再利用可能なAIエージェントスキルを個人的に使用するためのコレクション。 このリポジトリは、Codex、Claude Codeなどのリソースの手順、ワークフロー、および、スキルのフォーマットと互換性のあるリソースを維持します。

リポジトリ構造

各スキルは、独自のトップレベルのディレクトリに住んでいます。 ディレクトリ名は [name にマッチしなければなりません。

[SKILL.md以外に、agents/、[references/、[scripts/]]、または[assets/]を、スキルを実際に必要とする場合にのみ追加します。

##クロスプラットフォームの互換性

• [SKILL.mdは、スキルのクロスプラットフォームコアです。 トリガー条件とプライマリワークフローを定義します。
• [references/, scripts/], [assets/]は、必要なときにのみ読み込まれるサポートリソースを含みます。
• [[agents/openai.yamlは、表示名、短い説明、デフォルトプロンプト、およびその他のインタフェース機能のオプションのCodex / OpenAI製品メタデータです。
• クロードコードは[agents/openai.yamlに依存しません。このファイルは[SKILL.md]の普遍的な指示を置き換えたり変更したりしません。
• スキルには複数のエージェントプラットフォーム用のオプションのアダプターが含まれる場合がありますが、コアワークフローは単一のプラットフォームに依存しないはずです。

スキル

##加速ドメイン学習

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.