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volcano-plot-labeler

Analyze data with `volcano-plot-labeler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

39

Quality

49%

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SecuritybySnyk

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tessl review fix ./scientific-skills/Data Analysis/volcano-plot-labeler/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%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 has a solid executable core — working Python and CLI examples, a complete parameter table, and documented input/output formats — but it is wrapped in substantial auto-generated boilerplate (circular cross-references, duplicated commands, generic policy sections) that inflates token cost and fragments the workflow. The references/ bundle is present but never actually linked from the body. Trimming the boilerplate, fixing the malformed audit command, and linking runtime_checklist.md explicitly would move most dimensions up a level.

Suggestions

Delete the boilerplate sections and circular cross-references (Key Features' restatement of the description, "See `## X` above" lines, Output Requirements / Response Template / Output Contract / Validation and Safety Rules) and keep one consolidated workflow; the `python -m py_compile` check needs to appear once, not three times.

Fix the Audit-Ready command to pass real input (e.g., `python scripts/main.py --input data/deseq2_results.csv --top-n 10`); the current narrative-string argument comes from an unrelated template and would fail against the CSV-expecting script.

Link the actual reference file explicitly (e.g., "Pre-execution checklist: see [runtime_checklist.md](references/runtime_checklist.md)") and move the Parameters table and Algorithm details into a reference file to slim SKILL.md to an overview.

Reconcile the Python API examples (`from volcano_plot_labeler import label_volcano_plot`) with the packaged entry point `scripts/main.py`, or document where the importable module lives.

DimensionReasoningScore

Conciseness

The body is heavily padded: broken boilerplate cross-references ("See `## Features` above for related details.", "See `## Workflow` above", "See `## Prerequisites` above"), a Key Features section that restates the frontmatter description verbatim ("Scope-focused workflow aligned to: Analyze data with `volcano-plot-labeler`..."), the same `python -m py_compile scripts/main.py` command repeated in three sections, and generic policy sections (Output Requirements, Response Template, Output Contract, Validation and Safety Rules) that tell Claude things it already knows. This matches 'Noticeably verbose; several unnecessary explanations or padded sections'. Not 1 because the core technical sections (Usage, Parameters, Algorithm, Input Format) are genuinely informative and not concept-explanation filler.

2 / 5

Actionability

Mostly executable: complete Python examples with all keyword arguments ("fig = label_volcano_plot(df, log2fc_col='log2FoldChange', ... top_n=10)"), a concrete CLI invocation with flags ("python scripts/main.py --input data/deseq2_results.csv --top-n 10"), a Parameters table with defaults, and expected input columns. This matches 'Mostly executable guidance; concrete code or commands with minor gaps'. Not 5 because the Audit-Ready command passes a clinical narrative string ("--input 'Audit validation sample with explicit symptoms, history, assessment, and next-step plan.'") to a script that expects a CSV of differential expression results — it would fail — and the `from volcano_plot_labeler import ...` examples are never reconciled with the actual packaging (`scripts/main.py`). Not 3 because the bulk of the guidance is copy-paste ready.

4 / 5

Workflow Clarity

Steps exist and a validation checkpoint is present ("Quick Check ... python -m py_compile scripts/main.py", plus a documented fallback: "If execution fails or inputs are incomplete, switch to the fallback path"), but the actual sequence is fragmented across five overlapping sections (Workflow, Example Usage run plan, Implementation Details, Quick Check, Audit-Ready Commands) joined by circular "See ... above" references, so the effective sequence is implicit and a reader must merge the fragments. This fits 'Steps listed but ... sequence present but checkpoints missing or implicit'. Not 4 because the fragmentation and the broken cross-references leave no single coherent ordered procedure; not 2 because a runnable plan with validation and error recovery does exist.

3 / 5

Progressive Disclosure

A references/ bundle exists (runtime_checklist.md) but the body never names or links it — only generic pointers like "Reference material available in `references/`" and "references/ contains supporting rules, prompts, or checklists", so the reference is present but not clearly signaled. Meanwhile ~320 lines are inlined in SKILL.md, including material (Parameters table, Algorithm details, Risk/Security/Evaluation boilerplate) that could live in separate files. This matches anchor 3, 'references present but not clearly signaled; content that should be separate is inline'. Not 4 because the reference is unnamed and the inline bulk is large; not 2 because the document is well-sectioned rather than an unstructured wall.

3 / 5

Total

12

/

20

Passed

Description

25%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 buries a genuinely specific capability (labeling top significant genes on volcano plots) under generic process language. It answers neither 'what' concretely nor 'when' at all, and contains no natural trigger terms a user would utter. Rewriting it around the actual capability with a "Use when..." clause would fix all four dimensions at once.

Suggestions

State the concrete capabilities in third person: "Label the top 10 most significant genes on volcano plots (color-coded points, leader lines) using a repulsion algorithm that prevents label overlap; supports PNG/PDF/SVG output."

Add an explicit trigger clause: "Use when working with differential expression results (e.g., DESeq2 output CSVs), volcano plots, or when gene labels overlap and become unreadable."

Remove process buzzwords ("reproducible workflow", "explicit validation", "structured outputs", "review-ready interpretation") — they describe how the skill works internally, not what a user gets.

DimensionReasoningScore

Specificity

The only action stated is "Analyze data with `volcano-plot-labeler`", which is generic; "reproducible workflow, explicit validation, and structured outputs for review-ready interpretation" are process buzzwords rather than concrete capabilities (no mention of labeling genes, volcano plots, or overlap prevention). It matches the anchor 'Names the domain but actions are minimal or generic' — the tool name implies the domain, but the described actions are minimal. Not 3 because no 1-2 concrete, distinct actions are actually listed; not 1 because the tool name anchors it to a specific domain.

2 / 5

Completeness

The 'what' is vague ("Analyze data ... using a reproducible workflow") and there is no 'when' / "Use when..." clause at all, matching anchor 2 'Has a vague what and no when'. The missing-use-when guideline caps completeness at 3, and this falls below that cap because the 'what' itself is also vague. Not 3 because anchor 3 requires a clear 'what', which 'Analyze data' is not.

2 / 5

Trigger Term Quality

There are no natural keywords a user would say — "volcano plot", "label genes", "differential expression", "DESeq2" are all absent; the text offers only "Analyze data" plus the backticked tool name. This is 'one or two generic keywords; missing the natural phrases users say'. Not 3 because even the domain-level term (volcano plot) is missing, so keyword coverage is weaker than 'some relevant keywords'.

2 / 5

Distinctiveness Conflict Risk

"Analyze data ... using a reproducible workflow, explicit validation, and structured outputs" would match virtually any data-analysis or analytics skill; the only distinguishing element is the backticked tool name. This fits 'Very broad; high overlap risk with many similar skills'. Not 3 because the phrasing (not the tool name) provides no niche trigger; not 1 because the named tool keeps it from conflicting with non-data skills.

2 / 5

Total

8

/

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
Reviewed

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