Skill-uri pentru agenți pe care le poți verifica înainte să le instalezi
Skill-uri SKILL.md publice din depozite GitHub cu peste 50 de stele și de la producători, scanate și evaluate. Instalează oricare dintre ele cu o singură comandă în Claude Code, Cursor, Codex, GitHub Copilot sau Gemini CLI.
8.895 de skill-uri din 259 de depozite · actualizat acum 20 de minute
565 de skill-uri
- aer-identificationbrycewang-stanford/Auto-Empirical-Research-SkillsUse when selecting, implementing, or stress-testing the causal identification strategy for an empirical economics manuscript — difference-in-differences (including staggered designs), instrumental variables (including weak-IV-robust inference), regression discontinuity, synthetic control, or shift-share / Bartik. Apply before writing the introduction or results.TestareFără avertismente★ 4,7 K↓ —
- d4brycewang-stanford/Auto-Empirical-Research-SkillsAgent D4 - Measurement Instrument Developer - Scale construction and psychometric validation. Covers item development, validity evidence, and reliability testing for social science research.TestareFără avertismente★ 4,7 K↓ —
- test-suite-architectzebbern/claude-code-guideThis skill should be used when establishing comprehensive QA testing processes for any software project. Use when creating test strategies, writing test cases following Google Testing Standards, executing test plans, tracking bugs with P0-P4 classification, calculating quality metrics, or generating progress reports. Includes autonomous execution capability via master prompts and complete documentation templates for third-party QA team handoffs. Implements OWASP security testing and achieves 90% coverage targets.TestareFără avertismente★ 4,7 K↓ —
- analyzebrycewang-stanford/Auto-Empirical-Research-SkillsConfirmatory hypothesis testing matched to pre-registration, with full assumption testing, effect sizes, confidence intervals, and APA 7th formatted output. Supports OLS/GLM regression, panel regression (fixest), mixed models (lme4), SEM/CFA (lavaan), meta-analysis (metafor), and delegates PROCESS models to /process-model. Reads pre-registration to align planned analyses, flags deviations, and generates decision log entries for post-hoc choices. Use when the user says "test hypotheses," "run analysis," "confirmatory," "regression," "SEM," "mediation," "mixed model," "meta-analysis," or when /eda completes. Triggers on "analyze," "hypothesis," "regression," "model," "test."TestareFără avertismente★ 4,7 K↓ —
- figure-styleaipoch/open-sciencePublication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots. Use for a figure that will ship in a report, paper, export, or kept artifact. Covers data fidelity, label economy, color threading, chart choice, layout, and render-then-verify QA without imposing a visual house style. For multi-panel composition use `figure-composer`; for whole-paper ordering use `paper-narrative`.TestareFără avertismente★ 5,3 K↓ —
- skill-creatoraipoch/open-scienceCreate, 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.TestareFără avertismente★ 5,3 K↓ —
- synthetic-controlbrycewang-stanford/Auto-Empirical-Research-SkillsEconometrics skill for Synthetic Control Method (SCM). Activates when the user asks about: "synthetic control", "SCM", "synthetic counterfactual", "donor pool", "placebo test", "in-space placebo", "in-time placebo", "MSPE ratio", "Abadie Diamond Hainmueller", "augmented synthetic control", "penalized SCM", "synthetic DID", "合成控制", "合成控制法", "捐助池", "安慰剂检验", "合成反事实", "合成DID", "政策评估"TestareFără avertismente★ 4,7 K↓ —
- aer-robustnessbrycewang-stanford/Auto-Empirical-Research-SkillsUse when the main empirical results exist but the manuscript lacks the robustness, heterogeneity, mechanism, and placebo checks that AER referees will demand. Apply after aer-identification and before aer-introduction so that the value-added paragraph can reference these tests.TestareFără avertismente★ 4,7 K↓ —
- coverage-analysisdotnet/skillsActivation requires either supplied .NET coverage reports/percentages/line, branch, or condition metrics, or an explicit request to collect .NET coverage for analysis. USE FOR: interpreting that evidence, including Cobertura data, partial conditions, plateaus, target arithmetic, project-wide coverage-backed CRAP, and coverage-backed refactoring safety. Analyze supplied reports directly without rerunning tests or installing tools. DO NOT USE FOR: requests with neither coverage evidence nor explicit coverage-collection intent, including hypothetical change-survival questions (use test-gap-analysis); CRAP or refactoring safety for one named target (use crap-score); or requests owned by test-tagging, find-untested-sources, test-anti-patterns, run-tests, or code-testing.TestareFără avertismente★ 5,7 K↓ —
- Full-empirical-analysis-skill-Statabrycewang-stanford/Auto-Empirical-Research-SkillsClassical 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/TestareFără avertismente★ 4,7 K↓ —Apare și în 1 alt depozit
- sn-motion-htmlOpenSenseNova/SenseNova-SkillsBuild continuous-shot motion-driven HTML stories from a subject or narrative, including research, story structure, consistent AI stills, Seedance scene and connector clips, interface styling, media normalization, and browser QA. Use for immersive journeys, timelines, product stories, fictional worlds, or other scene-based visual narratives whose camera should flow from beginning to end; not for ordinary autoplay video pages or general web apps.TestareFără avertismente★ 5,8 K↓ —
- sn-ppt-doctorOpenSenseNova/SenseNova-SkillsUse when diagnosing PPT Skill setup or failures involving source parsing, Playwright or Chromium rendering, HTML-to-PPTX export, Workbench startup, or bundled search and image-tool configuration.TestareFără avertismente★ 5,8 K↓ —
- stata-data-auditbrycewang-stanford/Auto-Empirical-Research-SkillsAudit datasets for structure, missingness, labeling, suspicious values, duplicate identifiers, and documentation readiness. Use when a researcher asks for data QA, codebook review, sanity checks, or pre-analysis cleanup guidance.TestareFără avertismente★ 4,7 K↓ —
- codeart2d0x0funky/agent-sprite-forgeExplicit-only last resort. Draws game art from code, with no image model - pixel sprites from a PixelSpec, flat vector props, UI and icons from portable SVG, rigged characters with planted feet, FX with a JS runtime, seam-proven autotiles, playable top-down layouts with collision, stylized parallax and ambient plate motion - palette-exact, with QA and review sheets. Use only when the user explicitly asks for code-drawn art, or when generate2dmedia route_media.py finds no image or video route (no-route, exit 3). Never the default - image generation (generate2dsprite, generate2dmap, video2dsprite) comes first for characters, props, maps and FX. Not for painterly, detailed or likeness art, HD-2D plates or realistic fire, smoke or water. Always disclose the result as code-drawn.TestareFără avertismente★ 4,5 K↓ —
- find-untested-sourcesdotnet/skillsMANDATORY for static source-to-test pairing: find or list source files/modules without corresponding tests, or suggest test locations from repository structure. Invoke even for a tiny package; do not substitute manual globbing. Uses Roslyn for C#/.NET and tree-sitter for Python, TS/JS, Go, Java, Rust, Ruby, Kotlin, Swift, PowerShell, and C++. DO NOT USE FOR: real line/branch/Cobertura data, coverage-backed test priorities, CRAP risk, or grading existing tests.TestareFără avertismente★ 5,7 K↓ —
- edabrycewang-stanford/Auto-Empirical-Research-SkillsComprehensive exploratory data analysis with publication-quality descriptive tables, correlation matrices, distribution plots, and assumption testing. Generates a standalone EDA report with Table 1 (gtsummary/great_tables), correlation heatmap, distribution diagnostics, VIF for multicollinearity, and normality/homoscedasticity tests. All figures are APA-formatted and colorblind-safe. Use when the user says "exploratory analysis," "EDA," "descriptive statistics," "explore the data," "Table 1," "correlations," "distributions," or when /data-clean completes successfully. Triggers on "EDA," "descriptive," "Table 1," "explore," "correlations."TestareFără avertismente★ 4,7 K↓ —
- event-studybrycewang-stanford/Auto-Empirical-Research-SkillsUse this skill whenever the user wants to conduct an event study, create event study plots, test for parallel trends, implement difference-in-differences designs, or work with any panel data estimation that involves pre/post treatment comparisons. Trigger on phrases like "event study", "parallel trends", "pre-trends", "dynamic treatment effects", "leads and lags", "TWFE", "two-way fixed effects", "staggered adoption", "staggered treatment", "difference-in-differences", "DiD", "Sun and Abraham", "Callaway and Sant'Anna", "de Chaisemartin", "Borusyak", "did_multiplegt", "fixest", "did2s", "bacon decomposition", or any reference to plotting coefficients around a treatment event. Also trigger when the user uploads panel data and wants to estimate treatment effects with variation in treatment timing. All code is in R.TestareFără avertismente★ 4,7 K↓ —
- iv-estimationbrycewang-stanford/Auto-Empirical-Research-SkillsEconometrics skill for instrumental variables and treatment effect estimation. Activates when the user asks about: "instrumental variables", "IV estimation", "2SLS", "two-stage least squares", "endogeneity", "weak instruments", "first stage", "Sargan test", "overidentification", "propensity score matching", "PSM", "average treatment effect", "ATT", "LATE", "local average treatment effect", "endogenous regressor", "instrument validity", "工具变量", "两阶段最小二乘", "内生性", "弱工具变量", "倾向得分匹配", "平均处理效应", "处理效应", "局部平均处理效应"TestareFără avertismente★ 4,7 K↓ —
- rdd-analysisbrycewang-stanford/Auto-Empirical-Research-SkillsEconometrics skill for Regression Discontinuity Design (RDD). Activates when the user asks about: "regression discontinuity", "RDD", "RD design", "sharp RDD", "fuzzy RDD", "running variable", "forcing variable", "cutoff", "bandwidth selection", "local linear regression", "McCrary test", "density test", "RDROBUST", "continuity assumption", "donut hole RDD", "geographic RDD", "断点回归", "回归不连续", "运行变量", "截断值", "带宽选择", "精确断点", "模糊断点", "密度检验", "局部线性回归"TestareFără avertismente★ 4,7 K↓ —
- time-seriesbrycewang-stanford/Auto-Empirical-Research-SkillsEconometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"TestareFără avertismente★ 4,7 K↓ —
- wiki-agents-mdmicrosoft/skillsGenerates AGENTS.md files for repository folders — coding agent context files with build commands, testing instructions, code style, project structure, and boundaries. Only generates where AGENTS.md is missing.OficialTestareVerifică întâi★ 3,1 K↓ —
- game-theorybrycewang-stanford/Auto-Empirical-Research-SkillsThis skill covers game-theoretic methods in structural econometrics and industrial organization. Use when the user is working with strategic interactions, equilibrium analysis, or game-theoretic structural models — including entry games, conduct testing, auction models with strategic bidding, bargaining, or matching markets. Triggers on "Nash equilibrium", "subgame perfect", "best response", "strategic interaction", "entry game", "conduct testing", "auction", "mechanism design", "matching market", "bargaining", "BNE", "Bayesian Nash", "static game", "dynamic game", "repeated game", "multiple equilibria", "equilibrium selection", "discrete game", "oligopoly", "game-theoretic", "player", "payoff", "strategy", "dominant strategy", "Bresnahan-Reiss", "Ciliberto-Tamer", "partial identification", "set identification", or markup test.TestareFără avertismente★ 4,7 K↓ —
- panel-databrycewang-stanford/Auto-Empirical-Research-SkillsEconometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"TestareFără avertismente★ 4,7 K↓ —
- http-load-profilerzebbern/claude-code-guideRun stepped HTTP load tests with ab/wrk, ramping concurrency levels to collect p50/p90/p99 latency, detect performance inflection points, and recommend optimal concurrency. Triggered by requests like 'load test this URL', 'benchmark my API', 'find the max concurrency', or mentions of p99 latency, throughput saturation, or capacity planning.TestareFără avertismente★ 4,7 K↓ —
event-study
brycewang-stanford/Auto-Empirical-Research-Skills
TestareActualizat alaltăieri
De la autor
Use this skill whenever the user wants to conduct an event study, create event study plots, test for parallel trends, implement difference-in-differences designs, or work with any panel data estimation that involves pre/post treatment comparisons. Trigger on phrases like "event study", "parallel trends", "pre-trends", "dynamic treatment effects", "leads and lags", "TWFE", "two-way fixed effects", "staggered adoption", "staggered treatment", "difference-in-differences", "DiD", "Sun and Abraham", "Callaway and Sant'Anna", "de Chaisemartin", "Borusyak", "did_multiplegt", "fixest", "did2s", "bacon decomposition", or any reference to plotting coefficients around a treatment event. Also trigger when the user uploads panel data and wants to estimate treatment effects with variation in treatment timing. All code is in R.
Instalare
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill event-study -a claude-code
Se instalează în .claude/skills/event-study/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Fără descărcare: licența acestui skill (Personalizată sau neclară) nu ne permite să-i redistribuim fișierele. Comenzile de instalare le iau direct de pe GitHub.
La ce are acces
Doar instrucțiuniNu am văzut acces la rețeaLicență: Personalizată sau neclară
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De ce 85
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Proveniență84
Întreținere100
Licență45
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Fișiere (4)
- SKILL.md
- diagnostics-and-testing.md
- modern-extensions.md
- traditional-event-study.md