Conciseness

The body is dense policy prose with no explanations of concepts Claude already knows, and volatile pricing is correctly delegated to live verification rather than inlined. A few hedges could be trimmed — e.g., 'not a configuration change or an obligation to use Terra for a clearly harder task' and 'These are policy labels, not a guarantee every model/client supports every combination' — so it sits at 'efficient with minor instances of over-explanation' rather than the every-token-earns-its-place 5 anchor.

Actionability

For an instruction-only skill the guidance is concrete and executable: a decision table mapping work types to exact model/effort pairs ('Luna Light/Low', 'Terra Medium', 'Sol Medium or High', 'Astra Light/Low or Medium'), explicit effort-label mappings (low/medium/high/xhigh/max/ultra), a Fast default, and a required-output checklist including acceptance and stop conditions. It falls short of 5 because there is no worked example of a finished recommendation, which would make the expected output format copy-paste ready.

Workflow Clarity

'Choose a route' is a clear numbered 4-step sequence whose required output embeds validation (acceptance check, stop/escalation condition), and the continuity guardrail provides an explicit error-recovery feedback loop: handoff with verified facts, fresh bounded unit, and escalation to Sol High 'only after the failure repeats in the fresh, clean unit'. This matches the 5 anchor's clear sequence with explicit validation and feedback loops; this is an advisory skill, so the destructive/batch cap does not apply.

Progressive Disclosure

The SKILL.md body is an overview, and both referenced bundle files exist and are well matched to their signals: 'read [policy evidence](references/policy-evidence.md)' for source rationale and 'Read [evaluation plan](references/eval-plan.md) when testing or revising this skill'. References are one level deep (the reference files link only to external documentation), clearly signaled, and the split keeps provenance and eval material out of the main body — matching the 5 anchor.

Specificity

The description lists three concrete decision actions — 'Choose cost-efficient Codex/OpenAI models (Astra, Sol, Terra, Luna), reasoning effort (Light/Low, Medium, High, Extra High/xhigh, Max, Ultra), and Fast mode' — each with a comprehensive enumeration of the specific values in scope. It matches the anchor for multiple specific concrete actions with comprehensive coverage; a 4 would require minor gaps, and none are evident within the skill's stated scope.

Completeness

It explicitly answers both questions: the what ('Choose cost-efficient Codex/OpenAI models..., reasoning effort..., and Fast mode') and the when ('Use when the user asks for model routing, effort selection, consumption optimization, spending analysis, or whether Fast is worthwhile in Codex'), plus a negative boundary. This matches the 5 anchor — concrete trigger phrases with both what and when explicit — rather than 4, where the when clause would be less specific.

Trigger Term Quality

Natural trigger phrases like 'model routing', 'effort selection', 'consumption optimization', 'spending analysis', and 'whether Fast is worthwhile' give good keyword coverage, and 'cost' appears in the exclusion clause. It falls short of the 5 anchor because common user phrasings such as 'which model should I use', 'cheaper model', or 'model choice' are not covered as synonyms.

Distinctiveness Conflict Risk

The niche is clear (Codex/OpenAI routing only) and conflict risk is minimized by the explicit exclusion 'Do not activate for ordinary programming without a routing, cost, model, or effort request, or for routing other providers'. This is a distinct-trigger skill with minimal overlap risk, matching the 5 anchor.