Content
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A highly actionable, well-sequenced skill body with strong validation and failure-recovery loops. Its weaknesses are verbosity in the rationale prose and a monolithic structure with no progressive disclosure into separate reference files.
Suggestions
Tighten the rationale-heavy prose sections (e.g., 'When the user gives you a budget') to reduce token load without losing the operational facts.
Extract the inline submit_job parameter reference into a separate reference file and link to it, so the main workflow stays a lean overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is dense and almost entirely non-obvious operational knowledge (repl-vs-python kernel split, approval gate, harvest caps), but it is prose-heavy and long (~360 lines) with rationale passages that could be tightened, so it is mostly efficient rather than lean. | 2 / 3 |
Actionability | Provides fully executable, copy-paste-ready code (submit_job with real params, attach_job().result(), save_artifacts), specific commands (module avail, conda env list), and concrete gotchas (dst_filename no '/', bash -eo pipefail, wait $pid). | 3 / 3 |
Workflow Clarity | Sequenced end-to-end (compute_details → bind → discover → activate → submit → wait_for_notification → harvest → save_artifacts → close) with explicit validation checkpoints (run entrypoint before submit, ask before a third failed submit) and failure feedback loops (read exit_code/log → fix → resubmit; retry_after_user_action → ask_about_compute). | 3 / 3 |
Progressive Disclosure | Well-organized into clearly headed sections, but it is a monolithic single ~360-line file with reference-style detail (the submit_job parameter dictionary) inline and no file-level references to split content into, which the >50-line exception does not cover. | 2 / 3 |
Total | 10 / 12 Passed |