data-analytics

Use this skill for quantitative product or business analysis: data quality checks, metric diagnostics, KPI design and reporting, dashboards, analytical reports, charts, notebooks, market sizing, semantic layers, and evidence-backed recommendations. Also use it whenever Data Analytics is explicitly invoked.

Quality

86%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

Quality

Content

80%

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

A concise, well-structured routing overlay with concrete delivery and safety overrides. Its weaker points are the absence of explicit error-recovery checkpoints in the routing flow and inability to verify the referenced workflow bundle files.

Suggestions

Add an explicit validate→fix→retry step to the routing sequence (e.g., 'after producing a durable artifact, validate it against the workflow's QA checks; if it fails, fix and re-validate before declaring completion').

Reference the bundled sample asset (e.g., 'for market-sizing or funnel demos, prefer assets/demo-product-growth.csv') so progressive disclosure points to real, present files.

Confirm the `workflows/` directory and `workflows/index/SKILL.md` exist in the bundle, or note their absence so navigation claims are verifiable.

Dimension	Reasoning	Score
Conciseness	Lean, non-padded body that assumes Claude's competence — it jumps straight to routing and overrides with no concept explanations, matching the 'lean and efficient; every token earns its place' anchor.	3 / 3
Actionability	Concrete, specific directives ('Read `workflows/index/SKILL.md` completely', 'Resolve relative links from the file that contains the link', an explicit tool-deny list) give actionable guidance; absence of code is appropriate for this instruction-only meta-skill.	3 / 3
Workflow Clarity	A clear numbered routing sequence is present, but explicit validate→fix→retry checkpoints are thin (completion is enforced via the 'portable file exists or blocker reported' rule rather than an explicit feedback loop), matching the 'sequence present but checkpoints implicit' anchor.	2 / 3
Progressive Disclosure	Content is well-structured and signals one-level-deep loading via `workflows/`, but the referenced `workflows/` bundle is not present in this bundle to verify, and the only bundled asset (demo-product-growth.csv) is unreferenced by the body, matching the 'structure present but navigation not fully confirmable' anchor.	2 / 3
	Total	10 / 12 Passed

Description

92%

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, third-person description with explicit trigger guidance and a comprehensive list of concrete capabilities. Its main weakness is a broad domain that may overlap with adjacent analytical skills.

Suggestions

Tighten the domain boundary (e.g., 'product/business analytics with structured event or funnel data') to reduce overlap with general data-science or spreadsheet skills.

Add one or two negative-scope cues (e.g., 'Not for pure ML modeling or data engineering pipelines') to sharpen distinctiveness.

Dimension	Reasoning	Score
Specificity	Lists many concrete actions — 'data quality checks, metric diagnostics, KPI design and reporting, dashboards, analytical reports, charts, notebooks, market sizing, semantic layers, and evidence-backed recommendations' — matching the 'lists multiple specific concrete actions' anchor rather than the partial-coverage anchor at 2.	3 / 3
Completeness	Explicitly answers both 'what' (the enumerated analyses) and 'when' ('Use this skill for...' plus 'Also use it whenever Data Analytics is explicitly invoked'), satisfying the explicit-trigger anchor at 3 rather than capping at 2.	3 / 3
Trigger Term Quality	Includes natural user-facing terms (KPI, dashboards, charts, notebooks, market sizing, data quality) plus an explicit 'whenever Data Analytics is explicitly invoked' trigger, giving broad natural coverage rather than missing common variations.	3 / 3
Distinctiveness Conflict Risk	The 'quantitative product or business analysis' niche is broad and could overlap with general data skills, though the explicit 'Data Analytics invoked' trigger adds some distinctiveness; closer to the 'somewhat specific but could still overlap' anchor than the clear-niche anchor.	2 / 3
	Total	11 / 12 Passed

Validation

100%

Warnings & errors only

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
Path: packages/opencode/src/skill/builtin/.bundle/data-analytics/SKILL.md
Commit: ff87d47

Reviewed: about 10 hours ago

Table of Contents

Discovery Implementation Validation

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