Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is concise, highly actionable, and well-structured with executable commands and real bundle files referenced one level deep. The only gap is the absence of explicit validation/feedback-loop checkpoints, though the read-only search workflow makes this a minor concern.
Suggestions
Add a brief validation note in the output-format section, e.g. check 'success' field and surface 'error' to the user before relying on 'items', to add an explicit checkpoint to the workflow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and efficient: credential guidance, a script table, dependency install, per-script argument tables, and executable examples — it does not explain what Reddit/YouTube is or pad with concepts Claude already knows. | 3 / 3 |
Actionability | Each script gets a fully executable invocation (e.g. 'python3 scripts/reddit_search.py "machine learning projects" --limit 5') with complete argument tables, defaults, and concrete examples that are copy-paste ready. | 3 / 3 |
Workflow Clarity | The sequence (install deps → run script → parse JSON output) is present, but there are no explicit validation checkpoints or feedback loops; this is acceptable for read-only search yet still lacks the explicit checkpoints the rubric rewards. | 2 / 3 |
Progressive Disclosure | This ~85-line simple skill is well-organized into clear sections (credentials, scripts, dependencies, parameters, output) with one-level-deep references to real bundle scripts that exist in ./scripts/; no nested reference chains. | 3 / 3 |
Total | 11 / 12 Passed |