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 21 de ore
45.604 skill-uri
- 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↓ —
- your-skill-namegrafana/skillsClear description of what this skill does and when to use it. Use when the user asks about X or wants to work with Y. Include specific trigger phrases so agents auto-load it correctly. Max 1024 characters.OficialPlanificareFără avertismente★ 290↓ —
- linkedin-reply-handlersergebulaev/linkedin-skillsDraft a reply to one LinkedIn comment from its URL, or sweep a whole thread from just the post URL and draft a reply to every comment worth answering, in one batch. Use for replying to a comment, following an author reply, or clearing all comments on a post. Resolves the correct parentComment (LinkedIn flattens threads to 2 levels), filters low-value comments before a sweep, and posts via Publora on approval. Not for top-level comments (use linkedin-comment-drafter).MarketingFără avertismente★ 5,2 K↓ —Apare și în 1 alt depozit
- linkedin-employee-advocacysergebulaev/linkedin-skillsStand up and run a LinkedIn employee advocacy program for a marketing or sales team. Covers 14-day launch playbook, brand-guideline governance, per-post time budget, cadence benchmarks, and team ROI (reach, engagement, pipeline). Triggers on "employee advocacy", "get the team posting", "scale LinkedIn across team", "advocacy ROI". Not for planning one person's own calendar (use linkedin-content-planner).MarketingFără avertismente★ 5,2 K↓ —Apare și în 1 alt depozit
- fleet-managementgrafana/skillsManage a fleet of Grafana Alloy collectors with Fleet Management — author Alloy pipelines once, target them via attribute matchers (`env="production"`, regex `region=~"us-.*"`), push remotely via OpAMP without restarting collectors. Covers pipeline create / update / matcher RPCs, collector attribute API, `remotecfg` bootstrap block (standalone + Helm), pre-deploy `alloy fmt` validation, the local Alloy UI at port 12345 for component health, and post-deploy `REMOTE_CONFIG_STATUS_APPLIED` verification. Use when standing up a Cloud Alloy fleet, pushing a config change to 200 collectors, hunting why one collector shows `REMOTE_CONFIG_STATUS_FAILED`, validating River syntax before saving, or wiring `discovery.kubernetes` → `prometheus.remote_write` — even when the user says "configure Alloy", "remote config the collectors", "push pipeline", "OpAMP", "collector is unhealthy", or "manage agent config centrally" without naming Fleet Management.OficialDevOpsFără avertismente★ 290↓ —
- linkedin-interviewersergebulaev/linkedin-skillsInterview the user for the raw material their posts are made of. Builds a lasting Story Bank of roles, numbers, turning points, scars and positions, or runs a focused interview that turns one topic into a post spine. Use when a draft has nothing concrete to draw on, or the user says interview me. Not for learning how they write (use linkedin-humanizer --mode profile).MarketingFără avertismente★ 5,2 K↓ —Apare și în 1 alt depozit
- playwright-cizebbern/claude-code-guideProduction-ready CI/CD configurations for Playwright — GitHub Actions, GitLab CI, CircleCI, Azure DevOps, Jenkins, Docker, parallel sharding, reporting, code coverage, and global setup/teardown.TestareFără avertismente★ 4,7 K↓ —
- opentelemetrygrafana/skillsInstrument any app with OpenTelemetry and ship metrics / logs / traces to Grafana Cloud or self-hosted Mimir / Loki / Tempo / Pyroscope. Covers SDK auto-instrumentation for Go, Java (Grafana JVM agent), Python (`opentelemetry-instrument`), Node.js, .NET (`Grafana.OpenTelemetry`), Beyla eBPF for zero-code; Grafana Cloud OTLP gateway + Basic-auth (instanceID + API key, base64); env-var config (`OTEL_EXPORTER_OTLP_*`, `OTEL_RESOURCE_ATTRIBUTES`); Alloy / OTel-Collector pipelines; Kubernetes Operator inject-annotations; and head + tail sampling. Use when instrumenting a service, pointing OTLP at Grafana Cloud, switching from Jaeger / Datadog / New Relic, choosing head- vs tail-sampling, or debugging "spans aren't showing in Explore" — even when the user says "auto-instrument my Java app", "send traces to Grafana", "what env vars do I set", "OTLP endpoint", or "Operator inject" without naming OpenTelemetry.OficialDevOpsFără avertismente★ 290↓ —
- tempografana/skillsStand up Grafana Tempo as a cost-efficient distributed-tracing backend that only needs object storage, and write TraceQL queries against it. Covers OTLP + Jaeger + Zipkin ingestion, the distributor → live-store → block-builder → object-storage write path, metrics-generator for RED spanmetrics + service graphs, Helm `tempo-distributed` deployment, multi-tenant `X-Scope-OrgID`, TraceQL span / resource / event scopes, structural operators (`>>`, `<<`), `rate()` + `quantile_over_time` metrics, and the traces-to-logs / metrics / profiles datasource links. Use when deploying Tempo, writing a TraceQL query for slow / errored requests, debugging "no traces showing in Explore", sizing queriers / compactors, configuring S3 / GCS / Azure block storage, or wiring trace ↔ log ↔ profile correlation — even when the user says "tracing backend", "find slow requests", "show me the service graph", "store traces in S3", "Jaeger compatible store", or "what called this span" without naming Tempo.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↓ —
- skill-quality-gateZJU-REAL/Easel发布前质量关卡：合规风险检测（敏感词、绝对化用语、平台规则） + 产物质量审核（完整性、可读性、平台适配度）。一次检查，两道把关。 当用户说"检查合规"、"质量检查"、"能不能发"、"有没有敏感词"、 "审核一下"、"发布前检查"、"质量够不够"时使用。 合并了原 skill-check-compliance 和 skill-review-deliverable 的能力。JuridicFără avertismente★ 4,1 K↓ —
- guidelines-advisortrailofbits/skillsSmart contract development advisor based on Trail of Bits' best practices. Analyzes codebase to generate documentation/specifications, review architecture, check upgradeability patterns, assess implementation quality, identify pitfalls, review dependencies, and evaluate testing. Use when asking whether a smart contract project follows development best practices, reviewing on-chain/off-chain split, upgradeability, or delegatecall proxy patterns against guidelines, or seeking recommendations on contract design, inheritance, events, documentation, dependencies, or test strategy.TestareFără avertismente★ 7,8 K↓ —
- message-test-designeraaron-he-zhu/aaron-marketing-skillsUse when the user asks to "test our messaging before we scale it", "design a message-market-fit panel", or "run a 5-second comprehension test on our new tagline"; produces a message-test design spec — hypothesis, panel and recruit criteria, comprehension / 5-second / message-market-fit (Wynter-style) protocols, stimulus set drawn from the canon, success thresholds, and a stop/revise decision rule — for the TALE Evaluate phase so the message is validated before any paid scale. It designs the test; it never runs the experiment or adjudicates a claim. Not for running the panel or A/B experiment — use send-experiment-designer or ad-test-designer; not for analyzing the results — use performance-analyzer; not for authoring the message itself — use message-system-architect. 消息测试/理解度测试/面板设计/五秒测试/消息市场契合TestareFără avertismente★ 3 K↓ —
- narrative-cascade-planneraaron-he-zhu/aaron-marketing-skillsUse when the user asks to "plan how our narrative lands on every surface", "write per-surface message-match specs", or "brief each creative builder from the canon"; maps the narrative-registry canon onto every flagship surface (homepage, pricing, store listing, sales deck, social bio, docs, email) as a per-surface message-match spec — what claim, pillar, proof, and objection reframe each surface must carry — and cuts a handoff brief to each discipline's creative builder (content-writer, ad-creative-builder, email-creative-builder, social-creative-builder, message-house-builder), so the same story lands consistently everywhere. Not for writing the finished surface copy — use each discipline creative builder; not for the tier-scoped launch asset manifest — use launch-asset-packager; not for scoring cross-surface consistency — use narrative-quality-auditor; not for claim adjudication — use offer-claims-registry. 叙事落地/逐面消息一致/创意交接简报/画布传导PlanificareFără avertismente★ 3 K↓ —
- jira-automationdavepoon/buildwithclaudeAutomate Jira tasks via Rube MCP (Composio): issues, projects, sprints, boards, comments, users. Always search tools first for current schemas.ProductivitateFără avertismente★ 3,7 K↓ —
- tao-finetune-kumo-anomalyNVIDIA/skillsKumo-Anomaly — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune Kumo-Anomaly", "run Kumo-Anomaly inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune Kumo-Anomaly", "use perform_anomaly_analysis_with_diffusion", "automl Kumo-Anomaly", "hyperparameter search Kumo-Anomaly", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "kumo-anomaly", "sdk/anomaly_analysis", or "TSDiffuser_Generic".AlteleFără avertismente★ 3,7 K↓ —
- tao-train-reidNVIDIA/skillsPerson re-identification (ReID). Learns discriminative embeddings to match the same person across different camera views, based on metric learning. Use when training, evaluating, exporting, or running inference for a TAO person re-identification model. Trigger phrases include "train ReID", "person re-identification", "cross-camera person matching", "ReID embeddings", "person re-id".AlteleFără avertismente★ 3,7 K↓ —
- using-ovnewtonNVIDIA/skillsConnect the Newton physics engine to an Omniverse simulation or ovstage with ovnewton. Use when importing a populated ovstage into Newton, stepping its physics, synchronizing stage edits, or publishing simulation state for Omniverse rendering.MediaFără avertismente★ 3,7 K↓ —
- tao-train-ocdnetNVIDIA/skillsOCDNet for scene text detection. Detects arbitrary-oriented text regions in natural images using a differentiable binarization approach. Use when training, evaluating, exporting, pruning, quantizing, retraining, or running inference for a TAO OCDNet model. Trigger phrases include "train OCDNet", "scene text detection", "arbitrary-oriented text boxes", "differentiable binarization detector".AlteleFără avertismente★ 3,7 K↓ —
- trailmark-variant-neighborhoodtrailofbits/skillsExpands one confirmed or suspected vulnerability into a Trailmark graph neighborhood of variant candidates by finding sibling functions, shared callers and callees, common sensitive sinks, common entrypoint paths, interface implementations, override relationships, type/reference neighbors, and structurally similar nodes. Use after one issue is found to seed variant-analysis, semgrep-rule-creator, static-analysis, or manual review with graph-derived candidate locations.SecuritateFără avertismente★ 7,8 K↓ —
- nvflare-orientNVIDIA/skillsRoute open-ended NVFLARE advice and only conversion requests whose preliminary source inspection reports unresolved or conflicting ownership; never load this skill merely to inspect a concrete conversion request before selecting its detected framework converter.MarketingFără avertismente★ 3,7 K↓ —
- ml-pipeline-workflowforyourhealth111-pixel/Vibe-SkillsBuild end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.ProductivitateFără avertismente★ 3,8 K↓ —Apare și în 1 alt depozit
- networkxforyourhealth111-pixel/Vibe-SkillsComprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.ȘtiințăFără avertismente★ 3,8 K↓ —Apare și în 1 alt depozit
- audio-visualizerZJU-REAL/Easel音频可视化视频：把纯音频（播客片段、音乐、口播金句、电台）渲染成带动态波形/频谱的视频，配封面和标题，好发到抖音/B站/视频号等只收视频的平台。当用户说 音频可视化、音频转视频、播客做成视频、音频波形视频、音乐可视化、给音频配画面、声波视频、频谱视频、把音频发到视频平台、电台切片视频 时使用。基于 shared/scripts/audio_viz.py（ffmpeg showwaves/showcqt/showspectrum）。与 audio-mix 区别：那个输出音频，本 SKILL 输出视频；与 slideshow-video 区别：那个用图片，本 SKILL 用音频驱动画面。MediaFără avertismente★ 4,1 K↓ —
i4h-workflow-validate
NVIDIA/skills
ProductivitateActualizat alaltăieri
De la autor
Run the root-level workflow runtime policy or rule-based rollouts and verify simulator success. Use for evaluation, checkpoints, or local controllers; do not use for replay or dataset annotation.
Instalare
npx skills add NVIDIA/skills --skill i4h-workflow-validate -a claude-code
Se instalează în .claude/skills/i4h-workflow-validate/ · 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: APACHE-2.0
Verificări de siguranță pe SKILL.md
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De ce 90
Securitate100
Proveniență83
Întreținere100
Licență100
Adopție57
Fișiere (5)
- BENCHMARK.md
- SKILL.md
- evals/evals.json
- skill-card.md
- skill.oms.sig