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metric-diagnostics

Diagnose why a metric changed or differs from expectation. Use when the task is to identify likely drivers of a metric movement, anomaly, gap, or discrepancy.

62

Quality

73%

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tessl review fix ./packages/opencode/src/skill/builtin/.bundle/data-analytics/workflows/metric-diagnostics/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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 body is a well-sequenced, actionable methodology workflow with explicit guardrails, validation gates, and checklists. Its main weaknesses are repetitive source-selection emphasis and the absence of worked examples that would lift actionability and conciseness.

Suggestions

Consolidate the source-selection guidance (currently spread across 'Source Discovery And Verification', 'Source Access Guardrail', and step 2) into one place to reduce repetition and tighten conciseness.

Add one short worked decomposition example (e.g., a metric change broken into contribution shares with a reconciled residual) to move actionability toward fully concrete coverage.

Tighten the repeated $gather-business-context invocations to a single canonical pointer plus context-specific exceptions.

DimensionReasoningScore

Conciseness

Mostly efficient with no basic-concept padding, but source-selection guidance is repeated across three sections and $gather-business-context is invoked four times, so it could be tightened; not a 4 due to this noticeable repetition.

3 / 5

Actionability

Provides concrete checklists (grain, filters, joins, freshness, lineage), specific driver dimensions (model family, segment, cohort), and explicit interpretation rules (contribution share, numerator/denominator, reconcile residual); not a 5 because there are no worked numerical examples.

4 / 5

Workflow Clarity

Clear six-step sequence with an explicit source-availability guardrail, a pattern-verification gate before searching causes, an iteration loop with a stop condition, and a handoff checklist; not a 5 because a couple of checkpoints are soft ("verified or explicitly marked uncertain") rather than rigid.

4 / 5

Progressive Disclosure

Well-organized into labeled sections with clearly signaled cross-skill references ($gather-business-context, $analyze-data-quality, $build-report) and no nested references; not a 5 because all methodology is inline with no overview-pointing-to-details split, and no bundle files exist to warrant a higher structure score.

4 / 5

Total

15

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20

Passed

Description

78%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 well-structured with an explicit what and a concrete Use-when trigger clause listing multiple natural synonyms. It is specific to a clear niche with only minor overlap risk against related data-analysis skills.

DimensionReasoningScore

Specificity

Names the domain and 1-2 concrete actions ("Diagnose why a metric changed", "identify likely drivers"), but does not list a comprehensive set of distinct actions; not a score of 4 because it lacks several specific separate actions.

3 / 5

Completeness

Clearly states what ("Diagnose why a metric changed or differs from expectation") and an explicit "Use when..." clause with concrete trigger phrases, matching the top anchor exactly.

5 / 5

Trigger Term Quality

Strong natural keyword coverage with synonyms users would say ("metric changed", "movement", "anomaly", "gap", "discrepancy"); not a 5 because a few common phrasings (e.g., "why did X change", "drivers") are not exhaustively covered.

4 / 5

Distinctiveness Conflict Risk

Clear niche (metric diagnostics) with distinct triggers and minimal conflict risk; not a 5 because there is minor overlap with closely related data skills (data quality, business analysis) referenced in the body.

4 / 5

Total

16

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20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
XiaomiMiMo/MiMo-Code
Reviewed

Table of Contents

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