Content
88%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.
A well-structured, highly actionable skill body with strong workflow validation and a real bundled helper script. Main improvements are minor trimming and making the script/metric reference navigation slightly more explicit.
Suggestions
State the "LLMs are bad at exact token counting" rationale once near the script invocation; remove the near-duplicate in the Gotchas section.
Consolidate the 13 Gotchas bullets — merge the two min-data-threshold items and the two speaker-identification items — to cut length without losing the failure modes.
Add a short "References" pointer (e.g. "See scripts/mirror_metrics.py for the metric computation") so the bundle file is discoverable by navigation rather than only inside a code fence.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean and coach-toned with executable commands, but the "LLMs are bad at exact token counting" rationale is stated twice and the 13-bullet Gotchas section contains a few items that could be consolidated. | 4 / 5 |
Actionability | Fully executable: a copy-paste bash pipeline invoking a real bundled script, with the script's JSON output fields documented in a table and concrete output-format templates for both modes. | 5 / 5 |
Workflow Clarity | Clear phased sequence (0→1→2a/2b→3) with explicit validation checkpoints ("Require exit status 0", "Require both sides of the pipeline to exit successfully"), error-recovery on exit code 3, and minimum-data thresholds for the batch pattern mode. | 5 / 5 |
Progressive Disclosure | Well-organized into phases and a one-level-deep reference to scripts/mirror_metrics.py, but the script path sits inside a code block and the metric-field reference table is inlined rather than reached via a clearly signaled navigation pointer. | 4 / 5 |
Total | 18 / 20 Passed |