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create-data-context

Create, update, inspect, or repair Data Analytics semantic layers. Use when the user asks to save data context or create a semantic layer that future Data Analytics work can inspect and cite.

69

Quality

85%

Does it follow best practices?

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

78%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.

The content is a well-structured, actionable overview that uses real bundle references appropriately and keeps detail off the main page. It could be slightly tighter and could surface its existing validation scripts more explicitly in the workflow.

Suggestions

Replace some of the prose routing restatements across sections with a single canonical routing reference to reduce repetition and tighten conciseness.

Invoke the existing scripts (e.g., validate_data_context_contract.py, data_analytics_preflight.py) as explicit validate-then-fix commands in the workflow to strengthen actionability and add a concrete feedback loop.

Number the create-or-update steps with an explicit 'validate -> fix -> re-validate -> only then persist' checkpoint to lift workflow_clarity toward 5.

DimensionReasoningScore

Conciseness

The body is largely lean procedural prose without concept padding, but some table cells and repeated routing statements across sections could be tightened, keeping it just below fully lean.

4 / 5

Actionability

Concrete destinations, file paths, and a decision table provide actionable guidance; it is instruction-only without inline executable commands, but the guidance is specific enough to act on with only minor gaps.

4 / 5

Workflow Clarity

The intake-to-persistence flow is sequenced with validation mentioned before writing, satisfying the destructive/batch validation requirement; minor gaps remain because checkpoints are prose rather than explicit validate-fix-retry loops.

4 / 5

Progressive Disclosure

The body is a clear overview that signals one-level-deep references (source-inventory.md, skill-template.md, weekly-polling-automation.md), all verified to exist, and deliberately keeps detail in those linked files.

5 / 5

Total

17

/

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.

The description is specific, complete, and distinctive, clearly stating both capability and trigger conditions in third person. It is slightly light on synonym variation in trigger terms, which keeps trigger_term_quality at 4 rather than 5.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ("Create, update, inspect, or repair") against a clearly named domain ("Data Analytics semantic layers"), matching the comprehensive-coverage anchor.

5 / 5

Completeness

Explicitly answers both what (create/update/inspect/repair semantic layers) and when ("Use when the user asks to save data context or create a semantic layer...") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes natural trigger phrases ("save data context", "create a semantic layer") but offers few synonyms or variations a user might naturally say, leaving minor coverage gaps.

4 / 5

Distinctiveness Conflict Risk

Targets a clear niche (semantic-layer maintenance) and the skill body even routes ordinary data work to a separate index skill, minimizing conflict risk.

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

referenced_paths_exist

Referenced path issues: 2 missing, 2 deeper-than-1-level

Warning

Total

15

/

16

Passed

Repository
XiaomiMiMo/MiMo-Code
Reviewed

Table of Contents

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