扩展智能体 Skill

「扩展智能体」分类共 662 个 Skill，按仓库 star 排序。分类自动生成，仅供参考。

全部文档与办公202 前端与设计261 需求与规划324 编码与调试449 浏览器与测试380 安全269 Git 与发布465 运维与云391 数据181 图表98 写作与翻译239 图像与视频451 营销与商业510 扩展智能体662 科研189 其他310

amazon-listing-competitor-analysis-skill

browser-act/skills6.1k

amazon-listing-competitor-analysis-skill

This skill helps users analyze Amazon competitor listings by ASIN and produce structured competitive intelligence plus strategic opportunity points for their own go-to-market. The Agent should proactively apply this skill when users want to analyze a competitor Amazon listing by ASIN, understand what a top-ranked product does right in content keywords or visuals, find market gaps and unmet buyer needs, turn competitor research into opportunity maps for their brand, identify keyword placement patterns on rival listings, extract SEO insights from Amazon product pages, reverse-engineer competitor bullet and title strategies, mine competitor reviews for buyer psychology, compare seller and A plus content patterns, run gap analysis before launching a new SKU, research why a listing wins conversion signals, synthesize whitespace you can own versus the diagnosed listing, or say just look at this ASIN with a competitive or optimization angle.

amazon-product-api-skill

browser-act/skills6.1k

amazon-product-api-skill

This skill helps users extract structured product listings from Amazon, including titles, ASINs, prices, ratings, and specifications. Use this skill when users want to search for products on Amazon, find the best selling brand products, track price changes for items, get a list of categories with high ratings, compare different brand products on Amazon, extract Amazon product data for market research, look for products in a specific language or marketplace, analyze competitor pricing for keywords, find featured products for search terms, get technical specifications like material or color for product lists.

amazon-product-search-api-skill

browser-act/skills6.1k

amazon-product-search-api-skill

This skill is designed to help users automatically extract product data from Amazon search results. The Agent should proactively apply this skill when users request searching for products related to keywords, finding best-selling items from specific brands, monitoring product prices and availability on Amazon, extracting product listings for market research, collecting product ratings and review counts for competitive analysis, finding specific products with a maximum count, searching Amazon in different languages for localized results, tracking monthly sales estimates for brand products, gathering product URLs and titles for a product catalog, scanning Amazon for Best Seller tags in a specific category, monitoring shipping and delivery information for brand items, building a structured dataset of Amazon search results.

amazon-reviews-api-skill

browser-act/skills6.1k

amazon-reviews-api-skill

This skill helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a competitive product, tracking sentiment of recent Amazon reviews, extracting verified purchase reviews for quality assessment, summarizing user experiences from Amazon product pages, monitoring product performance through customer reviews, collecting reviewer profiles and links for market research, gathering review titles and descriptions for content analysis, scraping Amazon reviews without requiring a login.

sn-ppt-story

OpenSenseNova/SenseNova-Skills5.7k

sn-ppt-story

当前 PPT 任务已由 sn-ppt-entry 建好 DECK_DIR（task_pack.json 与 info_pack.json 已写入）后，必须由本 skill 将 query、全部用户材料与已完成 Research 编排为唯一可编辑的 outline.md 时使用。本 skill 是 entry → 出口（standard / dazzle / creative）之间的强制中间环节：不允许执行 Agent 跳过本 skill 直接依据 task_pack / info_pack 即兴编写大纲，不允许出口 skill 代写或改写大纲；前置产物缺失时停止并返回 sn-ppt-entry 补齐，不得自行补做研究。

antislop-layoutmobile

miqdadbadjuber/anti-slop5.6k

antislop-layoutmobile

Mobile layout skill for antislop. Use for layouts that reflow across screen sizes, phone to desktop: grids, overflow, tap targets. Load with the core.

agentic-workflows

dotnet/skills5.6k

agentic-workflows

Route gh-aw workflow design/create/debug/upgrade requests to the right prompts.

create-custom-agent

dotnet/skills5.6k

create-custom-agent

Creates VS Code custom agent files (.agent.md) for specialized AI personas with tools, instructions, and handoffs. Use when scaffolding new custom agents, configuring agent workflows, or setting up agent-to-agent handoffs.

create-skill

dotnet/skills5.6k

create-skill

Scaffolds new agent skills for the dotnet/skills repository. Use when creating a new skill, generating SKILL.md files, writing a skill description that the runtime will actually route to, or setting up skill directory structures. Handles frontmatter generation, section templates, and validation guidance. Do not use for fixing a skill that already fails its evaluation (use improve-skill-quality) or for writing eval.yaml (use create-skill-test).

improve-skill-quality

dotnet/skills5.6k

improve-skill-quality

Diagnoses and fixes skills in the dotnet/skills repository that lose to their own baseline, fail to activate, time out, or return "no credible improvement". Use when an evaluation verdict is a regression or underpowered, when a skill regressed after a change, when /evaluate reports no results, or when deciding whether a weak skill should be strengthened or retired. Do not use for scaffolding a brand-new skill (use create-skill) or a brand-new eval (use create-skill-test).

vectorization

dotnet/skills5.6k

vectorization

Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including the generic Vector type, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.

customize

aipoch/open-science5.5k

customize

Use when the user wants to create or manage a Specialist agent or create, revise, publish, or delete a Skill through the conversational `/Customize` entry. Routes Skill work to the internal skill-creator and handles Specialist work through the JavaScript host.agents SDK.

scvi-tools

aipoch/open-science5.5k

scvi-tools

Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score differentially expressed genes per cluster. For spatial deconvolution / mapping use the cell2location, DestVI, or Tangram methods instead.

skill-creator

aipoch/open-science5.5k

skill-creator

Create, revise, evaluate, publish, and improve Open-Science Skills through the native JavaScript host.skills composer. Use when the user wants a reusable workflow, an existing Skill changed, test cases or benchmarks for a Skill, or better Skill triggering.

cc-workflow-ai-editor

breaking-brake/cc-wf-studio5.4k

cc-workflow-ai-editor

AI workflow editor for CC Workflow Studio. Create and edit visual AI agent workflows through interactive conversation using MCP tools (get_workflow_schema, get_current_workflow, apply_workflow, update_nodes). Use when the user wants to create a new workflow, modify an existing workflow, or edit the workflow canvas in CC Workflow Studio via the built-in MCP server.

cc-workflow-ai-editor

breaking-brake/cc-wf-studio5.4k

cc-workflow-ai-editor

next-idea

breaking-brake/cc-wf-studio5.4k

next-idea

Run one unattended IDEATION iteration of the autonomous value-creation loop — invent improvements a user of cc-wf-studio would notice, judge them against the value bar, and file the winners as locked `idea` issues. Never implements anything; the next-task skill builds from the queue this skill fills. Use when the user says "アイデア出して", "next idea", or wants proposals without implementation.

workflow-schema-tuning

breaking-brake/cc-wf-studio5.4k

workflow-schema-tuning

Use when modifying `resources/workflow-schema.json` in cc-wf-studio to influence how AI agents generate workflows via the cc-workflow-ai-editor skill. Triggers include "AIが特定のノードタイプを選んでくれない", "ワークフロー生成のバイアスを調整したい", "スキーマの description を変えたい", "新しいノードタイプを追加したい", "嘘の制約がスキーマに混じっていないか確認したい". Covers what the schema actually does (instructions to AI, not runtime constraints), the design philosophy (align direction, do not prescribe rules), the build pipeline (.json → .toon auto-generated), and known bias sources to audit.

api-shape-explorer

zebbern/claude-code-guide4.7k

api-shape-explorer

Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".

48-de-AIGC-skills

brycewang-stanford/Auto-Empirical-Research-Skills4.6k

48-de-AIGC-skills

暂无描述

auto-empirical-research-skills

brycewang-stanford/Auto-Empirical-Research-Skills4.6k

auto-empirical-research-skills

Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows, or an end-to-end run from raw data all the way to a finished Word (.docx) manuscript, without reading the entire repository at once.

Full-empirical-analysis-skill

brycewang-stanford/Auto-Empirical-Research-Skills4.6k

Full-empirical-analysis-skill

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-R

brycewang-stanford/Auto-Empirical-Research-Skills4.6k

Full-empirical-analysis-skill-R

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".

Full-empirical-analysis-skill-Stata

brycewang-stanford/Auto-Empirical-Research-Skills4.6k

Full-empirical-analysis-skill-Stata

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".