Content
57%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 body delivers strong, concrete SDK coverage through runnable Java examples, but is padded with templated filler sections, lacks an explicit validated workflow for the multi-step multivariate train/inference process, and keeps all reference material inline with no bundle files. The result is a serviceable but not exemplary SKILL.md.
Suggestions
Remove the 'Trigger Phrases', 'When to Use', and 'Limitations' boilerplate sections (move genuine triggers into the frontmatter description) and add guidance for handling the pinned 3.0.0-beta.6 version.
Add validation checkpoints to the multivariate workflow: poll training until status is READY before running inference (show the imported SyncPoller being used), verify batch detection results, and confirm before deleteMultivariateModel.
Split the bulk API examples into one-level-deep reference files (e.g. references/univariate.md and references/multivariate.md) and keep SKILL.md as a concise overview with clearly signaled links.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is code-heavy and largely lean, but contains several removable sections: a 7-line 'Trigger Phrases' list that belongs in the description, a vacuous 'When to Use' ('This skill is applicable to execute the workflow or actions described in the overview'), generic 'Limitations' boilerplate, and a time-sensitive pinned beta version (3.0.0-beta.6) with no versioning guidance. This fits 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the minor-trimming of anchor 4. | 3 / 5 |
Actionability | Extensive concrete Java examples cover client creation, batch/streaming/change-point detection, multivariate training, inference, last-point detection, model management, and error handling — mostly executable. It falls short of copy-paste-ready anchor 5 because the univariate series uses a '// ... more data points' placeholder and the training snippet imports SyncPoller but never polls, calling the 'long-running operation' synchronously. | 4 / 5 |
Workflow Clarity | The multivariate flow is genuinely multi-step and the snippets follow the implied Train → Inference → Results order, with a training-status check shown, but there is no explicit sequencing, no 'wait until status READY before inference' checkpoint, and no validation loop. Per the rubric, batch operations (model training, batch inference) and a destructive deleteMultivariateModel without validation steps cap workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | No bundle files exist and all ~260 lines of API-pattern reference are inlined in SKILL.md with no external references at all. Section headers make it navigable (avoiding anchor 2's wall-of-text), but content that belongs in separate reference files is inline, matching 'some structure but could be better organized'. | 3 / 5 |
Total | 13 / 20 Passed |