Skill 디렉토리

AI Agent를 위한 재사용 가능한 Skill을 찾으세요.

작업으로 실제 GitHub Skill을 검색하고 사용 전에 Stars, 신뢰, 감사, 카테고리, 설치 경로를 확인하세요.

모든 추천은 리포지토리, 감사, 설치 경로와 명확하게 연결됩니다.

검색 결과: insight

영문 디렉토리

Repository for Project Insight: NLP as a Service

320
Stars
59/100
신뢰
카테고리: ml-automation감사

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
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카테고리: productivity감사

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
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카테고리: agent-frameworks감사

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

3.0K
Stars
83/100
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카테고리: growth-marketing감사

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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카테고리: robotics-iot감사

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

196
Stars
73/100
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카테고리: utility감사

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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카테고리: research감사

Python Computer Vision & Video Analytics Framework With Batteries Included

837
Stars
71/100
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카테고리: robotics-iot감사

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
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카테고리: research감사

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
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카테고리: rag-knowledge감사

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
신뢰
카테고리: security감사