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 8 minute
565 de skill-uri
- test-driven-developmenta5c-ai/babysitterStrict RED-GREEN-REFACTOR cycle enforcement. Tests are never skipped or deferred. Run mode only, never watch mode. Exit code evidence mandatory.TestareFără avertismente★ 1,9 K↓ —
- ab-testinga5c-ai/babysitterA/B testing skill for player segmentation.TestareFără avertismente★ 1,9 K↓ —
- test-driven-developmenta5c-ai/babysitterTest-first development practice where test specifications are written before production code, integrated into plan tasks as mandatory first sub-steps.TestareFără avertismente★ 1,9 K↓ —
- YARA Rules Skilla5c-ai/babysitterYARA rule creation, testing, and deploymentTestareFără avertismente★ 1,9 K↓ —
- websocketa5c-ai/babysitterSpecialized skill for WebSocket protocol implementation and testing. Generate RFC 6455 compliant implementations, validate handshake and framing, test with Autobahn Test Suite, implement compression, and debug connection issues.TestareFără avertismente★ 1,9 K↓ —
- screen-readera5c-ai/babysitterScreen reader compatibility, testing, and optimization.TestareFără avertismente★ 1,9 K↓ —
- acquisition-channel-advisordeanpeters/Product-Manager-SkillsEvaluate acquisition channels using unit economics, customer quality, and scalability. Use when deciding whether to scale, test, or kill a growth channel.TestareFără avertismente★ 7,6 K↓ —
- derisk-measurement-advisordeanpeters/Product-Manager-SkillsIdentify what to measure, test, or track to de-risk a product or AI idea. Use when stress-testing an idea across internal (DUFV) and external (PESTEL) dimensions.TestareFără avertismente★ 7,6 K↓ —
- opportunity-solution-treedeanpeters/Product-Manager-SkillsBuild an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.TestareFără avertismente★ 7,6 K↓ —
- pol-probedeanpeters/Product-Manager-SkillsDefine a Proof of Life probe to test a risky hypothesis cheaply. Use when you need harsh truth before building real product.TestareFără avertismente★ 7,6 K↓ —
- drawio-skillAgents365-ai/drawio-skillCreate and edit draw.io/diagrams.net diagrams as editable `.drawio` files. Covers architecture, UML/ERD/sequence, BPMN, network, and swimlane views authored from a description or converted from code, IaC, SQL, and API schemas, plus sync, query, test, review, export, and publish of existing diagrams. Use when the user asks for draw.io/diagrams.net or an editable diagram; prefer Mermaid/PlantUML when diagrams-as-code is enough.TestareFără avertismente★ 10,3 K↓ —
- daytona-electron-denDevin-AXIS/iPolloWorkElectron and Den, desktop plus cloud, two-sandbox e2e, cloud auth, marketplace, org policy, worker proxy, provider sync, desktop handoff. Validate Electron against a Daytona Den server with unified proof.TestareFără avertismente★ 7,1 K↓ —
- daytona-cloud-serverDevin-AXIS/iPolloWorkDaytona cloud server, Den sandbox, desktop plus cloud e2e, marketplace server, worker proxy, cloud auth, org policies, connect Electron to Den. Use for server-side setup in validated flows.TestareFără avertismente★ 7,1 K↓ —
- run-evalsDevin-AXIS/iPolloWorkdo e2e tests, run e2e, validate feature, prove it works, PR proof, frame proof, pnpm evals. Launches iPolloWork on Daytona or local Electron and runs the coded eval flows via CDP. Launch + run mechanics; the proof loop itself is the fraimz skill.TestareFără avertismente★ 7,1 K↓ —
- fraimzDevin-AXIS/iPolloWorkcreate a fraimz, make fraimz, prove it works, frame proof, PR proof, validate experience, e2e evidence, fraimz.html. The full fraimz loop — frame the claim, drive the real app via CDP, validate/repair, output fraimz.html. Use whenever a task ends with "please create a fraimz" or any change needs end-to-end proof.TestareFără avertismente★ 7,1 K↓ —
- robustnessbrycewang-stanford/Auto-Empirical-Research-SkillsChecklist of empirical robustness tests for finance/economics papersTestareFără 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
- 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↓ —
- generate2dsprite0x0funky/agent-sprite-forgeMake game-ready 2D characters, creatures, props, icons and FX as master stills, sprite sheets or animation clips. Plan size, motion and finish; generate the art (image generation is the default, through generate2dmedia route_media.py - the API when a key is configured, else the local Codex or Grok CLI); approve one master still per character; then key, slice, register, scale, palette, QA and export frames for a game engine (Aseprite JSON, Godot SpriteFrames or Sprite3D). HD finish by default, pixel finish on request. Character animation is one image-to-video clip per action from the master (video2dsprite); sheets are for FX, icons and props. Use for sprites, masters, sheets, frame-by-frame animation and packaging frames from any source. Not for maps, tiles or backgrounds (generate2dmap), video clips (video2dsprite), code-drawn art (codeart2d, only on request) or calling an image or video API by itself (generate2dmedia).TestareFără avertismente★ 4,5 K↓ —
- 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↓ —
- e1brycewang-stanford/Auto-Empirical-Research-SkillsE1-Quantitative Analysis Guide with Code Generation & Sensitivity Analysis VS-Enhanced with Full 5-Phase process: Avoids obvious analyses, explores innovative methodologies Expanded to include qualitative analysis (thematic, grounded theory, content, narrative) Absorbed E4 (Analysis Code Generator) and E5 (Sensitivity Analysis - Primary Study) capabilities Use when: selecting statistical/qualitative methods, interpreting results, checking assumptions, generating code, sensitivity analysis Triggers: statistical analysis, ANOVA, regression, t-test, power analysis, assumption checking, effect size, thematic analysis, grounded theory, content analysis, narrative analysis, NVivo, ATLAS.ti, coding, qualitative data, R code, Python code, SPSS syntax, sensitivity analysis, robustness checkTestareFără 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↓ —
- 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↓ —
- 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↓ —
Full-empirical-analysis-skill-Stata
brycewang-stanford/Auto-Empirical-Research-Skills
TestareActualizat alaltăieri
De la autor
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/
Instalare
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-Stata -a claude-code
Se instalează în .claude/skills/Full-empirical-analysis-skill-Stata/ · 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
Adopție58
Fișiere (10)
- README.md
- SKILL.md
- references/01-data-cleaning.md
- references/02-data-transformation.md
- references/03-descriptive-stats.md
- references/04-statistical-tests.md
- references/05-modeling.md
- references/06-robustness.md
- references/07-further-analysis.md
- references/08-tables-plots.md