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.
47.966 de skill-uri din 2.309 depozite · actualizat acum 16 ore
47.964 de skill-uri
- sn-research-reportOpenSenseNova/SenseNova-Skills用于撰写或组织研究报告结构。按读者认知任务（全景/对比/调查/时序）和领域惯例（学术/医疗/法律/政策）选择报告模板。ȘtiințăFără avertismente★ 6 K↓ —
- sn-image-imitateOpenSenseNova/SenseNova-SkillsGenerates a new image that imitates the style of a reference image while updating content based on user intent. Uses a three-stage pipeline: image annotation (long caption), caption rewriting, and image generation. Use when user asks to "imitate style", "保持这个风格重画", "按这张图风格生成", or "style transfer with new content".MediaFără avertismente★ 6 K↓ —
- deep-module-refactorzebbern/claude-code-guideExplore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.InginerieFără avertismente★ 4,7 K↓ —
- group-by-analysisOpenSenseNova/SenseNova-Skills对多 Sheet 的 Excel 文件进行行数统计、大文件 Parquet 转换预处理、数据清洗及分组聚合分析，并生成带样式标记的统计表与可视化图表。DocumenteFără avertismente★ 6 K↓ —
- secure-code-reviewzebbern/claude-code-guideSystematically reviews code for SQL injection, XSS, SSRF, broken access control, cryptographic failures, and other common OWASP Top 10 vulnerabilities, providing vulnerable code examples and ready-to-use remediation guidance. Trigger this skill when users ask for a security review, vulnerability scan, or penetration testing assistance, or mention keywords like OWASP, SQL injection, XSS, code audit, or security checklist.InginerieFără avertismente★ 4,7 K↓ —
- sn-image-resumeOpenSenseNova/SenseNova-SkillsGenerates a designed portfolio-resume image from resume content provided in conversation text. Extracts optional style instructions, converts the resume into a fixed portfolio-resume layout prompt, and generates the final image through sn-image-base. Use when user asks to create "resume image", "portfolio resume", "简历图", "简历海报", or "个人简历视觉设计".MediaFără avertismente★ 6 K↓ —
- tos-clause-scannerzebbern/claude-code-guideAudit Terms of Service, user agreements, and privacy policies for consumer risks, producing a structured report that flags unfair clauses, data traps, and liability issues. Trigger when a user asks to review, audit, or analyze a ToS, privacy policy, or user agreement, or mentions specific concerns like auto-renewal or data authorization.PlanificareFără avertismente★ 4,7 K↓ —Apare și în 1 alt depozit
- vercel-react-best-practiceszebbern/claude-code-guideReact and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.InginerieFără avertismente★ 4,7 K↓ —
- humanizeepoko77-ai/im-not-aiAI가 쓴 한글 텍스트를 자연스럽게 윤문하는 진입 명령. humanize-korean 파이프라인을 shim 의 route_hint 가 정한 경로(light 1콜 · standard 2콜 · heavy 3+콜)로 실행하고 `--strict`면 heavy 고정. 트리거 — "/humanize".RedactareFără avertismente★ 6,3 K↓ —
- humanize-scanepoko77-ai/im-not-ai한글 글에 AI 티가 얼마나 있는지 재고, 실제로 고치면 어떻게 되는지 표본으로 보여주는 정찰 스킬. 실측 판별력 상위 6개만 본다. 워크스페이스·진단·게이트·서브에이전트 없이 이 대화 안에서 끝낸다(파일 0개, 서브에이전트 0회). 손볼 게 많다고 나오면 전수 윤문은 humanize-korean(=/humanize)으로 넘긴다. 트리거 — "이 글 AI 같아?", "AI 티 있나 봐줘", "스캔해줘", "윤문 돌릴 값어치 있나", "AI 티 점수", "quick humanize", "/humanize-scan".RedactareFără avertismente★ 6,3 K↓ —
- hunt-ntlm-infoelementalsouls/Claude-BugHunterHunt NTLM/Negotiate information disclosure on internet-reachable IIS/SharePoint/Exchange. Anonymous NTLM Type-2 challenge capture leaks NetBIOS domain, internal DNS forest, computer name, AD timestamp via AV_PAIRS structure. Default Windows-installer hostnames (WIN-XXXXXXXXXXX pattern) signal lazy provisioning. Use when target advertises `WWW-Authenticate: NTLM` or `Negotiate` headers anonymously.MarketingFără avertismente★ 5,4 K↓ —
- admingrafana/skillsManage Grafana Cloud accounts — organizations, stacks, RBAC roles and assignments, SSO/SAML/OAuth/GitHub auth, service accounts for CI/CD, user invites, team membership, and API-driven provisioning. Creates stacks via the Cloud API, mints service-account tokens, applies role assignments, configures SSO providers, and provisions teams/folders/dashboards via Terraform. Use when managing Grafana Cloud access, configuring SSO/SAML/OAuth, setting up service accounts for Terraform/CI/CD, assigning RBAC roles, inviting users, managing multiple stacks or organizations, provisioning cloud resources via API or Terraform, or auditing admin actions — even when the user says "set up SSO", "create a stack", "make a service account", or "onboard a team" without explicitly saying "admin".OficialDevOpsFără avertismente★ 297↓ —
- ml-aigrafana/skillsTurn on AI + ML features in Grafana Cloud — Grafana Assistant (NL → PromQL/LogQL/TraceQL, dashboard build, incident investigation, MCP integration), Dynamic Alerting (Prophet forecasting + DBSCAN outlier detection), Sift (8-analysis automated root-cause), Knowledge Graph + RCA Workbench, and the LLM Plugin (OpenAI / Anthropic / Azure / Ollama / vLLM / LiteLLM). Use when you want anomaly alerts without static thresholds, natural-language querying, automated incident investigation, dashboards generated from a sentence, or a managed LLM proxy for plugins — even when the user says "alert when something looks weird", "explain this PromQL", "find the root cause", "make this a dashboard", or "wire Claude into Grafana" without naming any of these products.OficialDevOpsFără avertismente★ 297↓ —
- nextjs-developerzebbern/claude-code-guideUse when building Next.js 14+ applications with App Router, server components, or server actions. Invoke for full-stack features, performance optimization, SEO implementation, production deployment.DevOpsFără avertismente★ 4,7 K↓ —
- promqlgrafana/skillsWrite, validate, and optimize PromQL for Prometheus / Grafana Mimir / Grafana Cloud Metrics. Covers `rate` vs `irate` vs `increase`, label matchers and regex, `sum / avg / topk / by / without` aggregation, classic + native `histogram_quantile`, ratios with divide-by-zero guards, `absent` / `changes` for staleness, time offsets and `predict_linear`, recording-rule naming, SLO + burn-rate math, and a cardinality-hunting playbook. Use when writing a metric query, fixing wrong p95s, building an error-budget alert, debugging "query is slow", finding the noisy label that blew up cardinality, or migrating a dashboard query to a recording rule — even when the user says "calculate the error rate", "p99 latency", "sum by service", "why is this query slow", or "what's filling Mimir" without naming PromQL.OficialRedactareFără avertismente★ 297↓ —
- pyroscopegrafana/skillsContinuously profile applications with Grafana Pyroscope and read the result as flame graphs. Covers three instrumentation paths — language SDK push (Go / Java / Python / Ruby / Node / .NET / Rust), Alloy eBPF auto-instrumentation (no code change, requires kernel 5.8+ with BTF), and SDK → Alloy receiver — plus ProfileQL queries, profile types (CPU / memory / allocations / goroutines / mutex), Grafana Cloud Profiles endpoint, and Span Profiles trace-to-profile linking. Use when adding profiling to a service, deploying Alloy as a cluster-wide eBPF profiler, hunting CPU / memory hotspots from a flame graph, comparing two profiles to find a regression, or correlating a slow Tempo trace to its profile — even when the user says "find what's burning CPU", "flame graph this app", "continuous profiling", "heap hotspots", or "why is allocation so high" without naming Pyroscope.OficialDevOpsFără avertismente★ 297↓ —
- writing-specsJetBrains/thinkrailUse when a workflow step drafts or revises a spec artifact — a goal-and-requirements, an architecture, or a module SPEC — or when a workflow skill names it at such a step. The shared quality bar for specs — not a workflow, nothing to execute.RedactareFără avertismente★ 6,4 K↓ —
- single-sheet-reading-and-analysisOpenSenseNova/SenseNova-Skills读取并解析单个Excel工作表数据，支持合并单元格处理、数据清洗、交叉分析及多维度可视化，适用于需要从单表中提取关键指标并进行趋势模拟与图表生成的场景。DocumenteFără avertismente★ 6 K↓ —
- alerting-irmgrafana/skillsConfigure Grafana Alerting, Incident Response Management (IRM), and SLOs end-to-end — provisions Grafana-managed and data-source-managed alert rules, contact points (Slack/PagerDuty/email/webhook), notification policies with hierarchical matchers, silences, mute timings, on-call schedules and escalation chains, incident-management integrations, and SLOs with multi-window burn-rate alerts. Use when configuring alerts, debugging notification routing, setting up on-call rotations, declaring or managing incidents, defining SLOs, provisioning alerting via YAML or API, picking matchers for a notification policy, building a PagerDuty/Slack webhook receiver, or troubleshooting why an alert isn't firing — even when the user says "page me on errors", "alert me when X happens", "route this to the platform team", or "set up an SLO" without naming Alerting or IRM.OficialInginerieFără avertismente★ 297↓ —
- cost-managementgrafana/skillsCut your Grafana Cloud bill by attributing spend to teams and reducing telemetry volume. Covers FOCUS-compliant billing dashboards, cost-attribution labels in Alloy, Adaptive Metrics (cardinality reduction), Adaptive Logs (drop/sample), Adaptive Traces (tail sampling), usage alerts, and an optimization checklist. Use when investigating a high Grafana Cloud bill, attributing observability cost to a team or service, reducing active series / log bytes / trace spans, or setting up usage / quota alerts — even when the user says "our Grafana bill is too high", "who's burning the most metrics", "drop debug logs", "sample our traces", or "alert me before we hit quota" without naming Cost Management.OficialDevOpsFără avertismente★ 297↓ —
- infrastructuregrafana/skillsShip Kubernetes, host, container, and cloud-provider telemetry into Grafana Cloud — `k8s-monitoring` Helm chart for K8s clusters (metrics + logs + traces + events + cost), Alloy `prometheus.exporter.unix` for Linux hosts, cAdvisor + Docker discovery for containers, and CloudWatch / Azure Monitor / Google Cloud Monitoring datasource setup. Use when onboarding a new cluster or VM fleet to Grafana Cloud, picking the right Helm values for K8s scraping, wiring kube-state-metrics + node-exporter + cAdvisor, alerting on `PodCrashLooping` / node memory / PVC capacity, or pulling AWS / Azure / GCP cloud metrics — even when the user says "monitor my cluster", "send K8s metrics to Grafana", "scrape EC2 metrics", "cluster pod logs", or "install the monitoring helm chart" without naming `k8s-monitoring` or Alloy.OficialDevOpsFără avertismente★ 297↓ —
- 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★ 297↓ —
- godot-exportgamedev-skills/awesome-gamedev-agent-skillsExport and build a Godot 4.7 project for distribution: install export templates, define export presets (Windows/macOS/Linux/Web/Android), run headless command-line exports for CI, and handle web (HTML5) COOP/COEP and dedicated-server/headless builds. Use when exporting a Godot game, configuring export_presets.cfg, building for web/desktop/mobile, or automating builds from the command line.AlteleFără avertismente★ 5,8 K↓ —
- godot-tilemapgamedev-skills/awesome-gamedev-agent-skillsBuild and edit tile-based 2D levels in Godot 4.7 with TileMapLayer and TileSet: paint layers, set up collision/navigation/custom-data on tiles, autotile with terrain sets, and read/write cells from code (set_cell, get_cell_tile_data, local_to_map). Use when working with TileMapLayer nodes, .tres TileSets, autotiling, or migrating a deprecated TileMap node to TileMapLayer.RedactareFără avertismente★ 5,8 K↓ —
sn-da-large-file-analysis
OpenSenseNova/SenseNova-Skills
DocumenteActualizat alaltăieri
De la autor
万行以上 Excel 数据集的高性能分析引擎。提供 openpyxl read_only 流式读取（iter_rows 支持 10 万行以上）、Parquet 转换加速、内存优化、分块处理和大文件写入模式。**遇到以下任一情况就主动使用本 skill**：①数据行数 ≥ 10k（由 sn-da-excel-workflow 的行数评估步骤触发）；②用户出现触发词：大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunked processing；③直接使用 pd.read_excel() 导致超时或内存溢出；④用户明确要求对大规模数据集进行高性能处理。仅不用于：小于 10k 行的常规 Excel 分析（使用 sn-da-excel-workflow 即可）。
Instalare
npx skills add OpenSenseNova/SenseNova-Skills --skill sn-da-large-file-analysis -a claude-code
Se instalează în .claude/skills/sn-da-large-file-analysis/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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Doar instrucțiuniFolosește rețeauaLicență: MIT
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