyxcl
Azure pre-sales quoting from a hardware spec. Use when a user describes a VM by specs (e.g. "4U8G, 25G SSD", "4 vCPU 8GB", "8 cores 64GB memory") and wants Azure pricing or a quote. Two-step flow - first PROPOSE several matching Azure flavors (SKUs) across families with live prices in a region, let the user pick one, then output a full region QUOTE including managed disk and monthly/annual totals. Also supports BATCH sizing from a VMware RVTools .xlsx export (vInfo sheet) - map every VM to a D-preferred Azure flavor (RAM meets-or-exceeds source, vCPU floors to nearest size just below) with rollup totals. Defaults to francecentral region and EUR currency.
apex-spec
by moshehbenavraham
Specification-driven workflow system for AI-assisted development. Use when the user mentions "spec system", "session workflow", "create PRD", "UX PRD", "plan session", "implement session", "validate session", "code review", "creview", "phase build", "session scope", "task checklist", "initspec", "plansession", "audit", "pipeline", "infra", "carryforward", "documents", "copush", "sculpt-ui", "seshsplit", "session split", "split plan into sessions", "docker build", "upstream changes", "quick implement", "quick frontend", "quick backend", "pull upstream", or when working in a project containing a .spec_system/ directory. Philosophy: 1 session = 1 spec = 2-4 hours (12-25 tasks).
doctor-tieu-tam
by NachaFromMars
"Bác sĩ heal từ xa cho PC và OpenClaw khác. Dùng khi một máy/agent OpenClaw (qua Tailscale/SSH) báo lỗi, không reply, sai config model/provider, gateway chết, port proxy down, cron/subagent fail, hoặc cần chẩn đoán + sửa từ xa. Quy trình: kết nối an toàn → khám sức khỏe → đọc log → chẩn đoán nguyên nhân gốc → backup → fix → verify → ghi known-fixes → báo cáo. Triggers: heal PC, sửa openclaw từ xa, máy không reply, gateway down, model swap không chạy, codex limit nhưng đã chọn claude, ssh fix, tailscale fix, doctor, chẩn đoán lỗi máy khác."
silicon-circle
by txw842lkj-cmd
Operate Silicon Circle's task trading and Skill consignment loops: publish tasks, apply or submit work, consign Skill products, and buy or run reviewed Skills through platform APIs.
binance-agentic-wallet
by binance
Use when the user mentions connect/disconnect wallet, sign in, sign out, web3 wallet, wallet address, check balance, how much crypto do I have, send BNB/USDT/crypto, transfer tokens, swap tokens, buy/sell token, DEX trade, limit order, market order, cancel order, get a quote, transaction history, wallet settings, daily limit, slippage, MEV protection, supported chains, available networks, prediction market, predict.fun, YES/NO market, place a prediction, redeem winnings, claim payout, prediction portfolio, prediction PnL, x402 payment, HTTP 402 Payment Required, pay a known x402 API, check approvals, view token approvals, revoke approval, manage approvals, wallet approvals, authorization management, token authorization, or any on-chain wallet operation.
token-saver
by SiruGao
Reduce avoidable token and API cost through task-aware model selection, context hygiene, concise responses, and disciplined tool use. Apply as an advisory policy on every turn. Actual savings vary by agent, model, provider, cache behavior, and workload; measure results instead of assuming a fixed percentage.
universal-video-generator
by marketleonai-del
通用视频生成接口层 + 动态工作流编排器 + 长视频拼接 + 自进化。类比 LangChain 统一所有 LLM——本 skill 统一所有 AI 视频模型（Seedance/可灵/Veo/Runway/Wan…）与 TTS/声纹克隆工具（CosyVoice/GPT-SoVITS/火山/ElevenLabs…）。核心：①统一接口+能力画像；②能力驱动选型+回退；③@voice1 声纹锁；④动态工作流（按预算/素材/平台/矩阵组装）；⑤成本估算+120门质检；⑥多租户/配额/计量/幂等/持久/熔断/可观测/Webhook（商用）；⑦长视频（多段续帧一致+拼接+混合用户素材）；⑧自进化（匿名隐式遥测→验证门控→学习率→回滚，SkillOpt/Hermes 思路）；⑨插件式（新增模型只加 Adapter）。是 ugc-creative-amplifier 的底层通用化改造。自动触发：换/接入/统一视频模型或 TTS、按预算选模型、动态编排、长视频拼接、自进化/数据上报、给新模型写适配器、部署成服务时命中。显式指令：/接入模型 /选型 /锁声纹 /编排 /长视频 /自进化 /成本 /适配器 /部署。
tossinvest-openapi-integration
by kuil09
Use when integrating Toss Securities Open API for market data, holdings, order workflows, and approval-gated execution.
loops
by jwangkun
"AI驱动的自动化循环集合。覆盖软件开发、数据科学、内容创作、产品运营、学习管理等领域。当用户需要自动化工作流、修复问题、生成内容、分析数据或执行质量检查时使用。"
codex-audit
by Kanshan03
Codex 内容安全审计 — 每次用户输入时通过审计模型检测违规内容，防止 Pro 账号因滥用被封。
ashare-screener
by lc132
A股每日盘前短线标的智能筛选(v6.9.28)。基于前一日收盘数据，通过 38步筛选流程（网络授时北京→GitHub持仓跟踪→节假日→极端行情→外围市场→持仓同步→做T→持仓跟踪→持仓危机→全市场API拉取→行业查表补全→pytdx历史K线→东方财富财务(已降级)→F10单股API→风险事件→拥挤度(机构持仓+融资过热)→13项硬排除→27项信号过滤→十七策略评分→行业集中度→生成HTML报告→GitHub同步→飞书推送→每周复盘），输出短线标的_YYYYMMDD.md 为Markdown格式，同时生成可视化HTML报告。当用户需要运行盘前筛选、A股短线选股、每日标的预测时使用。
kanban-video-orchestrator
by NousResearch
Plan, set up, and monitor a multi-agent video production pipeline backed by Hermes Kanban. Use when the user wants to make ANY video — narrative film, product/marketing, music video, explainer, ASCII/terminal art, abstract/generative loop, comic, 3D, real-time/installation — and the work warrants decomposition into specialized profiles (writer, designer, animator, renderer, voice, editor, etc.) coordinated through a kanban board. Performs adaptive discovery to scope the brief, designs an appropriate team for the requested style, generates the setup script that creates Hermes profiles + initial kanban task, then helps monitor execution and intervene when tasks stall or fail. Routes scenes to whichever Hermes rendering / audio / design skill fits each beat (ascii-video, manim-video, p5js, comfyui, touchdesigner-mcp, blender-mcp, pixel-art, baoyu-comic, claude-design, excalidraw, songsee, heartmula, …) plus external APIs for TTS, image-gen, and image-to-video as needed.
ios-hig-design
by costrict-plugins-repo
'Design native iOS interfaces following Apple Human Interface Guidelines. Use when the user mentions "iPhone app", "iPad layout", "SwiftUI", "UIKit", "Dynamic Island", "safe areas", "HIG compliance", "SF Symbols", "haptic feedback", or "iOS accessibility". Also trigger when building tab bars, navigation stacks, sheets, or modals for iOS, implementing dark mode, or adapting layouts for different screen sizes. Covers navigation patterns, accessibility, SF Symbols, and platform conventions. For general UI polish, see refactoring-ui. For affordance design, see design-everyday-things.'
lean-startup
by costrict-plugins-repo
'Design MVPs, validated learning experiments, and pivot-or-persevere decisions using Build-Measure-Learn. Use when the user mentions "MVP scope", "validated learning", "pivot or persevere", "vanity metrics", "test assumptions", "innovation accounting", "build-measure-learn", or "minimum viable experiment". Also trigger when deciding what to include in a first version, measuring startup progress, or evaluating whether to change direction on a product bet. Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done.'
influence-psychology
by costrict-plugins-repo
'Apply the six principles of ethical persuasion (reciprocity, commitment, social proof, authority, liking, scarcity) to product design, copy, and sales. Use when the user mentions "social proof", "persuasive copy", "why users dont convert", "ethical persuasion", "reciprocity", "scarcity tactics", or "commitment and consistency". Also trigger when designing testimonial sections, crafting urgency messaging, or improving trust signals on landing pages. For deal negotiation tactics, see negotiation. For viral word-of-mouth, see contagious.'