개인용으로 재사용 가능한 AI Agent Skills 컬렉션. 이 저장소는 Codex, Claude Code 및 Skills 형식과 호환되는 다른 에이전트에 대한 지침, 워크플로우 및 지원 리소스를 유지합니다.

저장소 구조

각 스킬은 자신의 최상위 디렉토리에 살고 있습니다. 디렉토리 이름은 name]에 일치해야 SKILL.md:

SKILL.md 이외의, agents/], references/], scripts/, 또는 assets/]) 기술을 실제로 필요로 할 때.

Cross-Platform 호환성

• SKILL.md는 스킬의 크로스 플랫폼 핵심입니다. 트리거 조건과 기본 워크플로를 정의합니다.
• references/, scripts/], assets/]은 필요한 경우만 지원되는 리소스를 포함합니다.
• agents/openai.yaml는 디스플레이 이름, 짧은 설명, 기본 프롬프트 및 기타 인터페이스 기능에 대한 옵션 Codex/OpenAI 제품 메타데이터입니다.
• Claude Code는 agents/openai.yaml]에 의존하지 않습니다. 이 파일은 대체하거나 SKILL.md]의 보편적 인 지시를 변경하지 않습니다.
• A Skill은 여러 에이전트 플랫폼에 대한 옵션 어댑터를 포함 할 수 있지만 핵심 워크플로우는 단일 플랫폼에 의존하지 않습니다.

기술

가속도-도메인-learning

Skill

A collection of reusable AI Agent Skills for personal use. This repository maintains instructions, workflows, and supporting resources for Codex, Claude Code, and other agents compatible with the Skills format.

Repository Structure

Each Skill lives in its own top-level directory. The directory name must match the name in its SKILL.md:

skill/
├── README.md
└── /
    ├── SKILL.md
    ├── agents/
    │   └── openai.yaml
    ├── references/
    ├── scripts/
    └── assets/

Other than SKILL.md, add agents/, references/, scripts/, or assets/ only when the Skill actually needs them.

Cross-Platform Compatibility

• SKILL.md is the cross-platform core of a Skill. It defines triggering conditions and the primary workflow.
• references/, scripts/, and assets/ contain supporting resources loaded only when needed.
• agents/openai.yaml is optional Codex/OpenAI product metadata for display names, short descriptions, default prompts, and other interface features.
• Claude Code does not depend on agents/openai.yaml; this file neither replaces nor changes the universal instructions in SKILL.md.
• A Skill may include optional adapters for multiple agent platforms, but its core workflow should not depend on any single platform.

Skills

accelerated-domain-learning

Accelerated Domain Learning helps learners build a reliable mental map of an unfamiliar field quickly, then tests genuine understanding through evidence checks, deep questions, and adaptive follow-ups.

Key capabilities:

• Calibrate the learning goal, existing knowledge, and observable completion criteria.
• Establish domain boundaries, prerequisites, mental models, and concept relationships.
• Distinguish consensus, major debates, open questions, and frontier directions.
• Select evidence sources by knowledge type and label facts, opinions, and inferences.
• Generate explanation, distinction, boundary, counterexample, transfer, and evidence-evaluation questions.
• Locate misconceptions, repair missing prerequisites, and adapt the depth based on the learner’s answers.
• Produce a cognitive summary and continued learning path in the learner’s own language.
• Support scientific, technical, humanities, professional-practice, and general-knowledge domains.

Directory: accelerated-domain-learning

prd-writer

Universal PRD Writer turns product ideas, business needs, feature requests, and existing specifications into product requirements that can be reviewed, accepted, and implemented.

Key capabilities:

• Use one requirements core for standard digital products, AI products, Agent products, and multi-Agent systems.
• Adjust output depth based on audience, product type, requirement size, and deliverable.
• Define the problem, users, goals, scope, flows, features, metrics, risks, and acceptance criteria consistently.