Conciseness

Mostly high-value, non-inferable content (channel model, hard-refusal table, known limitations), but refusal rules are stated three times — the '禁止读取' table, the Phase 0 '不要做的事' list, and the 'Refusals' section — and the contamination grep pattern appears twice (body self-check and main-Claude call check). This fits 'mostly efficient but includes some unnecessary explanation or could be tightened' better than 'minor instances'.

Actionability

Concrete and largely executable: a fully filled JSON output example, exact grep regexes (e.g., grep -E '\d+\s*[wWmMkK万]|播放|实绩|实际'), enumerated refusal codes, a copy-paste Task prompt contract, and a parseability check via python3 -c "import json; json.loads(...)". Not 5: 'script_hash': '<sha256:12 of script content>' gives no command for computing the hash, a minor executable gap.

Workflow Clarity

Phases 0-3 are clearly sequenced with explicit validation checkpoints: whitelist path validation in Phase 0, a mandatory post-read contamination grep with a defined degradation rule (confidence dropped to medium, refusal set to non_blind_warning, snippet captured), refusal codes for every failure mode, a strict JSON-parseability requirement, and a re-prompt feedback loop for the main Claude's call contract. This matches the anchor with explicit validation steps, feedback loops, and checklists.

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ absent), so everything lives in one ~205-line SKILL.md; it is well organized with clear section headers, tables, and a worked example. Not 5: the call-contract template and refusal-code reference are inline content that could plausibly sit in a separate one-level-deep reference file, and at this length the single-file structure is adequate rather than exemplary.

Specificity

Lists multiple concrete actions covering inputs ('Receives ONLY script_path + rubric_notes_path'), outputs ('Outputs strict JSON: 9 dimensions × {score 0-5, confidence enum, one-line reason}'), refusals ('Hard refuses to Read .cheat-state.json, predictions/*'), and callers. Not 5: the 'blind 7-dim rubric scoring' vs '9 dimensions' mismatch leaves a minor accuracy/coverage gap, which fits the 'minor gaps' anchor better than comprehensive coverage.

Completeness

The 'what' is explicit (blind rubric scoring of a script with strict JSON output), and the 'when' is present as equivalent trigger guidance ('Called via Task tool by cheat-score / cheat-predict / cheat-bump' plus 'do NOT invoke from main conversation'), so it is not capped at 3. Not 5: the conditions under which a blind score is needed (e.g., which caller phase) are not stated, so the 'when' could be more explicit.

Trigger Term Quality

Good coverage of the terms the actual invoker would use: 'Called via Task tool by cheat-score / cheat-predict / cheat-bump', 'blind', 'context-isolated score'. Not 5: a few natural synonyms ('blind sub-agent', 'uncontaminated score') and file-extension-style triggers are missing; not below 4 because the core trigger phrases are all present and natural.

Distinctiveness Conflict Risk

A clear niche with distinct triggers: 'channel B in the 3-channel calibration model (A=main, B=blind sub, C=cross-model)' plus three named calling skills and an explicit 'NOT a user-facing skill — do NOT invoke from main conversation' guard. Minimal conflict risk with any other skill.