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datachain-jobs

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.

75

Quality

94%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

96%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A tight, highly actionable workflow with exact commands, an explicit output template, and validation checkpoints throughout. The only minor gap is length-relative progressive disclosure, where the body is slightly above the simple-skill threshold despite good structure.

DimensionReasoningScore

Conciseness

Lean and efficient with no concept exposition or padding; every line is a command, field mapping, or rule, assuming Claude's competence as in the anchor-5 example.

5 / 5

Actionability

Fully executable: exact commands with flags ('--fetch [--days N] [--limit N] [--enrich]'), a copy-paste markdown template with per-field mappings, and concrete arithmetic/price formulas with a worked example.

5 / 5

Workflow Clarity

Clear 3-step sequence with explicit validation checkpoints — '--plan' returns studio_available/up_to_date to gate the flow, 'If the script fails -> report the error and stop', and enrichment/truncation gating — matching the anchor-5 validate-fix-retry pattern for a batch fetch-and-write operation.

5 / 5

Progressive Disclosure

Well-organized sections with a single one-level-deep reference to the verified bundle script 'scripts/jobs.py' that holds the implementation detail; at ~88 lines it sits just past the under-50-line simple-skill case, so it lands at 4 rather than 5.

4 / 5

Total

19

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A specific, well-triggered description that clearly states both capabilities and invocation conditions with concrete trigger phrases. The only minor gap is relying on the category term rather than inline synonyms, which keeps trigger-term quality at 4.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('compute hours, user spend, failure rates, cost estimation, cluster usage') plus 'Generates and maintains dc-knowledge/jobs/index.md', giving comprehensive coverage matching the anchor-5 example.

5 / 5

Completeness

Explicitly answers 'what' (the five analytics computations plus index maintenance) and 'when' ('Use when asked about Studio job analytics'), with concrete trigger phrases in the YAML frontmatter satisfying the anchor-5 requirement.

5 / 5

Trigger Term Quality

Natural triggers are supplied via the YAML triggers list ('how many hours', 'who ran jobs', 'job cost') and 'Use when asked about Studio job analytics', but the body leans on the category phrase rather than surfacing synonyms/extensions inline, leaving it just below comprehensive.

4 / 5

Distinctiveness Conflict Risk

Scoped to 'Studio job analytics' with specific triggers and a dedicated output file, giving a clear niche with minimal overlap risk against other skills.

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
datachain-ai/datachain
Reviewed

Table of Contents

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