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: dstar-lite

영문 디렉토리

Applications self-hosting and DevOps platform for running open source, web-based linux Panel of lite PaaS

2.1K
Stars
75/100
신뢰
카테고리: devops감사

超轻量级中文ocr,支持竖排文字识别, 支持ncnn、mnn、tnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M

12K
Stars
86/100
신뢰
카테고리: document-processing감사

Query git repositories with SQL. Generate reports, perform status checks, analyze codebases. 🔍 📊

3.5K
Stars
77/100
신뢰
카테고리: data-analysis감사

Fathom Lite. Simple, privacy-focused website analytics. Built with Golang & Preact.

8.0K
Stars
84/100
신뢰
카테고리: legal-compliance감사

The best browser for both you and your AI agents work in parallel.

7.9K
Stars
74/100
신뢰
카테고리: agent-frameworks감사

可能是最深度的 AI 投研报告 Skill:九章个股深研 + 九章财报深度分析,脚本化 DCF/EPV 与可复算估值

234
Stars
75/100
신뢰
카테고리: utility감사

Two Claude Skills that turn agents into AI film directors, providing cinematic dramaturgy and exact prompt syntax for major video/image models.

118
Stars
76/100
신뢰
카테고리: design-creative감사

A hardware-aware Codex/WorkBuddy skill that automates local MiniMax H3 video generation through ComfyUI, handling model selection, installation, and low-VRAM configuration.

126
Stars
80/100
신뢰
카테고리: design-creative감사

Kodezi Chronos is a debugging-first language model that achieves state-of-the-art results on SWE-bench Lite (80.33%) and 67% real-world fix accuracy, over six times better than GPT-4. Built with Adaptive Graph-Guided Retrieval and Persistent Debug Memory. Model available Q1 2026 via Kodezi OS.

4.9K
Stars
73/100
신뢰
카테고리: ml-automation감사

🤖📈 EA31337 Lite, Advanced and Rider - Forex multi-strategy trading robot for MT4/MT5 platforms

1.2K
Stars
78/100
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카테고리: finance감사

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

A curated collection of battle-tested Claude Code skills for product, content, writing, presentations, and workflow automation.

130
Stars
75/100
신뢰
카테고리: coding-agents감사