Content
70%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
An exceptionally clear, well-validated multi-step workflow with concrete commands, schemas, and error-recovery paths — workflow clarity is exemplary. It is held back by systematic repetition of file-naming/write-order details and, most importantly, by the fact that two-thirds of its referenced bundle files (all six prompts/ templates) are missing from the shipped bundle, leaving the core LLM-call steps unnavigable.
Suggestions
Remove the ".txt file naming" and ".json file naming" tables (and the per-step restatements of the txt-first rule) — the full directory layout and the checklist already convey this information once each, which would tighten conciseness.
Fix the progressive-disclosure integrity failure: either ship the six prompts/*.md template files referenced throughout (build_eval_query.md, build_evidence_assembly.md, build_meta_review_query.md, build_novelty_queries.md, build_novelty_analysis.md, build_refinement_feedback_query.md) or inline their essential query-construction details into SKILL.md.
For actionability, include at least one concrete example of a built eval query or evidence block inline, so the persona-review steps remain executable even if the template files are unavailable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and assumes Claude's competence (no concept over-explanation), but file names are stated three times — the directory-layout tree, the ".txt file naming" and ".json file naming" tables, and again in the checklist — and the txt-first write-order rule is restated per step. This is one systematic redundancy pattern that could be consolidated, matching "mostly efficient but could be tightened" rather than the minor-trim level of 4. | 3 / 5 |
Actionability | Provides concrete executable guidance: a copy-paste bash invocation for search_ai_papers.py with explicit script-resolution fallbacks, exact file paths, complete JSON output schemas, and a numeric decision-threshold table. Not 5 because the six core LLM-call steps (evidence assembly, query building, persona evals, meta-review, refinement) are pointers to template files rather than inlined executable examples. | 4 / 5 |
Workflow Clarity | Steps 0 through 7 are clearly sequenced with explicit validation checkpoints and feedback loops: the fast-fail user decision gate (Proceed/Refine/Abandon), per-failure fallbacks ("If ALL searches fail, proceed with unverified novelty", query-extraction fallback), reviewer disagreement resolution at >3 points, threshold-driven quality gate, and a capped refinement loop (max 2 iterations), capped off with a full checklist. | 5 / 5 |
Progressive Disclosure | The in-body structure is well designed — a directory map, a "How to use the resource files" note, and one-level-deep per-step callouts to 6 prompt templates and 3 reference files — but scored against the actual bundle, the 6 referenced prompts/*.md files (build_eval_query.md, build_evidence_assembly.md, etc.) do not exist; only the 3 references/ files are present. Dead links to the core query templates are a substantive bundle-integrity failure, not the minor organization gap of a 4. | 3 / 5 |
Total | 15 / 20 Passed |