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: insight

英語版ディレクトリ

Repository for Project Insight: NLP as a Service

320
Stars
59/100
信頼
カテゴリ: ml-automation監査

A comprehensive set of 38 marketing skills and 5 commands for Claude Code covering SEO/GEO and influencer marketing with evaluation frameworks.

2.6K
Stars
86/100
信頼
カテゴリ: productivity監査

Every API has a secret identity. This finds it, absorbs every feature from every competing tool, then builds the GOAT CLI — designed for AI agents first, with SQLite sync, offline search, and compound insight commands.

4.0K
Stars
70/100
信頼
カテゴリ: agent-frameworks監査

Tianji: Insight into everything, Website Analytics + Uptime Monitor + Server Status. not only another GA alternatives

3.0K
Stars
83/100
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カテゴリ: growth-marketing監査

This MATLAB and Simulink Challenge Project Hub contains a list of research and design project ideas. These projects will help you gain practical experience and insight into technology trends and industry directions.

2.1K
Stars
76/100
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カテゴリ: robotics-iot監査

Turn project work into reusable knowledge — an AI-agent skill for Claude Code & Codex

196
Stars
73/100
信頼
カテゴリ: utility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
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カテゴリ: research監査

Python Computer Vision & Video Analytics Framework With Batteries Included

837
Stars
71/100
信頼
カテゴリ: robotics-iot監査

A SKILL.md-based agent skill covering the full deep learning experiment lifecycle—running jobs on owned or rented GPUs, verifying results, and delivering reproducible figures and tables.

62
Stars
70/100
信頼
カテゴリ: research監査

Secure AI conversations with documents, video, audio, and more. Personal workspaces for focused context, group spaces for shared insight. Classify docs, reuse prompts, and extend with modular features.

138
Stars
66/100
信頼
カテゴリ: rag-knowledge監査

LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready packaging. Use when creating, outlining, researching, enriching, rewriting, humanizing, auditing, or packaging LinkedIn Articles, LinkedIn newsletters, LinkedIn long-form posts, LinkedIn thought leadership, LinkedIn B2B articles, LinkedIn 长文, LinkedIn 专栏, LinkedIn 话题调研, LinkedIn 商务内容, 去 AI 化编辑, or LinkedIn 发布包.

483
Stars
63/100
信頼
カテゴリ: security監査