data-analytics-reporter

Data analytics and reporting specialist that reads raw data files (CSV, Excel, SQL query results, JSON), calculates summary statistics, identifies trends, builds charts and dashboards, tracks KPIs, and produces structured reports. Use when the user asks to analyze a dataset, create a dashboard, compute metrics or KPIs, generate bar/line/pie charts, run statistical summaries, perform A/B test analysis, segment customers, measure campaign performance, or produce a data-driven business report from spreadsheet or database data.

Quality

84%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

77%

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

This is a solid, actionable skill with executable code patterns covering the key analytics tasks (cleaning, KPIs, A/B tests, visualization) and useful output templates. Its main weakness is moderate verbosity — some content (the generic workflow steps, chart type guide) covers knowledge Claude already has, consuming tokens without adding unique value. The validation checklist is a strong inclusion that ensures quality outputs.

Suggestions

Remove or significantly trim the chart type selection guide and the generic workflow steps (1-3 especially), as these are common knowledge for Claude — focus tokens on the non-obvious patterns and project-specific conventions.

Consider splitting output templates (Executive Summary, Dashboard Spec) into a separate TEMPLATES.md file referenced from the main skill to improve progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The content is mostly efficient with useful code patterns and templates, but includes some unnecessary verbosity — e.g., the 7-step workflow is somewhat generic knowledge Claude already possesses, and comments like 'silently converts unparseable values to NaT' explain standard pandas behavior. The chart type selection guide is also largely common knowledge.	2 / 3
Actionability	The skill provides fully executable Python and SQL code snippets for IQR outlier removal, KPI calculations, A/B testing, and visualization. The output templates are copy-paste ready with clear placeholder conventions. Every major task has concrete, runnable code.	3 / 3
Workflow Clarity	The 7-step workflow is clearly sequenced with an explicit validation step at the end. The validation checklist provides concrete verification criteria (row counts, date ranges, totals reconciliation, percentage sums). The feedback loop is implicit but the checklist compensates well for data analytics work.	3 / 3
Progressive Disclosure	The content is well-organized with clear section headers and logical grouping, but it's a fairly long monolithic file (~150 lines of substantive content). The output templates and chart selection guide could be split into referenced files. No external references are provided for deeper topics like advanced statistical methods or complex dashboard patterns.	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.

This is a strong, well-crafted description that excels in specificity, trigger term coverage, and completeness with a clear 'Use when...' clause. Its main weakness is the very broad scope, which could create overlap with more specialized data or visualization skills in a large skill library. The third-person voice is used correctly throughout.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: reads raw data files (with formats), calculates summary statistics, identifies trends, builds charts and dashboards, tracks KPIs, and produces structured reports. Very comprehensive enumeration of capabilities.	3 / 3
Completeness	Clearly answers both 'what' (reads data files, calculates statistics, builds charts, tracks KPIs, produces reports) and 'when' with an explicit 'Use when...' clause listing numerous specific trigger scenarios.	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users would say: 'analyze a dataset', 'create a dashboard', 'compute metrics or KPIs', 'bar/line/pie charts', 'statistical summaries', 'A/B test analysis', 'segment customers', 'campaign performance', 'spreadsheet or database data', plus file formats like CSV, Excel, JSON.	3 / 3
Distinctiveness Conflict Risk	While the description is detailed, the broad scope covering CSV, Excel, JSON, charts, dashboards, statistical analysis, and reporting could overlap with more specialized skills like an Excel analysis skill, a charting/visualization skill, or a general statistics skill. The breadth increases conflict risk with narrower tools.	2 / 3
	Total	11 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: OpenRoster-ai/awesome-agents
Commit: 010799b

Reviewed: 3 months ago

Table of Contents

Discovery Implementation Validation

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