Content
82%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.
The content is a strong, highly actionable single-file skill: concrete MCP tool calls, explicit output formats, and MongoDB-specific query-quality rules that go beyond general knowledge. Its main weaknesses are moderate redundancy (field validation and index checking repeated across sections) and the absence of an execute-and-verify feedback loop for generated queries despite having read-capable MCP tools available.
Suggestions
Consolidate the field-validation guidance (currently in 'Analyze Context and Validate Fields', Best Practices #4, and Error Prevention #1) and the index-coverage guidance (currently in four places) into single authoritative sections to remove repetition and save tokens.
Add an explicit verification step to the workflow: after generating a query, execute it via mcp__mongodb__find (or the aggregation tool) to confirm it runs and returns plausible results, fixing field names or operators if not — this closes the feedback loop in a database context.
Consider moving the operator-selection and array-operations reference lists (Best Practices #5–6) into a references/ file, which would shorten SKILL.md and reduce overlap between the Schema Analysis / Sample Document Usage sections and steps 1–2.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and imperative with little concept over-explanation, but contains noticeable repetition: field-name validation is stated three times ("always validate field names against the schema you fetched", Best Practices "Validate field names against the schema before using them", Error Prevention "Validate all field references against the schema") and index-coverage checking is repeated across four sections, and the array-field bullet restates the previous bullet's rationale. This fits anchor 4 (efficient with minor over-explanation that could be trimmed) rather than anchor 5, where every token earns its place. | 4 / 5 |
Actionability | Guidance is fully executable: exact MCP tool invocations with parameters (e.g., "mcp__mongodb__collection-schema({ database, collection, sampleSize: 50 })"), concrete response-format templates for both find and aggregation outputs, specific operator-selection rules with code examples ("arrayField.0": {$exists: true}), and a worked end-to-end example with its generated query. This matches anchor 5's copy-paste-ready coverage of common cases; anchor 4 would require gaps that are not present. | 5 / 5 |
Workflow Clarity | The process is clearly sequenced (gather context in a specified order → validate fields against schema → choose find vs aggregation → format response) with checkpoints before and after generation, plus an Error Handling section and a contextualizing example workflow. It falls short of anchor 5 because there is no explicit execute-and-verify feedback loop (e.g., running the generated query via mcp__mongodb__find and fixing it if it fails or returns unexpected results), which matters in a database context per the rubric's feedback-loop note; the destructive/batch cap of 3 does not apply since the skill is explicitly read-only. | 4 / 5 |
Progressive Disclosure | The single SKILL.md is well-organized with clear headers, a numbered process, and no nested or dead references (no bundle files exist, and none are referenced), so navigation is easy. However, at ~185 lines with some content overlap (Schema Analysis and Sample Document Usage largely restate steps 1–2, and the operator reference lists could live in a reference file), it fits anchor 4 (good structure, minor organization gaps) rather than anchor 5, and the under-50-line simple-skill exception does not apply. | 4 / 5 |
Total | 17 / 20 Passed |