Content
77%Weight 40%Scale 1-3Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is highly actionable with a clear, well-validated workflow, but it is a long monolithic document that could be tightened and could split its runtime-dispatch detail into a one-level-deep reference file.
Suggestions
Move the runtime-dispatch detail (Shape A/B and the inline-dispatch-block mechanics) into a separate reference file referenced one level deep from SKILL.md to improve progressive disclosure.
Trim the base64-alphabet and 'bot review' rationale and consolidate repeated 'fail loud, don't substitute' statements to reduce token cost.
Drop precise source line-number citations (e.g., 'evaluation_handlers.py:1324, 1333') that will rot and add little actionable value for the agent.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient and task-relevant, but it is padded with internal line-number citations and over-explanation (e.g., the base64-alphabet rationale and repeated 'bot review' emphasis) that could be tightened without losing clarity. | 2 / 3 |
Actionability | It gives the exact MCP tool name, tool-discovery query, per-mode arguments, dispatch-block format and extraction procedure, per-runtime Task/delegation patterns, and a copy-paste synthesis template — fully executable guidance. | 3 / 3 |
Workflow Clarity | The four-step process (parse args → gather context → call MCP → branch on response) is clearly sequenced with explicit validation gates (proceed only when both context fields are populated) and error-recovery fallbacks for each response shape. | 3 / 3 |
Progressive Disclosure | It is a single ~270-line file with no bundle/reference files, and the detailed runtime-dispatch mechanics (Shape A/B, inline dispatch block) that could be split out are all inline, so structure is present but content that should be separate is not. | 2 / 3 |
Total | 10 / 12 Passed |