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data-scientist

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.

48

Quality

53%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./skills/data-scientist/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

31%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 body is an exhaustive capability catalogue rather than actionable guidance: it lists techniques Claude already knows, includes no executable code or concrete commands, and inlines everything instead of progressively disclosing detail via reference files. Workflows have a sequence but lack concrete validation checkpoints.

Suggestions

Replace the long capability/Knowledge Base enumerations with a few executable code templates (e.g., a pandas EDA snippet, an sklearn train/evaluate block, a statsmodels A/B-test snippet) covering the common cases.

Move the per-domain technique lists into reference files (e.g., references/statistical-methods.md, references/ml-methods.md) and have SKILL.md point to them one level deep.

Add concrete validation checkpoints to the Response Approach (e.g., specific checks for data leakage, train/test split integrity, and residual diagnostics) with explicit fix-and-retry feedback loops.

DimensionReasoningScore

Conciseness

The body is noticeably verbose, spending ~170 lines enumerating techniques and tools Claude already knows (ARIMA, Prophet, XGBoost, scikit-learn, SHAP, LIME, etc.) plus a padded 'Knowledge Base' section restating fundamentals, matching anchor 2's 'several unnecessary explanations or padded sections' rather than the mostly-efficient anchor 3.

2 / 5

Actionability

There is no executable code, no commands, and no copy-paste examples; the 'Instructions' are abstract ('Apply relevant best practices and validate outcomes') and the body describes capabilities rather than instructing, fitting anchor 2 ('minimal concrete guidance; high-level hints but missing the specific steps to execute') rather than anchor 1 only because a numbered 'Response Approach' process outline exists.

2 / 5

Workflow Clarity

The 8-step 'Response Approach' gives a clear sequence but validation checkpoints are vague ('Validate results rigorously through statistical testing') with no concrete commands or error-recovery feedback loops, matching anchor 3 ('steps listed but validation gaps; checkpoints missing or implicit').

3 / 5

Progressive Disclosure

No bundle files exist and the entire capability catalogue is inlined as a monolithic wall of text in SKILL.md with only section headers; content that clearly belongs in separate reference files (long per-domain technique lists) is not split out, matching anchor 2 ('minimal structure; content that clearly belongs in separate files is inlined').

2 / 5

Total

9

/

20

Passed

Description

75%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 clearly states both capabilities and proactive-use triggers with natural keywords, scoring well on completeness and trigger quality. Its main weakness is breadth: it overlaps with several adjacent analytics/ML skills and the action verbs are generic.

DimensionReasoningScore

Specificity

Names the domain ('advanced analytics, machine learning, statistical modeling') and a couple actions ('Handles complex data analysis, predictive modeling, and business intelligence') but the verbs are generic ('Handles') and coverage is not comprehensive, matching anchor 3 rather than 4 which requires several specific concrete actions.

3 / 5

Completeness

Explicitly answers both what ('Expert data scientist... Handles complex data analysis, predictive modeling, and business intelligence') and when ('Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights') with concrete trigger phrases, matching anchor 5.

5 / 5

Trigger Term Quality

Provides good natural-keyword coverage ('data analysis tasks, ML modeling, statistical analysis, data-driven insights') that users would commonly say, though it misses several natural synonyms like 'forecasting', 'A/B test', or 'build a model', fitting anchor 4 rather than the fully comprehensive anchor 5.

4 / 5

Distinctiveness Conflict Risk

'Data scientist for advanced analytics, ML, and statistical modeling' is somewhat specific to the data-science niche but overlaps heavily with adjacent skills (ML engineer, statistician, data analyst, business analyst, NLP), matching the 'somewhat specific but could still overlap' anchor 3 rather than the clearly-niche anchor 5.

3 / 5

Total

15

/

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
rmyndharis/antigravity-skills
Reviewed

Table of Contents

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