Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-crafted skill with excellent actionability and workflow clarity. The executable bash commands, structured output templates, and explicit validation steps make it highly usable. Minor verbosity in explanatory sections and some repetition of the agent-vs-human calibration concept prevent a perfect conciseness score, but overall this is a strong, production-ready skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some redundant explanations (e.g., explaining why agents differ from humans multiple times) and could tighten the workflow descriptions. The batch estimation section adds significant length but is justified by the complexity. | 2 / 3 |
Actionability | Provides fully executable bash commands for stack detection and codebase reading, concrete classification tables, specific output formats with exact templates, and clear step-by-step workflows. All guidance is copy-paste ready. | 3 / 3 |
Workflow Clarity | Excellent multi-step workflow with explicit validation (Step 4.5 self-check), clear sequencing (Steps 0-5), feedback loops for error correction, and specific checkpoints. The batch workflow also includes clear sequencing with consolidated output. | 3 / 3 |
Progressive Disclosure | Well-structured with clear overview and one-level-deep references to calibration.md, patterns.md, and honesty-rules.md. Content is appropriately split between the main skill and reference files, with clear signaling of when to consult each reference. | 3 / 3 |
Total | 11 / 12 Passed |