Content
85%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 highly actionable and well-sequenced with strong validation checkpoints and a clearly-signaled bundled script. The main weakness is conciseness — some rationale and thresholds are stated more than once across the phases and Gotchas.
Suggestions
De-duplicate the 'LLMs are bad at token counting' rationale and the ≥5-meeting / ≥3-per-group thresholds — state each once in Gotchas and reference it from the phases.
Consider moving the long metrics field table into a short reference block or the script's --help output to shorten the inline body.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with concrete commands and a compact metrics table, but contains repeated rationale (e.g., 'LLMs are bad at exact token counting' appears in both Phase 2a and Gotchas) and restated guidance (pattern-mode ≥5 threshold in Phase 2b and again in Gotchas) that could be tightened. | 3 / 5 |
Actionability | Fully executable: exact bash pipelines, the bundled script invocation with the --self flag, a JSON output-field table, and copy-paste output-format templates covering single-meeting and pattern modes. | 5 / 5 |
Workflow Clarity | Clear Phase 0→3 sequence with explicit validation ('Require exit status 0', 'Require both sides of the pipeline to exit successfully'), error-recovery feedback (exit code 3 handling), and minimum-data thresholds for the batch metric runs — matching the anchor 5 checklist pattern. | 5 / 5 |
Progressive Disclosure | A single one-level-deep bundled script (scripts/mirror_metrics.py) is clearly signaled and invoked; the body serves as a well-organized overview with sectioned phases and gotchas, appropriate for a single-script skill. | 5 / 5 |
Total | 18 / 20 Passed |