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graph-interpretation

Use when interpreting scientific graphs and charts, explaining data visualizations for research presentations, writing figure captions for publications, or analyzing trends in clinical research data. Converts complex visual data into clear, accurate explanations for academic papers, clinical reports, and public presentations.

66

Quality

81%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

62%

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

The body is richly structured with a clear workflow and validation, but it is verbose with redundant description repetition, and its code examples are misleading — they reference a module/class/script that do not match the actual bundled `main.py`.

Suggestions

Reconcile code examples with the real bundle: either implement `GraphInterpreter`/`graph_interpreter.py` with the shown methods, or rewrite examples to use the actual `GraphInterpretation` class and `describe()` method in `scripts/main.py`.

Remove the verbatim repetition of the description in 'When to Use' and 'Key Features'; reference it once or summarize.

Make `references/` navigation explicit by linking `guidelines.md` by name, and either populate it with the real statistical-reporting/figure-caption guidance or drop the reference.

DimensionReasoningScore

Conciseness

Mostly actionable but padded: the frontmatter description is repeated verbatim in both 'When to Use' and 'Key Features', and Quick Start / Core Capabilities / Common Patterns overlap with similar interpreter code; could be tightened considerably.

2 / 3

Actionability

Code looks concrete but is not executable against the actual bundle: examples import `from scripts.graph_interpreter import GraphInterpreter` and call `python scripts/graph_interpreter.py`, yet the only script is `scripts/main.py` defining class `GraphInterpretation` with a single `describe` method — the referenced module, class, and methods do not exist.

2 / 3

Workflow Clarity

A 5-step Workflow is sequenced with explicit validation ('stop early if the task would require unsupported assumptions'), a Quick Check (`python -m py_compile`), a fallback path on failure, and a Quality Checklist — matching the anchor for clear sequence with checkpoints and feedback loops.

3 / 3

Progressive Disclosure

Structure exists but references are poorly signaled: the body points generically to `references/` (never naming `guidelines.md`, which itself is near-empty) and references a non-existent `scripts/graph_interpreter.py`, while ~400 lines of API-style material sit inline that could live in separate files.

2 / 3

Total

9

/

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 strong, specific description with explicit trigger guidance and concrete actions targeting a well-defined niche. No first/second-person voice issues; nothing vague or over-claimed.

DimensionReasoningScore

Specificity

Names multiple concrete actions — 'interpreting scientific graphs and charts', 'explaining data visualizations', 'writing figure captions', 'analyzing trends in clinical research data' — matching the anchor for listing several specific actions.

3 / 3

Completeness

Explicitly answers both what ('Converts complex visual data into clear, accurate explanations...') and when ('Use when interpreting...'), with an explicit trigger clause.

3 / 3

Trigger Term Quality

Covers natural user terms well: 'scientific graphs and charts', 'data visualizations', 'figure captions', 'publications', 'clinical research data', 'academic papers' — terms a user would plausibly say.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (scientific/clinical graph interpretation) with distinct triggers unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 3 missing

Warning

Total

14

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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