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gene-protein-expression-matrix-normalization

Use when normalizing bulk gene or protein expression matrices with log2 transform, z-score standardization, or min-max scaling before downstream visualization or exploratory analysis. NOT for count-model normalization such as TPM/DESeq2 size factors, batch correction, or single-cell preprocessing.

Quality

87%

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SKILL.md
Quality
Evals
Security

Quality

Content

82%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.

A well-structured, highly actionable CLI skill reference: executable commands, complete argument and output tables, error feedback loops, and excellent situation-based navigation to real bundle files. Weaknesses are minor: a redundant exclusion sentence, no explicit in-flow output verification, and dangling references to DESCRIPTION and tests/ that do not exist in the bundle.

Suggestions

Remove the duplicated count-model exclusion sentence in 'Input Format' (CPM/TPM/TMM/DESeq2 already listed under 'When Not to Use') to tighten conciseness.

Ship or remove the referenced 'DESCRIPTION' and 'tests/' paths — the body and cli-guide.md point to them but neither exists in the bundle, breaking navigation.

Add an explicit post-run verification step (e.g., check output_manifest.txt or run the audit test) to the main workflow, and make the overwrite warning independent of --verbose.

DimensionReasoningScore

Conciseness

The body is table-driven and operational with no concept explanations, but the count-model exclusions (CPM/TPM/TMM/DESeq2) appear twice — once under 'When Not to Use' and again in 'Input Format' ("It does not infer count-model normalization such as CPM, TPM, TMM, or DESeq2 size factors") — a minor instance of trimming-worthy redundancy that keeps it below the 5 anchor's 'every token earns its place'.

4 / 5

Actionability

Fully executable, copy-paste-ready commands ("Rscript scripts/main.R --input_file tests/data/expression_matrix.csv --output_dir ./output --method log2 --pseudo_count 1 --seed 42"), a complete argument table with defaults, a concrete CSV input example, an output-file manifest, error codes with causes and fixes, and runnable test commands — matching the 5 anchor's 'copy-paste ready code or commands' covering common cases.

5 / 5

Workflow Clarity

The single-action workflow is unambiguous (validate input format, run the CLI, consume the documented outputs), with an error-code-to-cause-to-solution table as a feedback loop and a Testing section for verification. Below 5 because the main flow has no explicit post-run output-validation step (verification is deferred to separate test scripts) and the overwrite warning fires only when --verbose=true; above 3 because checkpoints are present via the error table and bundled tests.

4 / 5

Progressive Disclosure

The 'When to Read External Files' situation table is exemplary one-level-deep navigation, and scripts/main.R, references/algorithm.md, references/troubleshooting.md, and references/cli-guide.md all exist in the bundle. Below 5 because the body (and cli-guide.md) reference 'DESCRIPTION' and 'tests/' files that are not present in the bundle, leaving dangling references; above 3 because structure and signaling are otherwise clear and appropriately split.

4 / 5

Total

17

/

20

Passed

Description

87%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.

A strong description: it states what the skill does, when to use it, and when not to use it with concrete methods and explicit exclusions. Keyword coverage is good but missing a few natural synonyms and file-format terms users might mention.

DimensionReasoningScore

Specificity

Names the domain and three concrete methods ("log2 transform, z-score standardization, or min-max scaling") plus a purpose ("before downstream visualization or exploratory analysis"), matching the 'several specific actions; minor gaps in coverage' anchor. It falls short of 5 because capabilities like margin selection, pseudo-count handling, and output artifacts are unmentioned, and sits above 3 because it lists multiple concrete actions, not just one or two.

4 / 5

Completeness

Explicitly answers both what ("normalizing bulk gene or protein expression matrices with log2 transform, z-score standardization, or min-max scaling") and when ("Use when normalizing ... before downstream visualization or exploratory analysis") with concrete trigger phrasing, exactly matching the 5 anchor. Not 4: the 'when' clause is fully explicit rather than merely present-but-imprecise.

5 / 5

Trigger Term Quality

Natural terms users would say are present: "normalizing", "gene expression", "protein expression", "log2", "z-score", "min-max". It falls short of the 5 anchor's comprehensive synonym and file-extension coverage (e.g., no CSV/matrix file formats, no 'transform/scaling' variants), and is clearly above 3 because keyword coverage is good rather than partial.

4 / 5

Distinctiveness Conflict Risk

Clear niche with an explicit exclusion list ("NOT for count-model normalization such as TPM/DESeq2 size factors, batch correction, or single-cell preprocessing") that actively disambiguates from adjacent bioinformatics skills, matching the 5 anchor's 'clear niche with distinct triggers; minimal conflict risk'.

5 / 5

Total

18

/

20

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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

aipoch/medical-research-skills

Path

awesome-med-research-skills/Data Analysis/gene-protein-expression-matrix-normalization/SKILL.md

Reviewed

Table of Contents

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