data-analysis

Analyze datasets to extract insights, identify patterns, and generate reports. Use when exploring data, creating visualizations, or performing statistical analysis. Handles CSV, JSON, SQL queries, and Python pandas operations.

3.36x

Quality

78%

Does it follow best practices?

Impact

64%

3.36x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agent-skills/data-analysis/SKILL.md

Quality

Discovery

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 skill description that clearly articulates capabilities and usage triggers. It uses third person voice correctly, lists concrete actions, and includes natural trigger terms. The main weakness is potential overlap with other data-related skills due to the broad nature of 'data analysis'.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'extract insights', 'identify patterns', 'generate reports', 'creating visualizations', 'performing statistical analysis', and specifies formats (CSV, JSON, SQL, pandas).	3 / 3
Completeness	Clearly answers both what ('Analyze datasets to extract insights, identify patterns, and generate reports') AND when ('Use when exploring data, creating visualizations, or performing statistical analysis') with explicit trigger guidance.	3 / 3
Trigger Term Quality	Includes natural keywords users would say: 'datasets', 'data', 'visualizations', 'statistical analysis', 'CSV', 'JSON', 'SQL queries', 'pandas'. Good coverage of common data analysis terms.	3 / 3
Distinctiveness Conflict Risk	While it specifies data analysis, terms like 'datasets', 'CSV', 'JSON' could overlap with general file processing skills. The pandas/SQL specificity helps but 'data analysis' is a broad domain that could conflict with more specialized analytics skills.	2 / 3
	Total	11 / 12 Passed

Implementation

64%

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 data analysis skill with comprehensive executable code examples covering the full analysis workflow. However, it could be more concise by removing generic best practices Claude already knows, and would benefit from explicit validation checkpoints between data transformation steps. The placeholder example sections reduce its completeness.

Suggestions

Add explicit validation checkpoints after data cleaning steps (e.g., 'Verify: print(f"Rows before: {original_count}, after: {len(df)}, removed: {original_count - len(df)}")')

Remove or significantly trim the 'Best practices' section as these are generic principles Claude already understands

Complete the placeholder example sections with actual input/output demonstrations

Consider moving the detailed visualization code to a separate VISUALIZATION.md file and keeping only one representative example inline

Dimension	Reasoning	Score
Conciseness	The skill is reasonably efficient but includes some unnecessary content like the 'When to use this skill' section that Claude can infer, and the 'Best practices' section contains generic advice Claude already knows (e.g., 'understand the data first', 'use visualization').	2 / 3
Actionability	Provides fully executable, copy-paste ready code examples for Python/Pandas, SQL, and visualization. Code snippets are complete and specific with concrete operations like loading data, cleaning, statistical analysis, and generating charts.	3 / 3
Workflow Clarity	Steps are clearly numbered and sequenced (1-5), but lacks explicit validation checkpoints. For data analysis involving cleaning and transformations, there should be verification steps (e.g., 'verify row counts after cleaning', 'validate no data loss') between operations.	2 / 3
Progressive Disclosure	Content is reasonably structured with clear sections, but the skill is somewhat monolithic with all code inline. The Examples section has placeholder content ('Add example content here') which is incomplete. External references are provided but could better signal when to use them.	2 / 3
	Total	9 / 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
metadata_version	'metadata.version' is missing	Warning

	Total	10 / 11 Passed

Repository: supercent-io/skills-template
Commit: c033769

Reviewed: 15 days ago

Table of Contents

Discovery Implementation Validation

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