Content
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is highly concise and cleanly organized with a correctly referenced executable script, but it reads as a reference overview rather than a sequenced, fully-actionable workflow with validation checkpoints.
Suggestions
Add a short sequenced "How to run an audit" workflow: run scripts/geo_checker.py, review reported issues, fix the flagged page elements, re-run until the average score passes 60.
Convert the most decision-oriented tables (e.g., AI crawler access) into concrete directive steps or a short runnable checklist Claude can follow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is almost entirely compact tables with no filler and no explanation of concepts Claude already knows, matching the lean-and-efficient anchor 3. | 3 / 3 |
Actionability | Concrete specifics are present (named schema types, crawler user-agents, a "< 2.5s" threshold, and a real executable checker command), but most sections are reference/landscape tables rather than direct runnable instruction, fitting the some-concrete-but-incomplete anchor 2. | 2 / 3 |
Workflow Clarity | Content is organized by topic but a GEO audit is not sequenced into explicit steps with validation checkpoints; the checker script could form a validate-fix-retry loop but is not wired into the body, matching the steps-present-checkpoints-missing anchor 2. | 2 / 3 |
Progressive Disclosure | The body is well-organized into numbered sections and references exactly one real, one-level-deep bundle file (scripts/geo_checker.py, which exists and is cited with its command), matching the well-signaled-references anchor 3. | 3 / 3 |
Total | 10 / 12 Passed |