Composes three-part DACP bundles from skill library artifacts. Determines fidelity level, queries catalog, and records assembly rationale.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Compose deterministic agent communication bundles from skill library artifacts.
| Level | Name | Contents |
|---|---|---|
| 0 | PROSE | Intent markdown only |
| 1 | PROSE_DATA | Intent + structured JSON data |
| 2 | PROSE_DATA_SCHEMA | Intent + data + JSON Schema validation |
| 3 | PROSE_DATA_CODE | Intent + data + schemas + executable scripts |
For detailed criteria and decision tree, see @references/fidelity-levels.md
determineFidelity() to get proposed levelFor worked examples at each level, see @references/assembly-patterns.md
Full taxonomy at @references/handoff-taxonomy.md
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概要と使いどころ
Provides web accessibility best practices for semantic HTML, ARIA, keyboard navigation, color contrast, and screen reader patterns. Use when building UI components, reviewing accessibility, or when user mentions 'a11y', 'accessibility', 'ARIA', 'screen reader', 'keyboard navigation', 'WCAG'.
日本語の概要は準備中です。原文の説明を表示しています。
Active listening techniques for effective communication. Covers attending behaviors, paraphrasing, reflective listening, clarifying questions, empathic response, barriers to listening, listening in conflict, and cross-cultural listening. Use when building listening skills, improving understanding in conversation, mediating disputes, or analyzing communication breakdowns.
日本語の概要は準備中です。原文の説明を表示しています。
Adversarial spec-compliance PR review — cross-references diffs against approved specs, verifies runtime claims against source, detects competing PRs, audits scope/convention compliance. Use before merging.
日本語の概要は準備中です。原文の説明を表示しています。
Provides best practices for AI agent orchestration including MCP servers, A2A protocol, multi-agent coordination, and swarm architectures. Use when designing agent systems, configuring MCP servers, setting up agent teams, or when user mentions 'MCP', 'A2A', 'agent orchestration', 'multi-agent', 'swarm', 'agent team', 'LangGraph', 'CrewAI', 'AutoGen'.
日本語の概要は準備中です。原文の説明を表示しています。
Symbolic manipulation, equation solving, and algebraic structures for mathematical reasoning. Covers distributive law, factoring, completing the square, linear through polynomial equation solving, systems of equations (substitution, elimination, Gaussian elimination, matrix methods), algebraic structures (groups, rings, fields), modular arithmetic, polynomial theory, and inequalities. Use when solving equations, simplifying expressions, working with algebraic structures, or performing symbolic manipulation.
日本語の概要は準備中です。原文の説明を表示しています。
Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.
日本語の概要は準備中です。原文の説明を表示しています。