Content
92%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is concise, highly actionable, and clearly sequenced with the verdict gate as its built-in validation loop, but progressive disclosure is weakened by a broken reference path.
Suggestions
Create references/canary-thresholds.yml (the file is referenced in Step 2 but the references/ directory is absent) so the linked full config resolves.
Consider splitting the long Step 6 example output and Step 7 CI YAML into a references/ file to keep SKILL.md as a tighter overview, signaling the link clearly.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean for its complexity: uses compact tables for metric classes, thresholds, observation windows and anti-patterns, with executable code blocks and no padding explaining concepts Claude already knows; every section earns its place. | 3 / 3 |
Actionability | Provides fully executable guidance: a runnable scipy-based canary_verdict.py (chi-square proportion test, Welch's t-test, promote/pause/rollback classification), a concrete thresholds YAML, and a copy-paste GitHub Actions integration with real action references. | 3 / 3 |
Workflow Clarity | A clearly sequenced 7-step process (pick metrics -> set thresholds -> significance -> window -> traffic-share scaling -> output -> CI integration) where the verdict gate itself is the validation checkpoint, with a three-state promote/pause/rollback feedback loop including automatic rollback on clear failure. | 3 / 3 |
Progressive Disclosure | The overview is well-signaled and one-level-deep, and scripts/canary_verdict.py is a real referenced bundle file, but references/canary-thresholds.yml is cited in the body yet the references/ directory does not exist, leaving a dead link that undermines navigation. | 2 / 3 |
Total | 11 / 12 Passed |