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product-business-analysis

Analyze product or business data to support a decision or recommendation. Use when a decision depends on metric-backed evidence, such as choosing a direction, prioritizing an opportunity, evaluating a change, segmenting users, sizing tradeoffs, or deciding what to do next.

60

Quality

70%

Does it follow best practices?

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SecuritybySnyk

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Fix and improve this skill with Tessl

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

Quality

Content

56%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 well-structured, clearly sequenced decision-support workflow with strong guardrails and validation hooks. Its main weaknesses are inline catalog detail that belongs in reference files, guidance that stays at the directive level without examples, and some redundant framing across sections.

Suggestions

Move the decision-lens catalog (Current scale through Coverage) into a one-level-deep reference file (e.g. references/lenses.md) and keep a two-line pointer plus the most-used lenses in SKILL.md.

Add one short worked example of turning a question into a framed analysis (e.g. "should we prioritize mobile users?" -> data questions, comparison, denominator) to make the directive guidance concrete.

Tighten sections 1, 3, and 5 by stating the decision-first requirement once and removing repeated framing sentences like "Do not let unclear scope turn into broad exploratory work by default".

DimensionReasoningScore

Conciseness

The body is disciplined and directive but includes framing Claude largely already knows ("Give the audience enough trustworthy evidence, interpretation, and uncertainty framing...", "Do not let unclear scope turn into broad exploratory work by default") and restates the decision-first point across sections 1, 3, and 5. Mostly efficient, but several sections could be tightened.

3 / 5

Actionability

Directives like "State plainly: the question and decision the analysis should inform" and "Define a framework... the specific data questions that would support or change the recommendation" are directionally concrete, and the lens catalog gives usable definitions. However, there are no worked examples, output templates, or sample question formulations, leaving the guidance at the directive level rather than fully executable.

3 / 5

Workflow Clarity

A clear six-step sequence with explicit checkpoints and stop conditions ("If a required source is unavailable, stop that path", "Validate before concluding", "reconcile them or explain why they differ"). Not a 5 because validation is delegated to other skills ($validate-data) rather than embedded as concrete checkpoints describing what failure looks like and how to recover.

4 / 5

Progressive Disclosure

The single body file is well sectioned, but no bundle files exist, and ~40 lines of inline catalog material (the decision-lens list from "Current scale" through "Coverage", plus the context bullets in section 2) is detail that would fit better in a one-level-deep reference file. Structure is present, but the split is not taken advantage of.

3 / 5

Total

13

/

20

Passed

Description

83%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 strong description that cleanly answers what and when with concrete, natural trigger phrases covering the main decision-support scenarios. The only weaknesses are the single generic action verb and a few missing synonyms that slightly limit specificity and distinctiveness.

DimensionReasoningScore

Specificity

"Analyze product or business data to support a decision or recommendation" names the domain and a concrete purpose, and the enumerated contexts ("segmenting users, sizing tradeoffs") add specificity. It is not a 5 because the core action is a single generic verb rather than multiple distinct concrete actions; it is above 3 because the trigger list is specific and broad.

4 / 5

Completeness

It explicitly answers both what ("Analyze product or business data to support a decision or recommendation") and when ("Use when a decision depends on metric-backed evidence, such as...") with concrete trigger phrases, matching the top anchor. The 4 anchor's 'when could be more explicit' weakness does not apply here.

5 / 5

Trigger Term Quality

"choosing a direction, prioritizing an opportunity, evaluating a change, segmenting users, sizing tradeoffs, or deciding what to do next" are natural phrases users would say when needing this skill. A few common synonyms (e.g. "business case", "go/no-go decision", "KPI comparison") are missing, keeping it below the comprehensive 5 anchor.

4 / 5

Distinctiveness Conflict Risk

The metric-backed decision-support framing carves a clear niche distinct from document or reporting skills. Minor overlap risk remains with generic data-analysis or dashboard skills whose triggers could also mention analyzing business data.

4 / 5

Total

17

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
XiaomiMiMo/MiMo-Code
Reviewed

Table of Contents

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