Content
78%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 terse, well-structured meta-skill with clear safety guarantees, but its actionability is limited because the generation and scoring mechanics are described abstractly rather than with executable detail.
Suggestions
Specify how the four variations are generated (e.g., prompt template, model, parallel invocation) so the core mechanism is reproducible rather than implied.
Include the actual scoring rubric criteria per axis, not just the weights, so scoring is concrete and consistent across runs.
Add a short numbered workflow (generate → score → compare to original → apply or abort) to make the sequence and checkpoints explicit.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean ~48-line body using tables and bullets, assuming Claude's competence with no concept over-explanation; nearly every token earns its place. | 5 / 5 |
Actionability | Concrete specifics exist (AUTORESEARCH_NO_IMPROVEMENT code, autoresearch/${target} branch, SKILL.md.before-autoresearch backup) but the core mechanism — how variations are generated and how each axis is scored — is left abstract with no executable steps. | 3 / 5 |
Workflow Clarity | Clear generate→score→apply sequence with an explicit abort validation checkpoint and review/rollback feedback loops, though steps are spread across sections rather than given as a numbered list. | 4 / 5 |
Progressive Disclosure | Under 50 lines with no need for external references and well-organized sections (Inputs, Variations, Scoring, Safety, Versioning, Output), satisfying the simple-skill exception. | 5 / 5 |
Total | 17 / 20 Passed |