Content
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, highly actionable skill body with strong progressive disclosure backed by real reference files. The main weaknesses are generic best-practice padding and missing validation/feedback loops in the batch and pipeline workflows.
Suggestions
Add explicit validation/recovery checkpoints to the batch and multi-step pipeline patterns — e.g. check job state after `wait_on_done()`, retry or surface failed job logs before chaining the next step — to raise workflow_clarity to 3.
Trim the generic 'Best Practices' list (items like hardcoding credentials, logging, error handling are things Claude already knows) to domain-specific guidance such as instance-type sizing for genomics workloads or dxapp.json versioning conventions.
Remove the Overview section or fold it into the frontmatter description to avoid restating the same capability summary twice.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient with dense dxpy/dx calls, but includes content Claude already knows (generic 'Best Practices' like 'Never hardcode credentials', 'Include informative log messages', 'Always wrap API calls in try-except') and an Overview that restates the frontmatter, so it is 'mostly efficient but could be tightened' rather than fully lean. | 2 / 3 |
Actionability | Provides multiple copy-paste-ready, executable examples with real API calls and CLI commands — `dxpy.upload_local_file()`, `dxpy.download_dxfile()`, `dxpy.find_data_objects(classname='file', name='*.bam', ...)`, `@dxpy.entry_point('main')`, `dx build`, `dx login` — matching the 'fully executable code/commands; copy-paste ready' anchor. | 3 / 3 |
Workflow Clarity | A clear 5-step decision tree sequences capability selection, but the batch-processing and multi-step pipeline patterns launch/chain jobs with only `wait_on_done()` and no validation or failure-recovery checkpoint; per the rubric's batch-operation cap, missing validation holds workflow_clarity at 2 rather than 3. | 2 / 3 |
Progressive Disclosure | SKILL.md is an overview with quick-start examples inline, deferring detail to five clearly signaled one-level-deep references (e.g. 'See references/python-sdk.md for:'), and all five referenced files actually exist in references/, matching the 'clear overview with well-signaled one-level-deep references' anchor. | 3 / 3 |
Total | 10 / 12 Passed |