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.
8.582 de skill-uri din 252 de depozite · actualizat acum 22 de minute
486 de skill-uri
- causal-inference-mixtapebrycewang-stanford/Auto-Empirical-Research-SkillsThis skill should be used when the user asks to "implement a DiD regression", "write a causal inference pipeline", "set up an event study", "implement instrumental variables", "run a regression discontinuity design", "build a synthetic control model", "implement propensity score matching", "write parallel trends test", "implement Bacon decomposition", or needs code templates for causal inference methods in Python, R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape.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↓ —
- regression-modelerzebbern/claude-code-guideRun regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation. Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.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↓ —
- Full-empirical-analysis-skillbrycewang-stanford/Auto-Empirical-Research-SkillsClassical 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 scafTestareFără avertismente★ 4,7 K↓ —Apare și în 1 alt depozit
- 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
- 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↓ —
- tdd-workflowbrycewang-stanford/Auto-Empirical-Research-SkillsTest-driven development workflow for R using testthat. Use when writing new features, fixing bugs, or refactoring code. Enforces test-first development with 80%+ coverage.TestareFără avertismente★ 4,7 K↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- robustnessbrycewang-stanford/Auto-Empirical-Research-SkillsChecklist of empirical robustness tests for finance/economics papersTestareFără avertismente★ 4,7 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- next-qa-ideabreaking-brake/cc-wf-studioRun one unattended IDEATION iteration of the quality-assurance loop — find the highest-value untested behavior in the codebase, judge it against the QA value bar, and file ONE locked `qa` issue specifying the test to write. Never writes code or tests; the next-qa skill builds from the queue this skill fills. Use when the user says "QAアイデア", "next qa idea", or wants the QA backlog refilled without implementation.TestareFără avertismente★ 5,4 K↓ —
- next-qabreaking-brake/cc-wf-studioRun one unattended iteration of the QUALITY-ASSURANCE loop — steward any in-flight QA PR, then build ONE queued `qa` issue (test infrastructure, unit tests, regression tests for known bugs) on a branch off auto-qa and open a PR that squash-merges on green CI. Adds tests and tooling only; never edits product source. Use when the user says "QAタスク", "next qa", "テストを進めて", or wants autonomous progress on the quality track.TestareFără avertismente★ 5,4 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↓ —
- generate2dmedia0x0funky/agent-sprite-forgeThe art-route layer of the Forge skills. route_media.py is the one command that generates an image or an image-to-video clip - through the paid API of any provider whose key is configured (OpenAI, Google Gemini, xAI, BytePlus ModelArk, fal.ai; the configured key is the owner's consent), else through the user's own signed-in Codex or Grok CLI - and prints one JSON line with the route, artifact, sha256 and cost estimate; with no route it answers no-route (codeart2d is then the last resort). Also the capability check (forge_doctor), API key configuration, the spend ledger, resume and Codex image adoption. Use when a Forge skill sends a generation here, for the capability check, or when the user asks about keys, routes or spend. Not for planning, processing or QA of sprites, video frames or maps (generate2dsprite, video2dsprite, generate2dmap) or for code-drawn art (codeart2d).TestareFără avertismente★ 4,5 K↓ —