CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

data-scientist

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

Quality

48%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./cli-tool/components/skills/ai-research/data-scientist/SKILL.md

The canonical home for this skill is data-scientist in rmyndharis/antigravity-skills

SKILL.md
Quality
Evals
Security

Quality

Content

27%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.

This is a persona/catalog skill whose body mostly enumerates tools, algorithms, and domain applications Claude already knows, adding ~170 lines of near-zero instructional value while providing no executable guidance, code, or templates. The one genuine workflow (Response Approach) is a sensible but abstract sequence with no validation checkpoints, and the capability catalog should either be drastically cut or moved to reference files. The skill would be far stronger as a lean playbook of concrete method-selection heuristics, code/task templates, and validation criteria.

Suggestions

Cut the Capabilities, Behavioral Traits, and Knowledge Base sections to a handful of lines each (or delete them) — Claude already knows what random forests, SHAP, and Airflow are, so the ~140-line catalog is pure token cost.

Replace the abstract Response Approach with an actionable workflow: concrete method-selection guidance, validation checkpoints (e.g. 'report model performance with cross-validated metrics and a baseline comparison before recommending deployment'), and error-recovery steps.

Add executable artifacts — a starter analysis notebook skeleton, code templates for common tasks (train/evaluate a model, run a significance test), and worked examples tied to the listed Example Interactions — or move domain-specific playbooks (marketing/financial/operations analytics) into reference files.

DimensionReasoningScore

Conciseness

Roughly 170 of ~190 lines are bullet enumerations of tools and methods Claude already knows ('Supervised learning: linear/logistic regression, decision trees, random forests, XGBoost, LightGBM', 'Python ecosystem: pandas, NumPy, scikit-learn'), which teach nothing new — this matches the 'Severely verbose; heavily padded' anchor. It is not 2 because the padding is not confined to a few sections; it is the bulk of the document, with only 'Response Approach' and 'Example Interactions' carrying instruction value.

1 / 5

Actionability

The guidance is almost entirely descriptive: the Instructions section says 'Apply relevant best practices and validate outcomes' and the Response Approach offers abstract steps ('Apply appropriate methods based on data characteristics') with no code, commands, templates, or specific procedures. It sits above 1 because the Response Approach sequence and the eight example interactions give minimal concrete orientation, but below 3 because nothing is executable or specific enough to act on directly.

2 / 5

Workflow Clarity

The 'Response Approach' section lists an 8-step numbered sequence (understand context, explore data, apply methods, validate, communicate, consider constraints, plan implementation, document) matching the 'Steps listed but validation gaps' anchor — the sequence is coherent but checkpoints are only implicit ('Validate results rigorously' names no actual validation test or criterion). It is not 2 because the steps are not poorly defined or full of gaps, and not 4 because no concrete checkpoint or error-recovery guidance exists.

3 / 5

Progressive Disclosure

The body has consistent, clearly labeled section headers (Capabilities, Behavioral Traits, Knowledge Base, Response Approach, Example Interactions) and no nested or buried references since no bundle files exist at all. However, the ~140-line capability catalog is exactly the kind of bulk content that belongs in separate reference files, matching 'Some structure but could be better organized; content that should be separate is inline'. It is not 4 because none of this material is split out, and not 2 because the structure that is present is clean and easy to navigate.

3 / 5

Total

9

/

20

Passed

Description

45%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 identifies the domain but reads as a persona summary rather than a trigger-optimized skill description. Its actions are generic buzz-adjacent phrases, and the mandatory 'when to use' guidance is completely missing, which both caps completeness and weakens trigger quality. Adding an explicit 'Use when...' clause with concrete natural-language triggers and one or two concrete deliverables would lift every dimension.

Suggestions

Add an explicit 'Use when...' clause listing concrete trigger phrases such as 'when the user asks for data analysis, predictive modeling, churn or forecast modeling, A/B test design or analysis, or customer segmentation'.

Replace generic action phrases ('Handles complex data analysis') with concrete deliverables, e.g. 'Builds and validates predictive models, designs and analyzes A/B tests, forecasts time series, segments customers'.

Include common user-side synonyms and variations ('data science', 'ML', 'forecasting', 'churn') to improve trigger term coverage and reduce overlap with adjacent analytics skills.

DimensionReasoningScore

Specificity

The description names the domain ('advanced analytics, machine learning, and statistical modeling') but its actions are generic: 'Handles complex data analysis, predictive modeling, and business intelligence' is at the 'Processes PDF files' level of abstraction, with no concrete deliverables like 'build churn prediction models' or 'design and analyze A/B tests'. It is above 1 because the domain is clearly named, but below 3 because no action is concretely specified.

2 / 5

Completeness

The 'what' is reasonably clear (an expert data scientist covering analytics, ML, and statistical modeling), but the 'when' is entirely absent — there is no 'Use when...' clause or equivalent trigger guidance, which per the guidelines caps completeness at 3. It is not 4 because 'when' is not present even weakly; it is not 2 because the 'what' is explicit rather than vague.

3 / 5

Trigger Term Quality

Terms like 'data scientist', 'machine learning', 'predictive modeling', and 'business intelligence' are natural phrases users would say, matching the 'Some relevant keywords but missing common variations or synonyms' anchor. It is below 4 because common variants users actually say — 'data science', 'forecasting', 'churn', 'A/B test', 'analysis of my data' — are absent.

3 / 5

Distinctiveness Conflict Risk

'Data scientist' names a recognizable niche, but the description's breadth ('complex data analysis', 'business intelligence') overlaps with adjacent skills such as data visualization, general code analysis, or business analytics personas, matching the 'Somewhat specific but could still overlap with similar skills' anchor. It is not 4 because the generic analysis/BI phrasing would contend with several related skills for the same queries.

3 / 5

Total

11

/

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

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository

davila7/claude-code-templates

Path

cli-tool/components/skills/ai-research/data-scientist/SKILL.md

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.