scientist-low
Basic data analysis - fast exploratory analysis (Haiku-tier)
Install with Tessl CLI
npx tessl i github:TurnaboutHero/oh-my-antigravity --skill scientist-lowOverall
score
61%
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
22%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 description is too vague and lacks the specificity needed for effective skill selection. It fails to list concrete actions, omits explicit trigger guidance, and relies on internal terminology ('Haiku-tier') that users wouldn't naturally use. The description would benefit significantly from concrete capability examples and a clear 'Use when...' clause.
Suggestions
Add specific concrete actions like 'Summarize datasets, compute basic statistics, create quick visualizations, identify patterns in tabular data'
Include a 'Use when...' clause with natural trigger terms: 'Use when the user needs quick data summaries, basic CSV analysis, simple statistics, or initial data exploration'
Replace 'Haiku-tier' jargon with user-facing language like 'lightweight' or 'quick' and add file type triggers like 'CSV', 'spreadsheet', '.xlsx'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description uses vague language like 'basic data analysis' and 'fast exploratory analysis' without listing any concrete actions. No specific capabilities like 'create charts', 'compute statistics', or 'clean data' are mentioned. | 1 / 3 |
Completeness | The description weakly addresses 'what' (basic data analysis) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. | 1 / 3 |
Trigger Term Quality | Contains some relevant keywords ('data analysis', 'exploratory analysis') that users might say, but missing common variations like 'CSV', 'spreadsheet', 'statistics', 'visualize', or 'summarize data'. The term 'Haiku-tier' is internal jargon users wouldn't use. | 2 / 3 |
Distinctiveness Conflict Risk | The 'Haiku-tier' qualifier and 'fast exploratory' framing provide some distinction from potentially more advanced analysis skills, but 'data analysis' is still broad enough to overlap with many other analytical skills. | 2 / 3 |
Total | 6 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides good actionable code examples for basic data analysis tasks with clear structure. However, it includes some unnecessary framing content and lacks validation/error handling guidance that would help Claude handle common data loading failures gracefully.
Suggestions
Remove the 'Use Cases' section and closing tagline - they add no actionable value and waste tokens
Add basic error handling patterns for common failures (file not found, encoding issues, malformed data)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient but includes some unnecessary framing ('You are Scientist-Low', the tagline at the end) and the 'Use Cases' section lists things Claude already knows how to do without adding actionable guidance. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready Python code examples for data loading, statistics, and visualization with specific library usage and file paths. | 3 / 3 |
Workflow Clarity | Shows a logical sequence (load → inspect → analyze → visualize) but lacks validation checkpoints or error handling guidance for data operations that could fail (e.g., file not found, malformed CSV). | 2 / 3 |
Progressive Disclosure | For a simple, single-purpose skill under 50 lines, the content is well-organized with clear sections (Use Cases, REPL, Output Format, Visualization) and doesn't require external references. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
91%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 | |
- Reviewed
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