Content
68%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.
A thorough, highly actionable scoring skill with clear sequencing and well-signaled references, weakened by inlined reference content and a lack of validation checkpoints in its batch workflow. Tightening the duplication and adding a verify step would raise the score.
Suggestions
Move the full per-category scoring tables into skills/blog/references/quality-scoring.md and keep only a brief category/points summary in SKILL.md to remove the duplication flagged in conciseness.
Add an explicit validation checkpoint to the batch workflow (e.g., verify each post was scored against every category before emitting the summary table, and re-extract on parse failure) to lift workflow clarity above the batch cap of 3.
Confirm the referenced bundle files (skills/blog/references/*.md, scripts/cognitive_load.py) actually ship with the skill, since no references/scripts/assets directories are present in the bundle.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and avoids explaining concepts Claude already knows, but it inlines the full 100-point scoring checklist (Content Quality, SEO, E-E-A-T, Technical, AI Citation tables) while simultaneously pointing to 'skills/blog/references/quality-scoring.md' for 'the full checklist' — duplicated/padded content that could be tightened. | 3 / 5 |
Actionability | Highly actionable: exact point values and pass thresholds (Flesch 60-70, sentence 15-20 words), a concrete 18-item AI phrase list, an executable command ('python3 scripts/cognitive_load.py'), JSON schemas, and copy-ready report/table templates covering common cases. | 5 / 5 |
Workflow Clarity | Steps are clearly sequenced (Step 1 extraction → Step 2 scoring → Step 3 AI detection → Step 4 rating → Step 5 report), but the skill explicitly supports batch mode ('--batch', directory scan) with no validation/verification checkpoint or feedback loop, which caps workflow clarity at 3 per the rubric. | 3 / 5 |
Progressive Disclosure | Good structure with a dedicated 'Reference documents' list and clearly signaled inline references (quality-scoring.md, eeat-signals.md, editorial-heuristics.md, etc.); the main gap is inlining the bulk of the scoring rubric that the reference file is meant to hold. | 4 / 5 |
Total | 15 / 20 Passed |