Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, well-crafted skill that provides concrete, actionable output specifications with a complete JSON schema and clear graph relationship constraints. Its main weakness is the lack of an explicit step-by-step workflow showing how to move from raw learner input to the final JSON output, which could lead to inconsistencies in how extraction and graph construction are sequenced. The extraction rules are excellent and the conciseness is commendable.
Suggestions
Add an explicit numbered workflow sequence (e.g., 1. Extract evidence from learner input, 2. Map evidence to skills, 3. Identify target career requirements, 4. Compute missing skills as careerRequires minus proven skills, 5. Build prerequisite chains, 6. Validate all relationships conform to the graph rules, 7. Output JSON).
Add a validation checkpoint: before emitting the final JSON, verify that every skill in missingSkills appears in careerRequires, every prerequisite pair references skills that exist in the plan, and nextBestSkill is actually in missingSkills.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It avoids explaining what Neo4j is, what JSON is, or how career planning works conceptually. Every section serves a direct purpose — extraction rules, output format, graph relationships — with no padding or unnecessary context. | 3 / 3 |
Actionability | The skill provides a complete, concrete JSON output schema with realistic example data, explicit graph relationship types, and specific rules for selecting nextBestSkill. Claude can directly produce conforming output without guessing at structure or semantics. | 3 / 3 |
Workflow Clarity | While the extraction rules and output format are clear, there is no explicit sequenced workflow (e.g., Step 1: extract evidence, Step 2: identify missing skills, Step 3: build prerequisite chains, Step 4: validate graph consistency). The process is implied but not explicitly ordered, and there are no validation checkpoints to verify graph integrity before output. | 2 / 3 |
Progressive Disclosure | For a standalone skill with no bundle files, the content is well-organized into clearly labeled sections (Purpose, Extraction Rules, Output Format, Graph Relationship Rules, Selecting nextBestSkill, Sparse Profiles). The length is appropriate and doesn't need external references. | 3 / 3 |
Total | 11 / 12 Passed |