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 13 ore
45.604 skill-uri
- line-chart-visualizationOpenSenseNova/SenseNova-Skills提取结构化数据并进行特征清洗与聚类分析，生成包含趋势对比、分布特征与参数敏感性的多维度综合可视化图表，适用于各类趋势预测与多维对比场景。AlteleFără avertismente★ 5,9 K↓ —
- range-reading-and-large-file-analysisOpenSenseNova/SenseNova-Skills读取多 Sheet Excel 文件，根据数据量动态选择处理策略，支持特定区域数据提取、大文件 Parquet 转换、统计分析及可视化图表生成。DocumenteFără avertismente★ 5,9 K↓ —
- multi-file-excel-parquet-analysisOpenSenseNova/SenseNova-Skills读取多 Sheet Excel 文件并统计规模，支持大文件向 Parquet 格式转换、分类数据统计及可视化报告生成。DocumenteFără avertismente★ 5,9 K↓ —
- pivot-table-cross-analysisOpenSenseNova/SenseNova-Skills利用交叉表与热力图对分类数据进行多维度占比分析，适用于奖项分布、绩效评估或市场占有率等结构化数据的清洗与可视化。AlteleFără avertismente★ 5,9 K↓ —
- excel-bar-chart-visualizationOpenSenseNova/SenseNova-Skills读取多工作表Excel文件，自动处理合并单元格与数据清洗，进行交叉分组统计并生成带总计行的结果表，最后绘制支持中英文字体的美化柱状图，适用于多维度数据汇总与可视化分析。DocumenteFără avertismente★ 5,9 K↓ —
- grouped-statisticsOpenSenseNova/SenseNova-Skills对多 Sheet 的 Excel 文件进行行数统计、数据合并与前向填充。DocumenteFără avertismente★ 5,9 K↓ —
- numeric-extraction-and-distribution-analysisOpenSenseNova/SenseNova-Skills从带单位的字符串列中提取数值并清洗，生成包含直方图、饼图、条形图和累积分布图的多维度综合分布可视化图表，用于展示数据的集中趋势与分布特征。AlteleFără avertismente★ 5,9 K↓ —
- typescript-prozebbern/claude-code-guideUse when building TypeScript applications requiring advanced type systems, generics, or full-stack type safety. Invoke for type guards, utility types, tRPC integration, monorepo setup.InginerieFără avertismente★ 4,7 K↓ —
- 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↓ —
- large-file-conditional-formattingOpenSenseNova/SenseNova-Skills根据Excel总行数自动切换Parquet加速读取，计算特定维度的时间序列平均值，并使用openpyxl输出带有条件格式（如低于均值标绿）和自定义样式的分析报告。DocumenteFără avertismente★ 5,9 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↓ —
- designlang-tokensManavarya09/design-extractUse when styling UI for railway.app — references the extracted design system tokens instead of inventing colors, spacing, or typography.DesignFără avertismente★ 4,4 K↓ —
- author-componentdotnet/skillsCreate or review Blazor components (.razor files) with correct architecture. USE FOR: writing new Blazor components that do NOT involve JavaScript interop, implementing parameters and EventCallback, RenderFragment slots, component lifecycle (OnInitializedAsync, OnParametersSet), async patterns, IAsyncDisposable, CancellationToken, CSS isolation, code-behind. DO NOT USE FOR: creating new projects (use create-blazor-project), JavaScript interop or calling browser APIs from Blazor (use use-js-interop), forms and validation (use collect-user-input), prerendering issues (use support-prerendering), HTTP data fetching patterns (use fetch-and-send-data), coordinating state between unrelated components (use coordinate-components).DesignFără avertismente★ 5,7 K↓ —
- invalid-data-cleaningOpenSenseNova/SenseNova-Skills用于大规模Excel数据的预处理，通过统计总行数判断是否转换为Parquet格式以提升读写效率，并使用正则表达式清洗指定文本列（如仅保留中文字符），最后导出清洗后的文件并提供下载链接。DocumenteFără avertismente★ 5,9 K↓ —
- excel-threshold-analysis-and-stylingOpenSenseNova/SenseNova-Skills根据 Excel 数据量级自动判断处理策略，执行数值列清洗、条件过滤，并使用 openpyxl 对符合条件的单元格进行样式标记与导出。DocumenteFără avertismente★ 5,9 K↓ —
- time-series-and-categorical-analysisOpenSenseNova/SenseNova-Skills对时间序列或分类数据进行多维度趋势分析、百分比清洗、绩效分级建模与预测，并生成高分辨率的可视化综合报告，适用于业务指标监控与预测场景。AlteleFără avertismente★ 5,9 K↓ —
- condition-filtering-and-large-file-optimizationOpenSenseNova/SenseNova-Skills根据数据规模动态选择处理策略。AlteleFără avertismente★ 5,9 K↓ —
- replica-buildJakeschincariol/replica-skillRebuilds an app screen by screen from the recon map: app shell first, then the core flow as a vertical slice, then every screen with all its states, ticking off the feature matrix as it goes. Writes every line fresh, never the original's code, assets or copy. Use when the user says "build the clone", "build screen S07", "start building", "rebuild this screen", "implement the booking page", or after /replica-design.DesignFără avertismente★ 5,4 K↓ —
- word-analysisOpenSenseNova/SenseNova-SkillsWord (.docx/.doc) 文档全量解析。覆盖：正文/段落文本提取、表格数据提取、高亮/颜色格式读取、多文件汇总对比、嵌入图片转 caption。DocumenteFără avertismente★ 5,9 K↓ —
- pdf-analysisOpenSenseNova/SenseNova-SkillsPDF 文档解析。自动区分文字型 PDF 与扫描型 PDF，覆盖：文本/表格提取、多页全量扫描、嵌入图表 caption、单位感知数值计算。DocumenteFără avertismente★ 5,9 K↓ —
- sn-report-format-discoveryOpenSenseNova/SenseNova-Skills用于用户希望推荐研究成品形式，或最终形式无法从需求中直接判断时。把需求解析为一个简短的 format 字符串，不创建格式文件或 schema。AlteleFără avertismente★ 5,9 K↓ —
- esmfold2aipoch/open-scienceBiohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.ȘtiințăFără avertismente★ 5,4 K↓ —
- category-statisticsOpenSenseNova/SenseNova-Skills提取指定类别列并统计各类别数量与占比，生成高分辨率的柱状图、饼图等组合可视化报告，适用于分类数据的分布情况分析。AlteleFără avertismente★ 5,9 K↓ —
- mcp-builderanthropics/skillsGuide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).OficialInginerieFără avertismente★ 183,6 K↓ —Apare și în 10 alte depozite
promql
grafana/skills
OficialRedactareActualizat acum 16 ore
De la autor
Write, 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.
Instalare
npx skills add grafana/skills --skill promql -a claude-code
Se instalează în .claude/skills/promql/ · 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 instrucțiuniFolosește rețeauaLicență: APACHE-2.0
Verificări de siguranță pe SKILL.md
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De ce 91
Securitate100
Proveniență100
Întreținere100
Licență100
Adopție41
Fișiere (2)
- SKILL.md
- references/patterns.md