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Use when asked about Studio job analytics — compute hours, user spend, failure rates, cost estimation, cluster usage. Generates and maintains dc-knowledge/jobs/index.md.
Passed 8 of 13 test cases with the skill, and 4 of 13 without it.
and 5 more in the full report
7 fixed6 passed either way8 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
What format and timezone must be used for job creation timestamps in the Date column of dc-knowledge/jobs/index.md?
| Checked | Without skill | With skill |
|---|---|---|
| Formats date column values as YYYY-MM-DD HH:MM in UTC timezone. | Missed | Passed |
The instructions your model reads when the skill loads.
You are now loaded with the datachain-jobs skill. Maintain a jobs analytics file at dc-knowledge/jobs/index.md. Follow the 3-step flow below exactly.
python3 {skill_dir}/scripts/jobs.py --plan
"studio_available": false → report the error message and stop."up_to_date": true → skip to Step 3."up_to_date": false → continue to Step 2.python3 {skill_dir}/scripts/jobs.py --fetch [--days N] [--limit N] [--enrich]
--days N from the user's request if stated (e.g. "last 7 days" → --days 7). Default: --days 30.--enrich only when the question requires duration, workers, or cluster data AND enriched: false in an existing index — tell the user it makes one API call per terminal job.Write dc-knowledge/jobs/index.md using EXACTLY this format:
---
generated: <generated from script output>
days_covered: <days_covered>
total_jobs: <filtered_count>
failed_count: <failed_count>
complete_count: <complete_count>
running_count: <running_count>
other_count: <other_count>
enriched: <true|false>
duration_note: "Wall-clock duration (submit→finish). Null when enriched=false or job still running."
truncated: <true|false>
test cases passed, vs 4 / 13 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.