Content
86%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 well-architected reference skill: dense and assumption-respecting prose, a concrete tool-driven workflow with validation, and an excellent one-level-deep reference index. The main gaps are deferred copy-paste JSON patterns and a slightly implicit error-recovery loop in the workflow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and information-dense; it assumes MPS/baseLanguage competence and confines explanation to dataflow-specific mechanics (emit-instruction semantics, label-position semantics, null-guarding) that Claude would not already know, with no generic padding. | 5 / 5 |
Actionability | Concrete guidance with exact mps_mcp_* tool calls and parameters ('mps_mcp_create_model', 'modelName: "<lang>.dataFlow"', 'mps_mcp_check_root_node_problems') and specific concept names per step, but copy-paste JSON patterns are deferred to references/json-patterns.md rather than inlined. | 4 / 5 |
Workflow Clarity | A clear numbered 6-step Common-Path Workflow ending in a validation checkpoint ('Validate with mps_mcp_check_root_node_problems'), but the error-recovery feedback loop is only weakly implied ('For tricky cases, inspect...') rather than an explicit validate-fix-retry cycle. | 4 / 5 |
Progressive Disclosure | Exemplary structure: a concise overview body with a 'Reference Index' of six well-signaled one-level-deep references, each prefixed with an 'open when you need...' trigger, plus a 'Related Skills' section for cross-skill navigation. | 5 / 5 |
Total | 18 / 20 Passed |