Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, actionable body with a real executable script, a clear workflow including verification, and proper progressive disclosure into one verified reference. The main weakness is mild verbosity in the worked-example matrix that could be trimmed.
Suggestions
Trim the coverage-matrix worked example (drop the '| ...' row and shorten emoji columns) to reduce tokens without losing the output-format illustration.
Define or inline the read/extract_acs/read_all helpers in scripts/ai-coverage.py so the example runs as printed.
Move the full coverage-trend block into the reference file, keeping only a one-line pointer in the worked example.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient and does not over-explain known concepts, but the worked-example coverage matrix (with emoji columns and a trailing '| ...' row) plus the trend block could be tightened; some illustrative detail competes with context. | 2 / 3 |
Actionability | Provides a real, executable openai-based script with a concrete system prompt, concrete YAML inputs, and a copy-paste-ready output matrix; the only pseudocode is trivial I/O glue (read/extract_acs/read_all). | 3 / 3 |
Workflow Clarity | A clear 5-step sequence (point mapper, run, read matrix, file action items, schedule+verify) with an explicit verification checkpoint in step 5 and reinforced in the anti-patterns table, satisfying the batch-operation validation expectation. | 3 / 3 |
Progressive Disclosure | Well-organized sections (Overview, When to use, How to use, Worked example, Anti-patterns, Limitations, References) with a single one-level-deep reference (continuous-coverage-and-verification.md) that exists on disk and is clearly signaled. | 3 / 3 |
Total | 11 / 12 Passed |