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: submission

영문 디렉토리

Prior to making any Submission(s), you must sign an Adobe Contributor License Agreement, available here at: https://opensource.adobe.com/cla.html. All Submissions you make to Adobe Inc. and its affiliates, assigns and subsidiaries (collectively “Adobe”) are subject to the terms of the Adobe Contributor License Agreement.

12K
Stars
86/100
신뢰
카테고리: commerce-automation감사

Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.

2.2K
Stars
85/100
신뢰
카테고리: agent-skills감사

Pre-submission AI review stress-test for research papers. A Claude Code skill: review, verdict, revise, verify.

917
Stars
84/100
신뢰
카테고리: utility감사

🤖📐专为数学建模设计的 Agent & skills ,自动完成数学建模,生成一份完整的可以直接提交的论文。 An Agent Designed for Mathematical Modeling ,Automatically complete mathmodel and generate a complete paper ready for submission.

2.3K
Stars
76/100
신뢰
카테고리: agent-skills감사

AI agent skill to scan iOS/macOS projects for App Store rejection patterns before submission

1.3K
Stars
84/100
신뢰
카테고리: agent-skills감사

A set of installable AI agent skills for evidence-driven design award research, evaluation, matching, entry writing, and submission readiness, supporting multiple agent runtimes.

111
Stars
76/100
신뢰
카테고리: design-creative감사

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

34K
Stars
78/100
신뢰
카테고리: design-creative감사

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
신뢰
카테고리: research감사

Homework Submission, Automated Grading, and TA grading system.

774
Stars
71/100
신뢰
카테고리: education감사

Use when preparing iPolloWork local changes for GitHub: stash or preserve local edits, sync the current personal branch with remote main, re-apply local work, diagnose conflicts, commit in English, push to the current remote branch, and open a PR to main without creating extra branches.

4.5K
Stars
67/100
신뢰
카테고리: coding-agents감사

Pre-submission AI review stress-test for research papers. A Claude Code skill: review, verdict, revise, verify.

320
Stars
70/100
신뢰
카테고리: development감사