Content
71%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 well-structured, mostly actionable catalog with lean tables and a clear selection decision tree. Its main weakness is progressive disclosure: the SKILL.md inlines reference material and fails to link to the existing references/ bundle files, which are also inconsistent with the body's model IDs.
Suggestions
Link to the existing references/ bundle files from the body (e.g., 'See references/available-models.md for per-model specs') so the overview points to detailed material one level deep.
Reconcile model identifiers between SKILL.md (kling-v2-6, kling-v2-5-turbo) and references/available-models.md (kling-pro, kling-v1.5), which currently disagree.
Make the API example self-contained by defining BASE and the Authorization header, and add a brief response/polling example since video generation is asynchronous.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and table-driven with no concept-explanation padding, but the decision tree partially repeats recommendations already conveyed in the model tables and 'Recommended' subsections. | 4 / 5 |
Actionability | Concrete model IDs, endpoints, a decision tree, and an executable Python API example give mostly actionable guidance; minor gaps include undefined BASE/headers variables and no response-handling example. | 4 / 5 |
Workflow Clarity | The model-selection decision tree provides a clear, sequenced branching workflow; as a reference catalog there is no destructive operation requiring validation checkpoints. | 4 / 5 |
Progressive Disclosure | Section structure is clear, but the body inlines the full catalog while five references/*.md bundle files exist and are never linked or signaled from SKILL.md, leaving them orphaned. | 3 / 5 |
Total | 15 / 20 Passed |