Content
46%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.
The skill body is well-structured with real, clearly signaled references and useful validation commands, but it is weighed down by substantial generic boilerplate and lacks executable guidance for its core extraction task. Tightening the governance sections and documenting the actual input format would materially improve it.
Suggestions
Consolidate the overlapping governance sections (Output Requirements, Output Contract, Response Template, Error Handling, Failure Handling, Input Validation) into one concise block or a reference file to reduce boilerplate and token load.
Document the --papers input JSON format with a concrete schema/example and add a copy-paste command showing real extraction on a paper, not just --demo and validation commands.
Replace the abstract process Workflow with the concrete extraction sequence (parse inputs -> extract methods section -> identify protocol steps -> compare across papers -> validate output), keeping the validation checkpoints attached to those specific steps.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is noticeably verbose, carrying large generic boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status with a time-sensitive date, plus overlapping Output/Error/Failure/Response-Template sections) that add little skill-specific value; not as padded as score 1, but well below an efficient skill. | 2 / 5 |
Actionability | Provides concrete validation commands (py_compile, --help) and documents parameters, but the core extraction usage is incomplete: the --papers input JSON format is undocumented and the Example section is vague rather than copy-paste ready. | 3 / 5 |
Workflow Clarity | A sequenced workflow with validation hooks (Quick Check, fallback path, user checkpoints) exists, but the steps are generic process guidance rather than the concrete extraction sequence (parse papers, extract methods, identify steps, compare), so checkpoints feel abstract rather than task-anchored. | 3 / 5 |
Progressive Disclosure | Real one-level-deep references are present and clearly signaled (references/audit-reference.md, scripts/main.py, both verified to exist), with organized section headers; held back from 5 only by the large volume of inline boilerplate that could be consolidated into reference files. | 4 / 5 |
Total | 12 / 20 Passed |