Content
76%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 body is actionable and well-structured with executable config/commands and clear scoring/output schemas, but its batch workflow lacks validation checkpoints and error-recovery feedback loops, capping workflow clarity at 3.
Suggestions
Add explicit validation checkpoints after scraping and after running the tool (e.g. verify competitor_manual_signals.json is non-empty, confirm the CSV row count and that switching signals were detected, with a fix-and-retry loop on failure).
Fix the step numbering gap (Step 8 jumps to Step 10) so the sequence is coherent.
Trim over-explanatory asides like 'This is agent-driven because every competitor website has a different format' to tighten conciseness toward 5.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with phases, a scoring table, output schema, and cost estimates, but includes minor over-explanation such as 'This is agent-driven because every competitor website has a different format' that could be trimmed. | 4 / 5 |
Actionability | Provides copy-paste-ready config JSON, an executable bash invocation of scripts/competitor_signals.py, a concrete manual-signals JSON example, and full scoring/output schema tables — fully executable guidance. | 5 / 5 |
Workflow Clarity | Phases and numbered steps are sequenced, but this batch scraping/CSV-export skill lacks validation/verification checkpoints and error-recovery loops (Step 9 is missing entirely, jumping 8 to 10), which caps workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | The body is a well-organized overview that references the real one-level-deep bundle file scripts/competitor_signals.py via ${CLAUDE_SKILL_DIR}; minor gaps since there are no references/ or assets/ directories to further separate detail. | 4 / 5 |
Total | 16 / 20 Passed |