Content
86%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 tight, well-structured skill body that externalizes scene code appropriately and provides a runnable entry point. The main gap is the absence of an explicit render/validation feedback loop in the workflow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and sectioned with terse bullets, assumes Claude knows Manim, and avoids explaining what the library is; every section earns its tokens. | 5 / 5 |
Actionability | A copy-paste smoke-test command (`manim -ql assets/network_graph_scene.py NetworkGraphExplainer`) plus a real starter asset give concrete entry points, but most workflow guidance is instructional rather than executable code, leaving minor gaps. | 4 / 5 |
Workflow Clarity | The seven-step Workflow plus Render Conventions give a clear sequence with implicit checkpoints (smoke test first, upgrade only after timing is stable), but there is no explicit validate-then-proceed feedback loop. | 4 / 5 |
Progressive Disclosure | A well-organized overview SKILL.md points to a single real, one-level-deep bundle file (assets/network_graph_scene.py) via a clearly signaled markdown link, with no nested references and scene code correctly externalized. | 5 / 5 |
Total | 18 / 20 Passed |