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.
45.604 skill-uri din 2.205 depozite · actualizat acum 7 ore
45.604 skill-uri
- agentic-workflowsdotnet/skillsRoute gh-aw workflow design/create/debug/upgrade requests to the right prompts.DesignFără avertismente★ 5,7 K↓ —
- code-testingdotnet/skillsALWAYS USE for test work that requires changes: write, add, generate, repair, or strengthen tests for existing code in xUnit, MSTest, NUnit, pytest, Vitest/Jest, Go, or another framework. Includes regression cases, failing or flaky tests, coverage-driven additions, and audit-then-fix requests. Focused work stays direct; broad or multi-stage work invokes test-engineer. DO NOT USE for only running tests, analysis-only audits, framework/platform migrations, a test blocked on a missing production seam (testability-obstacle), or MSTest API/configuration corrections that do not design new cases (writing-mstest-tests). Within an active test-engineer pipeline, reuse supplied guidance and do not re-enter this skill.TestareFără avertismente★ 5,7 K↓ —
- uitw93/WazaProduces distinctive production UI and screenshot-grounded visual polish. Use when building or restyling pages, components, or typography. Not for backend logic or data pipelines.DesignFără avertismente★ 7,4 K↓ —Apare și în 1 alt depozit
- dowhybrycewang-stanford/Auto-Empirical-Research-SkillsCausal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"TestareFără avertismente★ 4,8 K↓ —
- replica-architectJakeschincariol/replica-skillPlans the stack, database schema and API for an app clone, from the recon map replica-recon wrote. Picks boring, managed tech, turns the inferred data model into real SQL with indexes and access rules, lists every route by flow, and orders the build as a thin vertical slice first. Use when the user says "plan the clone", "what stack should I use", "design the database", "write the schema", "plan the API", "architecture for my version of X", or after /replica-recon finishes.PlanificareFără avertismente★ 5,4 K↓ —
- replica-reconJakeschincariol/replica-skillReverse-engineers any app into a recon map: screen inventory, user flows, component list, inferred data model and a feature matrix, from public pages, screenshots, app store listings, help docs and the user's own account. The first step of the Replica pack. Use when the user says "clone this app", "reverse engineer X", "how does X work", "map out X", "what screens does X have", "I want to build my own version of X", "copy this app", or pastes an app's URL or App Store link and wants to rebuild it.AlteleFără avertismente★ 5,4 K↓ —
- authoring-github-workflowsdotnet/skillsAuthor and review GitHub Actions workflow YAML safely so syntactically-valid YAML can't ship a workflow that GitHub Actions refuses to run. USE FOR: editing, adding, or reviewing any file under .github/workflows/, writing run-name/name/if/env/run values that contain ${{ }} expressions, diagnosing a run that fails with 'This run likely failed because of a workflow file issue' and no jobs starting, deciding when a workflow scalar must be quoted, validating workflows with actionlint. DO NOT USE FOR: authoring application YAML unrelated to GitHub Actions, Azure Pipelines, GitLab CI, or non-workflow YAML. SCOPE: this skill covers *syntactic/structural* correctness of workflow YAML (quoting, parsing, actionlint); for *semantic and functional* workflow design (what a workflow should do, agentic-workflow behavior), see .github/agents/agentic-workflows.agent.md — the two are complementary. INVOKES: actionlint (downloaded pinned binary) plus git/grep for inspection.DevOpsFără avertismente★ 5,7 K↓ —
- large-file-kpi-analysisOpenSenseNova/SenseNova-Skills根据数据量自动选择读取策略（大文件转Parquet），提取关键指标进行单位一致性验证与排序分析，并输出可下载的结果表格。AlteleFără avertismente★ 5,9 K↓ —
- category-filtering-and-difficulty-analysisOpenSenseNova/SenseNova-Skills对Excel数据进行自定义分类统计、交叉分析与可视化，并基于多维度指标（如文本长度、术语密度、正则匹配等）进行综合评分与分级，适用于多类别数据分布统计及文本内容难度/质量评估场景。DocumenteFără avertismente★ 5,9 K↓ —
- excel-basic-statistics-and-routingOpenSenseNova/SenseNova-Skills对多Sheet Excel文件进行基础统计与，支持按条件筛选计算均值，以及从指定行区间提取数据去重求和，并生成结果文件与下载链接。DocumenteFără avertismente★ 5,9 K↓ —
- excel-large-file-processing-and-cleaningOpenSenseNova/SenseNova-Skills读取多 sheet Excel 文件，动态识别目标列进行统计，并使用正则清洗文本字段提取中文字符，最终输出标准化 Excel 文件。DocumenteFără avertismente★ 5,9 K↓ —
- top-value-coloringOpenSenseNova/SenseNova-Skills根据数据规模动态选择处理策略，对多表数据进行合并、统计筛选，并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。AlteleFără avertismente★ 5,9 K↓ —
- text-normalization-and-large-file-processingOpenSenseNova/SenseNova-Skills对Excel文件进行文本标准化清洗（如去除异常前缀、提取纯中文字符等），并，最终输出清洗后的Excel文件并提供下载链接。DocumenteFără avertismente★ 5,9 K↓ —
- replica-deployJakeschincariol/replica-skillShips an app clone live on the user's own domain: a preflight gate (tests green, parity must-haves done, rebrand sweep clean, listing linted, legal pages up), production database and env vars, the host, DNS records for the domain and for email, Stripe live mode, OAuth redirects, monitoring, and mobile builds to TestFlight and Play. Use when the user says "deploy it", "ship it", "put it live", "connect my domain", "go to production", "publish the app", or after /replica-launch.DevOpsFără avertismente★ 5,4 K↓ —
- replica-backendJakeschincariol/replica-skillBuilds the backend of an app clone: auth, database migrations and access rules, payments with Stripe, email, background jobs and third-party integrations through official APIs only, plus a security checklist. Use when the user says "add login", "set up auth", "wire up the database", "add payments", "connect Stripe", "add Google Calendar", "send emails", "backend for my clone", or when /replica-build is running on fake data.SecuritateFără avertismente★ 5,4 K↓ —
- avoid-ai-writingbrycewang-stanford/Auto-Empirical-Research-SkillsAudit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detection-only mode that flags patterns without rewriting.RedactareFără avertismente★ 4,8 K↓ —
- keyword-researchRyze-AI-Adgent/open-seo-mcp-skillsKeyword research from a seed topic — ideas, real Google volume/CPC, intent, difficulty, clustered into a plan. Use when asked to "find keywords", "keyword research for X", "what should I rank for", or "build a keyword plan".ȘtiințăFără avertismente★ 7,1 K↓ —
- humanize-chinesebrycewang-stanford/Auto-Empirical-Research-SkillsDetect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC scRedactareFără avertismente★ 4,8 K↓ —
- deepspeedOrchestra-Research/AI-Research-SKILLsExpert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attentionAlteleFără avertismente★ 13,9 K↓ —
- lambda-labs-gpu-cloudOrchestra-Research/AI-Research-SKILLsReserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.AlteleFără avertismente★ 13,9 K↓ —
- sentencepieceOrchestra-Research/AI-Research-SKILLsLanguage-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.ProductivitateFără avertismente★ 13,9 K↓ —
- openrlhf-trainingOrchestra-Research/AI-Research-SKILLsHigh-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.PlanificareFără avertismente★ 13,9 K↓ —
- vitepressantfu/skillsVitePress static site generator powered by Vite and Vue. Use when building documentation sites, configuring themes, or writing Markdown with Vue components.RedactareFără avertismente★ 6 K↓ —
- whisperOrchestra-Research/AI-Research-SKILLsOpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.MediaFără avertismente★ 13,9 K↓ —
three-best-practices
zebbern/claude-code-guide
MediaActualizat alaltăieri
De la autor
Three.js performance optimization and best practices guidelines. Use when writing, reviewing, or optimizing Three.js code. Triggers on tasks involving 3D scenes, WebGL/WebGPU rendering, geometries, materials, textures, lighting, shaders, or TSL.
Instalare
npx skills add zebbern/claude-code-guide --skill three-best-practices -a claude-code
Se instalează în .claude/skills/three-best-practices/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Descarcă .zip
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
La ce are acces
Doar instrucțiuniNu am văzut acces la rețeaLicență: MIT
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 91
Securitate100
Proveniență84
Întreținere100
Licență100
Adopție58
Fișiere (29)
- SKILL.md
- rules/_sections.md
- rules/animation-system.md
- rules/asset-compression.md
- rules/audio-spatial.md
- rules/core-web-vitals.md
- rules/debug-devtools.md
- rules/draw-call-optimization.md
- rules/error-handling-recovery.md
- rules/geometry-instanced-mesh.md
- rules/gltf-loading-optimization.md
- rules/lighting-shadows-advanced.md
- încă 17