Content
67%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 well-structured and lean, with clear mode-based workflows and useful schemas/thresholds, but it stays at the guidance level without executable code or runnable commands, which limits actionability.
Suggestions
Add a minimal runnable eval-runner snippet (e.g. a short Python function iterating the golden dataset and reporting pass_rate) so the eval pipeline is copy-paste executable rather than described in prose.
Include a concrete one-command rollback example (registry YAML diff or CLI invocation) instead of describing rollback only conceptually.
Tighten or remove the trailing 'To initialize.../To execute evals...' sentences that restate the preceding sections, recovering tokens without losing information.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean and assumes Claude's competence without explaining what prompts or LLMs are, but the trailing 'To initialize...' / 'To execute evals...' lines restate the preceding prose, adding minor redundancy. | 4 / 5 |
Actionability | Provides concrete schemas, directory layouts, pass thresholds, and step lists, but offers no executable code or runnable commands for the eval runner or registry init — the 'To execute evals' line describes the process rather than giving copy-paste implementation. | 3 / 5 |
Workflow Clarity | Mode 3 lays out a clear 8-step lifecycle with COMPARE/REVIEW/MONITOR gates and a rollback playbook that re-runs evals to verify, but explicit 'only proceed when eval passes' gating language between steps is only implied. | 4 / 5 |
Progressive Disclosure | Well-organized into clearly signaled modes, tables, and sections with no bundle files needed; not a 5 only because there is no external reference structure to split out, which this self-contained skill does not strictly require. | 4 / 5 |
Total | 15 / 20 Passed |