Content
27%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is excessively verbose, repeating its own description multiple times and including large amounts of boilerplate that doesn't add value for graph interpretation specifically. While it contains some useful domain-specific content (statistical reporting standards, audience-specific templates, common pitfalls), the actionability is undermined by referencing non-existent scripts and providing API examples that appear aspirational rather than executable. The lack of bundle files means all content is crammed into one massive document with no progressive disclosure.
Suggestions
Eliminate all repeated content: the skill description appears 3 times, and boilerplate sections (Output Requirements, Error Handling, Input Validation, Response Template) should be removed or drastically condensed since they're generic to all skills.
Split detailed reference content (graph type tables, audience templates, common patterns, statistical reporting standards) into separate files in a `references/` directory and link to them from a concise SKILL.md overview.
Resolve the script path inconsistency: `scripts/main.py` vs `scripts/graph_interpreter.py` — pick one and ensure the code examples are consistent and actually executable against provided bundle files.
Replace the generic 5-step workflow with a graph-interpretation-specific workflow that integrates the quality checklist as explicit validation gates (e.g., 'Step 2: Verify axes are labeled and units present before extracting statistics').
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose and repetitive. The skill description is copy-pasted multiple times (in 'When to Use', 'Key Features', etc.). There are massive sections of boilerplate (Output Requirements, Error Handling, Input Validation, Response Template) that add little value. The 'Implementation Details' section says 'See ## Workflow above' when Workflow is actually below. Many sections explain things Claude already knows (what a forest plot is, what a box plot is). The document is easily 3-4x longer than needed. | 1 / 3 |
Actionability | The skill provides code examples with specific API calls (GraphInterpreter class, CLI commands), but none of it is verifiably executable — it references modules like `scripts.graph_interpreter` and `scripts/graph_interpreter.py` that don't exist in any bundle. The code examples look like aspirational API design rather than working code. The statistical output structures and audience-specific templates are useful concrete guidance, but the disconnect between `scripts/main.py` (referenced in workflow) and `scripts/graph_interpreter.py` (referenced in code) undermines actionability. | 2 / 3 |
Workflow Clarity | There is a numbered workflow (5 steps) and a quality checklist with before/during/after phases, which provides decent structure. However, the workflow steps are generic and abstract ('Confirm the user objective', 'Validate that the request matches the documented scope') rather than specific to graph interpretation. There are no explicit validation checkpoints for the actual graph analysis process — the quality checklist is separate from the workflow and not integrated as verification gates. | 2 / 3 |
Progressive Disclosure | The document is a monolithic wall of text with no bundle files to reference. It mentions `references/` directory and `scripts/main.py` but no bundle files are provided. Everything is inlined — the supported graph types table, all code examples, all templates, all patterns — resulting in an extremely long document. Content like the detailed common patterns, audience templates, and graph type reference tables should be in separate files with clear navigation from the SKILL.md. | 1 / 3 |
Total | 6 / 12 Passed |