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 5 ore
45.604 skill-uri
- deepchat-sddThinkInAIXYZ/deepchatUse before substantial DeepChat code, configuration, documentation, test, build, feature, issue, refactor, or architecture changes that need a durable RFC and an explicit execution path. Skip trivial style fixes, small localized logic changes, routine docs edits, and simple bugs unless the developer asks for SDD. Use plan.md as the only separate tracker when needed, default to implementation-first validation, and ask before optional GitHub issue sync unless the developer explicitly requested sync.TestareFără avertismente★ 6,4 K↓ —
- code-reviewThinkInAIXYZ/deepchatComprehensive code review assistant that analyzes code quality, security, and best practicesInginerieFără avertismente★ 6,4 K↓ —
- designlang-tokensManavarya09/design-extractUse when styling UI for framer.com — references the extracted design system tokens instead of inventing colors, spacing, or typography.DesignFără avertismente★ 4,4 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↓ —
- 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↓ —
- diffdockaipoch/open-sciencePredict small-molecule binding poses with DiffDock-L (Corso et al. 2023/2024, github.com/gcorso/DiffDock) — blind diffusion docking that places a ligand into a protein pocket without a predefined search box and ranks the samples with a learned confidence model. Reach for this skill to dock a SMILES or SDF against a PDB, to generate ranked 3D poses for a small fragment library, or to get a starting pose for downstream rescoring. DiffDock predicts geometry, not affinity.ȘtiințăFără avertismente★ 5,4 K↓ —
- mongodb-schema-designmongodb/agent-skillsMongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".OficialDesignFără avertismente★ 190↓ —Apare și în 3 alte depozite
- trend-analysisOpenSenseNova/SenseNova-Skills基于多维度数据进行分级评估与趋势预测,通过设定差异化增长率计算预测值,并生成对比可视化图表,适用于绩效评估、目标设定等场景。AlteleFără avertismente★ 5,9 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↓ —
- humanize-redoepoko77-ai/im-not-ai가장 최근 윤문 결과를 2차로 다시 다듬는다 — 특정 카테고리·문단·강도 조정도 가능. humanize-korean strict 윤문(Phase B)을 기존 run_id에 재실행해 잔존 finding을 처리한다. 트리거 — "/humanize-redo".RedactareFără avertismente★ 6,2 K↓ —Apare și în 1 alt depozit
- 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↓ —
- 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↓ —
- prometheus-cardinality-troubleshootergrafana/skillsDiagnostic guide for active Prometheus cardinality problems — slow queries, OOMing Prometheus, high Grafana Cloud Active Series or DPM bills, "too many samples" ingest errors, series churn, or rapid memory growth. Walks through tsdb status endpoints, per-metric and per-label drill-downs, common-culprit galleries, and remediation paths. Use when the user is *currently experiencing* a cardinality fire. For preventing cardinality issues at the source, route to prometheus-label-strategy. For post-ingest aggregation, route to adaptive-metrics. For DPM-specific analysis, route to dpm-finder.OficialMarketingFără avertismente★ 290↓ —
- datasources-provisioninggrafana/skillsGenerate a copy-paste Grafana data source provisioning file (YAML or Terraform) for any plugin from its standardized settings schema on the plugins CDN. Use when the user wants to provision or configure a data source as code — e.g. "provision infinity", "datasource yaml for clickhouse", "terraform for the github datasource" — even when they only name the plugin and not the word "provisioning".OficialDevOpsFără avertismente★ 290↓ —
- profilecli-insightsgrafana/skillsQuery live Pyroscope profiles with profilecli, analyze them with pprof, and correlate hot functions with checked-out source code. Use when the user asks to investigate a service with a configured Pyroscope server, profilecli, and pprof.OficialFinanțeFără avertismente★ 290↓ —
- admission-controlgrafana/skillsUse when the user asks to "write a validator", "add validation", "implement admission control", "write a mutating webhook", "add a mutation handler", "validate incoming resources", "implement admission logic", "add admission webhooks", "write ingress validation", or asks how to validate or mutate resources before they are persisted in a grafana-app-sdk app. Provides guidance on implementing validation and mutation admission handlers for grafana-app-sdk apps.OficialRedactareFără avertismente★ 290↓ —
- synthetic-monitoring-checksgrafana/skillsAuthor Grafana Cloud Synthetic Monitoring checks, with deep coverage of k6 scripted and browser checks: SM's single-VU/single-iteration execution model, assertions that actually fail probe_success (expect() and fail() vs bare check()), secrets, deterministic scripts, robust browser locators, local validation with k6 run, deployment via UI/API/Terraform, verifying probe_success, and rollback. Also helps choose the simplest sufficient check type (HTTP/ping/DNS/TCP, MultiHTTP, scripted, browser). Use when writing a synthetic check, monitoring a login/checkout/signup flow in production, converting a k6 script or an OpenAPI spec into a check, authoring a browser check, validating a user journey, or asking "is my site up from multiple regions". NOT for load, stress, or performance testing — SM runs one iteration per execution; for load tests use the grafana-k6 plugin or Grafana Cloud k6. For the broad Grafana Cloud Testing overview (SM + k6 Cloud + Faro), use the testing skill.OficialTestareFără avertismente★ 290↓ —
- cloud-integrationsgrafana/skillsSet up, configure, and troubleshoot Grafana Cloud integrations for AWS, Azure, and other cloud providers. Use when the user asks to connect AWS CloudWatch, set up Azure Monitor, configure Confluent Cloud observability, install a Grafana integration, set up hosted exporters, use AWS Firehose for CloudWatch logs, or troubleshoot a cloud integration. Triggers on phrases like "AWS CloudWatch", "Azure Monitor", "Confluent integration", "cloud integration", "hosted exporter", "AWS Firehose", "install integration", "cloud metrics", or "cloud logs".OficialDevOpsFără avertismente★ 290↓ —
- app-observabilitygrafana/skillsGet RED metrics + service maps + frontend RUM + AI/LLM monitoring out of Grafana Cloud — Application Observability (`traces_spanmetrics_*` from OTel traces, p50/p95/p99 latency, exemplar-to-trace, traces-to-logs / profiles), Frontend Observability with the Faro Web SDK (Core Web Vitals, session replay, `pushError`, React + router integration, `TracingInstrumentation` for browser → backend trace correlation), and AI Observability via OpenLIT (token / cost / latency, GPU, hallucination + toxicity evals). Use when standing up APM for a service, wiring an Alloy OTLP receiver + forwarding to Cloud, instrumenting a React frontend for RUM, debugging why service-map edges are missing, monitoring LLM cost drift, or correlating a frontend error to its backend trace — even when the user says "set up APM", "show service map", "monitor browser perf", "session replay", "RUM SDK", or "watch our OpenAI bill" without naming App / Frontend / AI Observability.OficialDevOpsFără avertismente★ 290↓ —
- grafana-scenesgrafana/skillsBuild Grafana plugin pages using the @grafana/scenes framework. Use when creating new scene pages, adding panels/visualizations, setting up drilldown navigation, defining variables, configuring query runners, building table/timeseries/stat panels, or extending SceneObjectBase for custom scene objects. Triggers on any work involving SceneApp, SceneAppPage, EmbeddedScene, SceneQueryRunner, SceneDataTransformer, PanelBuilders, SceneFlexLayout, QueryVariable, or drilldown/tab configuration in Grafana plugins.OficialAlteleFără avertismente★ 290↓ —
- app-sdk-conceptsgrafana/skillsUse when starting any grafana-app-sdk work — scaffolding a Grafana app, initializing a Grafana App Platform app, picking a deployment mode (standalone operator / grafana/apps / frontend-only), wiring app-specific config, or onboarding to the SDK. Covers `grafana-app-sdk` CLI install, `project init` per deployment mode, project layout, the schema-centric workflow (CUE kinds → generated code → reconciler/admission logic), and `SpecificConfig` for env-driven app configuration. Use even if the user just says "make a new app", "Grafana app platform", "kinds and watchers", "operator scaffold", or asks about `apps/` inside the Grafana repo, without naming the SDK explicitly.OficialDevOpsFără avertismente★ 290↓ —
- k6-test-maintenancegrafana/skillsMaintain and improve existing k6 test scripts. Covers threshold tightening based on trend data, version migration between k6 releases, auto-fixing tests when the underlying service changes, refactoring for cleanliness, and auditing scripts against current best practices from docs. Use when the user asks to fix a failing k6 test, tighten thresholds, migrate a script to a new k6 version, refactor a test, update a script after a service change, or improve a script with best practices. Trigger on phrases like "fix my k6 test", "tighten my thresholds", "migrate to k6 v2", "update my test script", "refactor this k6 test", "my test is failing after a deploy", "apply best practices to my script", "modernize my k6 test", or "the service changed and my test broke". Also trigger when another skill (k6-trend-analysis or k6-cloud-investigate-test) hands off with a recommendation to edit a script. Maintains existing scripts; to author a new test use the k6 skill, or k6-browser-test for a browser test.OficialTestareFără avertismente★ 290↓ —
- melonjs-particles-and-trailsActualizatmelonjs/melonJSUse this skill for particle effects and motion trails in melonJS — ParticleEmitter configuration, burst versus stream emission, the referenceSpace frame of reference introduced in 20.2, 3D particle motion through elevation and minSpread/maxSpread in 20.8, and the Trail renderable. Covers the emitter that silently does nothing until you call an emit method. Triggers on: Trail3d, ribbon, exhaust trail, speed streak, ski track, tyre mark, ribbonNormal, ParticleEmitter, particles, burstParticles, streamParticles, referenceSpace, ParticleEmitterSettings, elevation, minSpread, maxSpread, spawn area, Trail, explosion, smoke, sparks, debris, ring, exhaust, emitter, tintOverLife, colour over life, color over life, flame, fire.MediaFără avertismente★ 6,4 K↓ —
designlang-tokens
Manavarya09/design-extract
De la autor
Use when styling UI for duolingo.com — references the extracted design system tokens instead of inventing colors, spacing, or typography.
Instalare
npx skills add Manavarya09/design-extract --skill designlang-tokens -a claude-code
Se instalează în .claude/skills/designlang-tokens/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
La ce are acces
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 91
Fișiere (1)
- SKILL.md