Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: density-estimation

英語版ディレクトリ

A geospatial analytics skill for AI agents like Claude, Codex, and Copilot, enabling map-based queries on PostGIS, BigQuery, Snowflake.

571
Stars
84/100
信頼
カテゴリ: data監査

A full-lifecycle Claude Code skill for creating, compiling, reviewing, and polishing academic Beamer LaTeX presentations with quality scoring and pedagogical audits.

324
Stars
77/100
信頼
カテゴリ: presentation監査

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

34K
Stars
71/100
信頼
カテゴリ: ml-automation監査

An agent skill that transforms AI assistants into expert economics paper writers by synthesizing best practices from over 50 authoritative guides.

470
Stars
78/100
信頼
カテゴリ: research監査

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

34K
Stars
77/100
信頼
カテゴリ: data-analysis監査

Create branded architecture, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, Venn, pyramid/funnel, treemap, bar, line, Gantt and scatter charts, high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map, or database schema diagrams as standalone HTML/SVG/PNG. Redraw .drawio/.drawio.png/.drawio.svg or Mermaid .mmd sources at a chosen size/detail; onboard brand tokens from a website; add semantic patterns, callouts, accessible motion, or sketchy/hand-drawn styling.

24K
Stars
75/100
信頼
カテゴリ: security監査
aeo77

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

25K
Stars
77/100
信頼
カテゴリ: security監査

[ICCV 2019] Monocular depth estimation from a single image

4.5K
Stars
66/100
信頼
カテゴリ: robotics-iot監査

Data Analysis with Bootstrap Estimation in R

224
Stars
70/100
信頼
カテゴリ: data-analysis監査

Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar.

1.8K
Stars
68/100
信頼
カテゴリ: robotics-iot監査