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: coverage

英語版ディレクトリ
Cli85

Official Lark/Feishu CLI tool with 200+ commands and 26 AI agent skills, designed for agent-native operation and easy integration with AI runtimes.

17K
Stars
85/100
信頼
カテゴリ: productivity監査

Create a structured post-earnings equity research update with key metrics, estimate changes, charts, and thesis review.

34K
Stars
75/100
信頼
カテゴリ: Finance監査

GitHub action to set up PHP with extensions, php.ini configuration, coverage drivers, and various tools.

3.2K
Stars
85/100
信頼
カテゴリ: development監査

Help your coding agents (Claude Code, Codex, Qoder, Cursor, and other coding agents) get better at getting better.

1.1K
Stars
86/100
信頼
カテゴリ: utility監査

LLM-powered engine that compiles raw documents into structured, local-first wikis as a transparent alternative to RAG.

767
Stars
83/100
信頼
カテゴリ: data監査

Qodo-Cover: An AI-Powered Tool for Automated Test Generation and Code Coverage Enhancement! 💻🤖🧪🐞

5.4K
Stars
84/100
信頼
カテゴリ: testing-qa監査

Curated collection of 14 domain-specific agent skills covering the CesiumJS API, installable as a Claude Code plugin or via the Agent Skills standard.

112
Stars
76/100
信頼
カテゴリ: coding-agents監査

Admin / analytics dashboard in a single HTML file. Fixed left sidebar, top bar with user/search, main grid of KPI cards and one or two charts. Use when the brief asks for a "dashboard", "admin", "analytics", or "control panel" screen.

90K
Stars
72/100
信頼
カテゴリ: research監査

A CLI and local admin UI for previewing and publishing static HTML, Markdown, and mini apps to Cloudflare Pages, ideal for agent-generated reports.

179
Stars
72/100
信頼
カテゴリ: development監査

A collection of 19 Claude Code skills for cybersecurity professionals covering offensive security, defensive operations, reverse engineering, threat hunting, and CSOC automation.

179
Stars
75/100
信頼
カテゴリ: security監査

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

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

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

34K
Stars
77/100
信頼
カテゴリ: research監査