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 well-structured instruction skill: the workflow is sequenced, safety-conscious (report-don't-remediate is stated in the workflow, Mindset, and Anti-Patterns), and grounded in executable commands and verified bundle files. It stays lean and pushes detail to a one-level-deep reference; the remaining gaps are a missing error-recovery path for a failed fetch and slight redundancy between the Workflow, Mindset, and Anti-Patterns sections.
Suggestions
Add one error-recovery step to the Workflow (e.g., 'If the fetch fails or the shape check rejects the payload, retry with a pinned commit URL as the script's first argument, then stop and report rather than proceeding with stale data') to earn a validation feedback loop.
Trim the Mindset section to bullets that don't restate Workflow steps (the cardinality-not-preference point appears in both) to cut tokens without losing guidance.
Include one short worked example of a finished finding (layer name, cardinality, the two conflicting tool ids, and the cited `note` field) so step 4's cross-referencing has a concrete template to follow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is efficient — it explains nothing Claude already knows (no what-is-MCP filler), and every section (Gotcha, Workflow, Mindset, Anti-Patterns) carries task-specific information. Minor trim opportunities exist: the Anti-Patterns' BAD/GOOD pairs and the Mindset bullets partially restate points already made in the Workflow, matching the 'efficient; minor instances of over-explanation' anchor rather than the every-token-earns-its-place anchor. | 4 / 5 |
Actionability | Guidance is mostly executable: copy-paste commands (`node scripts/fetch-comparison-data.js > /tmp/context-radar-data.json`, `collect-active-tools.sh <project-dir>`), concrete destination paths, and worked mapping examples (`cargo-tokensave/latest/bin/tokensave` → id `tokensave`). Minor gaps keep it off the 5 anchor: the cross-referencing in step 4 is described but has no example of a worked finding, and there is no sample invocation output for the collection script. | 4 / 5 |
Workflow Clarity | A clearly sequenced 6-step workflow with most checkpoints present: the fetch script validates the dataset shape (`isDataset` check), findings are classified into distinct types (redundancy, removal, staleness, phantom-tool), and the destructive-action risk is handled by an explicit 'Report, don't remediate' gate requiring user confirmation. Not the 5 anchor because there is no explicit error-recovery loop (e.g., what to do when the fetch fails or the shape check rejects the payload). | 4 / 5 |
Progressive Disclosure | SKILL.md is a genuine overview: the full field list is split into `references/data-shape.md` (verified to exist, one level deep, no nested references), data operations live in the two scripts, and the References table signals each resource with a 'When to Use' column. This matches the clear-overview / well-signaled one-level-deep-references anchor; the body's inline field discussion is correctly limited to the two load-bearing fields. | 5 / 5 |
Total | 17 / 20 Passed |