Agent skills you can check before you install
Public SKILL.md skills from GitHub repositories with 50+ stars and from the vendors themselves, scanned and scored. Install any of them with one command in Claude Code, Cursor, Codex, GitHub Copilot or Gemini CLI.
48,164 skills from 2,325 repositories · updated 3 minutes ago
48,162 skills
- skill-authoringgrafana/skillsAuthor, audit, and improve Grafana SKILL.md files against Anthropic's published Agent Skills guidance and the four-dimension rubric the grafana/skills CI gate uses (conciseness, actionability, workflow clarity, progressive disclosure). Applies the canonical SKILL.md structure (YAML frontmatter + body + references/ + scripts/ + assets/), the "pushy description" trigger pattern, the three-level progressive-disclosure model, and the validate-fix-rerun feedback loop. Use when creating a new skill in this repo, when reviewing a skill PR, when a skill's Tessl review score is below 75 (the merge gate), when a skill's description isn't getting picked up by agents, when restructuring a long SKILL.md into a bundle, or when the user asks how to write, improve, optimize, audit, or fix a skill - even if they don't say "skill" explicitly (e.g. "this isn't triggering", "Tessl scored this 72", "split this doc").OfficialWritingNo warnings★ 300↓ —
- hunt-nosqlielementalsouls/Claude-BugHunterHunt NoSQL Injection — MongoDB operator injection ($where, $regex, $gt, $ne), CouchDB, Redis command injection, auth bypass via NoSQLi, data dump. Use when target uses MongoDB/Mongoose, CouchDB, Redis, or shows NoSQL error messages.OtherNo warnings★ 5.4K↓ —
- template-authoringdotnet/skillsGuides creation and validation of custom dotnet new templates from existing projects. Generates a .template.config/template.json that preserves the source project's conventions. USE FOR: creating a reusable dotnet new template from an existing project, bootstrapping .template.config/template.json with correct identity, shortName, parameters, and post-actions, adding parameters or conditional content to a template you are authoring, validating the template.json you are authoring before publishing, packaging templates as NuGet packages for distribution. DO NOT USE FOR: validating an existing template.json as a standalone task (use template-validation), finding or using existing templates (use template-discovery and template-instantiation), MSBuild project file issues unrelated to template authoring, NuGet package publishing (only template packaging structure).ProductivityNo warnings★ 5.8K↓ —
- marginaleffectsbrycewang-stanford/Auto-Empirical-Research-SkillsManual for the marginaleffects R and Python package, and guide to the book "Model to Meaning". Use when users ask about predictions, comparisons, slopes, marginal effects, average treatment effects (ATE/ATT/CATE), hypothesis testing, contrasts, counterfactuals, risk ratios, odds ratios, causal inference with G-computation, or need help with marginaleffects functions like predictions(), comparisons(), slopes(), hypotheses(), datagrid(), avg_predictions(), avg_comparisons(), avg_slopes(), or plot functions.TestingNo warnings★ 4.8K↓ —
- 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.OfficialOtherNo warnings★ 300↓ —
- 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?"TestingNo warnings★ 4.8K↓ —
- pie-chart-data-analysisOpenSenseNova/SenseNova-Skills对多Sheet Excel或CSV数据进行分类汇总统计，自动识别关键字段并生成包含占比、数值及美化饼图的可下载分析报告。DataNo warnings★ 6K↓ —
- integrating-jupiterinternet-court/internet-court-skillComprehensive guidance for integrating Jupiter APIs (Swap, Lend, Perps, Trigger, Recurring, Tokens, Price, Portfolio, Prediction Markets, Send, Studio, Lock, Routing). Use for endpoint selection, integration flows, error handling, and production hardening.OtherNo warnings★ 7K↓ —
- mirrord-propagation-discoverymetalbear-co/mirrordScans a live Kubernetes environment with `mirrord dump` to find an identifier mirrord can filter traffic on: a header, a `baggage` or `tracestate` member, a tenant or user id, a trace id (W3C, B3, X-Ray, Google, Jaeger), a JWT claim, a cookie, a path segment, or a JSON body field that is present on every hop of a request chain, unique to one developer's traffic, and settable by that developer. Runs mirror-only dumps on each service, sends a tagged probe request at the edge or observes real traffic, scores the candidates, and produces the matching `http_filter` (regex, jq on headers, path, method, body, or combinations) plus a per-hop report of where each identifier is dropped. Installs the mirrord CLI through a package manager when it is missing. Use when the user asks what to filter on, whether the cluster already propagates a request id or trace context, how to isolate their own requests on a shared staging cluster, how to filter on a JWT or cookie with mirrord, or wants to inspect live traffic with mirrordDevOpsNo warnings★ 5.4K↓ —
- sn-da-large-file-analysisOpenSenseNova/SenseNova-Skills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）。DocumentsNo warnings★ 6K↓ —
- sn-md-to-html-reportOpenSenseNova/SenseNova-Skills将 Markdown 报告、研究笔记、行业分析、战略备忘录、技术白皮书、复盘、周报等长文档，重组并创作为有编辑判断、网页美感和证据秩序的自包含 HTML 专题页。用户要求“转成 HTML”“网页化报告”“美化报告”“做成专题页”“便于分享”“提升可读性/设计感”“把报告做成网页”时使用。必须先写 plan.md 再写 HTML，保留原文事实断言和结论强度，不套模板，不做机械 Markdown 转换。PlanningNo warnings★ 6K↓ —
- 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.TestingNo warnings★ 6.4K↓ —
- dflow-phantom-connectinternet-court/internet-court-skillBuild Solana wallet-connected apps with Phantom Connect SDKs and DFlow spot trading. Use when user asks to connect a Phantom wallet, integrate Phantom in React, React Native, or vanilla JS, sign messages or transactions, build token-gated pages, mint NFTs, accept crypto payments, or swap/stream tokens with DFlow. Covers @phantom/react-sdk, @phantom/react-native-sdk, @phantom/browser-sdk, and DFlow spot trading and market-data streaming. Do NOT use for Ethereum or EVM wallet integrations, or non-DFlow DEX routing.BrowserNo warnings★ 7K↓ —
- excel-statistical-viz-large-fileOpenSenseNova/SenseNova-Skills对 Excel 数据进行多维度统计分析与可视化。DocumentsNo warnings★ 6K↓ —
- borzoiaipoch/open-sciencePredict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi. Use this skill when: (1) Scoring the regulatory effect of a variant on expression/accessibility, (2) Generating predicted coverage tracks for a locus, (3) Prioritising non-coding variants by predicted track delta.ScienceNo warnings★ 5.4K↓ —
- numeric-format-normalizationOpenSenseNova/SenseNova-Skills对 Excel 数据进行数值格式标准化与清洗，支持大规模数据的 Parquet 转换流程，并完成关键指标的合计核对与结果文件导出。DocumentsNo warnings★ 6K↓ —
- excel-data-analysis-and-report-generationOpenSenseNova/SenseNova-Skills从Excel提取多类型数据，并生成包含可视化图表与下载链接的综合分析报告。DataNo warnings★ 6K↓ —
- excel-conditional-comparison-and-large-file-processingOpenSenseNova/SenseNova-Skills对比Excel多表中的特定系数并对异常值进行颜色标记。DocumentsNo warnings★ 6K↓ —
- 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。DocumentsNo warnings★ 6K↓ —
- condition-filtering-and-large-file-optimizationOpenSenseNova/SenseNova-Skills根据数据规模动态选择处理策略。OtherNo warnings★ 6K↓ —
- 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。OtherNo warnings★ 6K↓ —
- heliusinternet-court/internet-court-skillBuild Solana applications with Helius infrastructure. Covers transaction sending (Sender), asset/NFT queries (DAS API), real-time streaming (WebSockets, Laserstream), event pipelines (webhooks), priority fees, wallet analysis, and agent onboarding.DevOpsNo warnings★ 7K↓ —Also in 1 other repo
- shader-programminggamedev-skills/awesome-gamedev-agent-skillsWrite game shaders from cross-engine fundamentals — the vertex→fragment pipeline, coordinate spaces, UV math, and common 2D/3D effects (tint, UV scroll, dissolve, outline, fresnel rim, vignette) in GLSL with HLSL equivalents. Use when the user mentions shaders, fragment/pixel shader, vertex shader, UV, GLSL, HLSL, or effects like dissolve, outline, or rim light.WritingNo warnings★ 6.2K↓ —
- 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。DocumentsNo warnings★ 6K↓ —
melonjs-lighting
melonjs/melonJS
PlanningUpdated 59 minutes ago
From the author
Use this skill for 2D dynamic lighting in melonJS — Light2d, ambient light on a Stage, multiple lights, and per-pixel normal-mapped sprites authored in SpriteIlluminator. Covers specular highlights via shininess, the ambient-cutout model, which parts need a GPU backend, and why Light2d does not survive a Camera3d. Triggers on: Light2d, specular, shininess, highlight, glint, lighting, ambientLight, ambientLightingColor, normalMap, SpriteIlluminator, per-pixel lighting, drawLight, torch, glow, darkness.
Install
npx skills add melonjs/melonJS --skill melonjs-lighting -a claude-code
Installs to .claude/skills/melonjs-lighting/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Download .zip
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
What it can touch
Instructions onlyNo network use seenLicence: MIT
Safety checks on SKILL.md
No warnings.
Why 91
Security100
Provenance86
Maintenance100
Licence100
Adoption60
Files (1)
- SKILL.md