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exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain formats are reference-only and unknown formats fail closed.

74

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

The body is highly actionable with a well-sequenced, validated workflow and a clean one-level-deep reference structure. The only weakness is an inline dated dependency snapshot that competes with the token budget rather than living in a versioning/old-patterns section.

Suggestions

Move the dated package-version table and 'verified 2026-07-23' snapshot into a separate references/version_snapshot.md (or a clearly marked versioning section) so SKILL.md stays evergreen and lighter.

Consider linking the version pins from the 'Install only capabilities needed' block rather than reproducing the full dated table inline in SKILL.md.

If the version baseline must stay inline, isolate it under a labeled 'Dependency snapshot (verify before use)' heading so it reads as a deliberately time-sensitive block rather than core guidance.

DimensionReasoningScore

Conciseness

The body is dense and avoids padded concept explanations, but it carries an inline dated version pin table ('verified 2026-07-23', specific package versions) that the rubric penalizes when not placed in a deprecated/old-patterns section.

2 / 3

Actionability

Commands are fully executable with concrete flags (e.g. 'python scripts/eda_analyzer.py data.csv --root /approved/project --max-rows 100000 --output data.eda.json') and explicitly note placeholder identifiers.

3 / 3

Workflow Clarity

A five-step workflow is clearly sequenced with explicit validation checkpoints (manifest before analysis; 'If status is reference_only, do not run'; 'If unknown, stop') and an authorization gate.

3 / 3

Progressive Disclosure

SKILL.md is an overview pointing to six one-level-deep references in a clear table ('Read the one relevant format reference. Do not load every reference'); all referenced scripts, assets, and references were verified to exist.

3 / 3

Total

11

/

12

Passed

Description

100%

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 concise, third-person description that pairs concrete capabilities with an explicit 'Use for' trigger clause and a clear scope boundary. It answers both what and when while staying distinct from general data skills.

DimensionReasoningScore

Specificity

Lists multiple concrete actions such as 'missingness/leakage audits', 'outlier and transformation sensitivity', and 'EDA report scaffolds' rather than vague language.

3 / 3

Completeness

Explicit 'Use for ...' clause answers when, and the opening sentence plus the enumerated capabilities answer what, both clearly.

3 / 3

Trigger Term Quality

Names natural file-type terms users would say (CSV/TSV/JSON, NumPy, HDF5, FASTA/FASTQ) alongside task keywords like 'profiles' and 'metadata inspection'.

3 / 3

Distinctiveness Conflict Risk

The bounded-scientific-file niche and 'unknown formats fail closed' boundary make it unlikely to trigger for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

15

/

16

Passed

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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