Conciseness

The body is dense and directive throughout — tables, checkpoints, and imperative lists — without explaining concepts Claude already knows; every line earns its place for the breadth covered.

Actionability

Although code-free, the instruction guidance is highly actionable: explicit table schemas (e.g. 'Task | AI role | Inputs and sources | ...'), a claim-control table, and a fill-in hypothesis template tell Claude exactly what to produce.

Workflow Clarity

An explicit 8-step sequence is punctuated by two validation checkpoints with feedback loops ('re-check until unresolved risks are explicitly owned or moved to needs decision'), matching the explicit-validation anchor.

Progressive Disclosure

The body is an overview that defers detail to four clearly-signaled, one-level-deep references (capability-risk-and-trust, interaction-and-agent-patterns, evaluation-and-states, worked-example), all of which exist as real files.

Specificity

Lists multiple concrete actions ('Design, specify, map, evaluate, or improve') plus a comprehensive set of dimensions to define (capability boundaries, user control, recovery, trust, evidence, uncertainty, permissions, evaluation), matching the comprehensive-coverage anchor.

Completeness

Clearly answers both 'what' (design/specify/map/evaluate AI workflows and which dimensions to define) and 'when' (concrete trigger phrases), plus a negative boundary ('Do not use for model training or prompt-only writing').

Trigger Term Quality

Explicit 'Trigger on' clause gives natural phrases users would say ('design this AI feature', 'chatbot design', 'plan this agent workflow') with synonyms across AI assistant, copilot, chatbot, agent, and automation.

Distinctiveness Conflict Risk

The design/specification framing and explicit exclusions carve a distinct niche, but broad triggers like 'build an AI feature' and 'AI automation' carry minor overlap risk with general agent-building skills, so it sits below the minimal-conflict anchor.