Conciseness

The body is dense with TypeSafe-specific guidance (primitive distinctions, Noul confidence semantics, state/instructions/criteria structure) and wastes little on concepts Claude already knows. A few passages carry philosophical framing ('Code owns the workflow; the model supplies programmable common sense') or expansive hedging that could be trimmed, so anchor 4 fits better than the perfectly lean anchor 5, and it is well above the noticeable padding of anchor 3.

Actionability

Concrete guidance exists — the .md-suffix trick for fetching Mintlify pages, the task-to-docs table, backticked state paths like `ticket.messages[0].text`, and per-pattern cookbook links — but there are no executable artifacts: no API call, SDK snippet, or example question/state JSON. Anchor 3 (some concrete guidance but incomplete, missing key details) fits; anchor 4 would require concrete code or commands, and everything executable is deferred to external live docs.

Workflow Clarity

The section order forms a coherent arc (read live docs → find the useful shape → design the judgments → compose and verify) and validation is explicit: 'Test representative cases and the resulting application behavior' plus failure triage ('inspect the exact state, questions, candidates, answers... Separate missing evidence, model errors, code errors, and service failures'). Anchor 4 fits — clear sequence with most checkpoints — falling short of anchor 5 because the sequence is implicit in section order rather than explicit numbered steps with checkpoints.

Progressive Disclosure

Sections are well organized and references are clearly signaled one level deep (docs index, a task-to-doc table, inline cookbook links); no local bundle files exist to structure. It falls short of anchor 5 because the offline fallback ('use available local docs or installed SDK types') points at unspecified resources, and a notable amount of design detail (primitive distinctions, question-design rules) is inlined in the overview rather than split into reference material.

Specificity

Names several concrete capabilities ('typed judgments and probabilities that code can combine', 'routing, ranking, extraction, verification, and interactive experiences'), but the core framing ('units of AI intelligence you can use like programming primitives') remains category-level rather than fully concrete operations. Fits anchor 4 — several specific actions with minor gaps — not 5, and clearly above the 1-2-action coverage of anchor 3.

Completeness

Explicitly answers both: what ('turn natural language and application state into typed judgments and probabilities that code can combine') and when ('Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision'). The 'when' clause is explicit with three concrete trigger conditions, matching anchor 5 rather than anchor 4 where the 'when' would need to be more specific.

Trigger Term Quality

Includes natural trigger phrases users would say ('when brainstorming what AI could make possible in an app', 'when an LLM prompt-and-parse step could become a structured decision') plus application keywords (routing, ranking, extraction, verification). Missing common synonyms such as classification, structured output, or scoring, so it fits anchor 4 (good coverage, a few natural terms missing) rather than 5's comprehensive synonym coverage, and is well above anchor 3.

Distinctiveness Conflict Risk

The niche is distinct (programmable common sense, typed judgments, named models TypeSafe/Jev), but 'brainstorming what AI could make possible in an app' and the LLM prompt-and-parse trigger create minor overlap risk with generic ideation, prompt-engineering, or structured-output skills. Fits anchor 4 (mostly distinct, minor overlap with closely related skills), not 5's minimal-conflict clear niche.