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.
69
62%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/Academic Writing/graph-interpretation/SKILL.mdQuality
Discovery
89%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a well-structured description with strong completeness and distinctiveness. It clearly identifies when to use the skill and targets a specific scientific/academic niche. The main weakness is that the specific capabilities could be more concrete - listing specific actions like 'identify trends', 'extract numerical values', or 'describe statistical relationships' would strengthen specificity.
Suggestions
Add more concrete specific actions such as 'extract data points from graphs', 'identify statistical trends', 'describe axis relationships', or 'summarize key findings from visualizations' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (scientific graphs, data visualizations) and some actions (interpreting, explaining, writing captions, analyzing trends), but the actions are somewhat general rather than listing multiple concrete specific operations like 'extract data points', 'identify statistical significance', or 'compare datasets'. | 2 / 3 |
Completeness | Explicitly answers both what ('Converts complex visual data into clear, accurate explanations') and when ('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'). Has clear 'Use when...' clause with explicit triggers. | 3 / 3 |
Trigger Term Quality | Good coverage of natural terms users would say: 'scientific graphs', 'charts', 'data visualizations', 'research presentations', 'figure captions', 'publications', 'clinical research data', 'academic papers', 'clinical reports'. These are terms researchers would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused on scientific/clinical research context with distinct triggers like 'figure captions', 'publications', 'clinical research data', 'academic papers'. Unlikely to conflict with general data visualization or generic document skills due to the specific scientific/academic focus. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill suffers from significant verbosity and redundancy, with the same concepts repeated across multiple sections (workflow, implementation details, key features). While it provides useful domain-specific content like graph type tables and audience-specific explanation templates, the core execution path is unclear—it references scripts that may not exist or work as shown. The skill would benefit from aggressive trimming and consolidation.
Suggestions
Remove redundant sections: consolidate 'Workflow', 'Implementation Details', 'Key Features', and 'Example Usage' into a single clear workflow with validation steps
Move the large reference tables (Supported Graph Types, Common Pitfalls) to separate files in references/ and link to them
Clarify whether scripts/main.py and scripts/graph_interpreter.py actually exist and provide their real interfaces, or remove references to non-existent code
Add specific validation checkpoints for graph interpretation: e.g., 'Verify image is readable', 'Confirm graph type detection', 'Validate extracted statistics against visible data'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with significant redundancy. The 'When to Use' section repeats the description verbatim. Multiple sections explain the same concepts (workflow appears 3+ times). Contains unnecessary boilerplate like 'See ## Workflow above for related details' and extensive template structures that pad the content without adding value. | 1 / 3 |
Actionability | Provides some concrete code examples (GraphInterpreter class usage, CLI commands) but the core script 'scripts/main.py' is referenced without showing its actual implementation. The Quick Start shows a plausible API but it's unclear if this is real executable code or aspirational pseudocode. Many examples show data structures but lack complete working implementations. | 2 / 3 |
Workflow Clarity | Multiple workflow sections exist but they're generic and lack validation checkpoints specific to graph interpretation. The numbered workflow steps (1-5) are abstract process descriptions rather than concrete validation steps. No explicit error recovery for common graph interpretation failures like unreadable images or ambiguous data. | 2 / 3 |
Progressive Disclosure | References 'references/' directory and 'scripts/main.py' but doesn't clearly signal what's in those files. The document is monolithic with extensive inline content (supported graph types table, statistical reporting, audience templates) that could be split into reference files. Structure exists but content that should be separate is inline. | 2 / 3 |
Total | 7 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- aipoch/medical-research-skills
- Commit
4a48721
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.