exploratory-data-analysis

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

Quality

62%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./backend/cli/skills/coding/exploratory-data-analysis/SKILL.md

The canonical home for this skill is exploratory-data-analysis in K-Dense-AI/scientific-agent-skills

Quality

Content

53%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 skill body is well-structured with real bundle files and actionable code, but it is notably verbose with redundant workflow restatements and inlined reference content, and lacks explicit validation checkpoints in its workflow. Trimming repetition and adding verification steps would meaningfully improve it.

Suggestions

Remove the duplicate workflow restatements in 'Detailed Format References' and 'Examples' (keep one canonical 5-step workflow) and drop the inlined extension lists since they already live in the reference files, to improve conciseness.

Add explicit validation checkpoints to the workflow (e.g., verify the file is readable and confirm format detection before analysis; sanity-check report output exists after save) to raise workflow_clarity.

Convert placeholder comment blocks in the Examples into complete, copy-paste-runnable code, or explicitly justify the pseudocode style, to strengthen actionability.

Dimension	Reasoning	Score
Conciseness	The ~440-line body restates the 5-step workflow three times (Workflow, Detailed Format References, Examples), inlines extension lists already present in the reference files, and includes generic best-practice prose ('Be comprehensive', 'Be actionable') that pads without adding Claude-unknown knowledge.	2 / 5
Actionability	Provides mostly executable guidance — real commands ('python scripts/eda_analyzer.py data.csv output_report.md'), working library calls (SeqIO.parse, pandas.read_csv, ND2Reader), and a grep pattern — though several examples use placeholder comments ('# Calculate: read count...') instead of complete runnable code.	4 / 5
Workflow Clarity	A clear 5-step sequence (detect, load reference, analyze, report, save) is present, but there are no explicit validation checkpoints (e.g., confirm file is readable, verify format detection succeeded), leaving checkpoints implicit.	3 / 5
Progressive Disclosure	Good structure with well-signaled, one-level-deep references to six verified reference files, a script, and a template; however the body re-inlines extension lists and lookup guidance that the references already cover, a minor organization gap.	4 / 5
	Total	13 / 20 Passed

Description

71%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 clearly communicates the skill's purpose and trigger context with concrete capabilities and a distinct scientific-data niche. It could be strengthened by adding natural user verbs and file extensions to the trigger phrasing.

Suggestions

Add natural trigger verbs users actually say (e.g., 'explore', 'summarize', 'profile') and a few representative file extensions to improve trigger_term_quality.

Make the 'when' clause more concrete by listing explicit trigger phrases (e.g., 'Use when the user provides a scientific data file path or asks to explore/summarize a dataset').

Dimension	Reasoning	Score
Specificity	Lists several concrete actions ('Automatically detects file type', 'generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations') across multiple domains, though 'understand its structure, content, quality' is slightly more abstract than a fully concrete verb list.	4 / 5
Completeness	Explicitly states both what (perform EDA, detect type, generate reports) and when ('This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics'), but the 'when' triggers could be more concrete and specific.	4 / 5
Trigger Term Quality	Includes relevant keywords ('analyzing any scientific data file', domain names) but misses natural user verbs like 'explore' or 'summarize' and omits file extensions, leaving common variations uncovered.	3 / 5
Distinctiveness Conflict Risk	Carves a clear niche (EDA on scientific data across named domains: chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics) with distinct triggers and minimal conflict risk against generic skills.	5 / 5
	Total	16 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/coding/exploratory-data-analysis/SKILL.md
Commit: fcd2e11

Reviewed: 1 day ago

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