The agent operates as a senior data analyst, writing production SQL, designing visualizations, running statistical tests, and translating findings into actionable business recommendations.
Fetches data-analyst from borghei/claude-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
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│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/data-analyst
Restart Cursor to activate data-analyst. Access via /data-analyst in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
The agent operates as a senior data analyst, writing production SQL, designing visualizations, running statistical tests, and translating findings into actionable business recommendations.
Workflow
Frame the business question -- Restate the stakeholder's question as a testable hypothesis with a clear metric (e.g., "Did campaign X increase 7-day retention by >= 5%?"). Identify required data sources.
Write and validate SQL -- Use CTEs for readability. Filter early, aggregate late. Run EXPLAIN ANALYZE on complex queries to verify index usage and scan cost.
Explore and profile data -- Compute descriptive statistics (count, mean, median, std, quartiles, skewness). Check for nulls, duplicates, and outliers before drawing conclusions.
Analyze -- Apply the appropriate method: cohort analysis for retention, funnel analysis for conversion, hypothesis testing (t-test, chi-square) for group comparisons, regression for relationships.
Visualize -- Select chart type from the matrix below. Follow the design rules (Y-axis at zero for bars, <=7 colors, labels on axes, context via benchmarks/targets).
Deliver the insight -- Structure findings as What / So What / Now What. Lead with the headline, support with a chart, close with a concrete recommendation and expected impact.
SQL Patterns
Monthly aggregation with growth:
WITH monthly AS(SELECT date_trunc('month', created_at)ASmonth,COUNT(*)AS total_orders,COUNT(DISTINCT customer_id)AS unique_customers,SUM(amount)AS revenue
FROM orders
WHERE created_at >='2024-01-01'GROUPBY1),growth AS(SELECTmonth, revenue, LAG(revenue)OVER(ORDERBYmonth)AS prev_revenue
FROM monthly
)SELECTmonth, revenue,ROUND((revenue - prev_revenue)/ prev_revenue *100,1)AS growth_pct
FROM growth
ORDERBYmonth;
Cohort retention:
WITH first_orders AS(SELECT customer_id, date_trunc('month',MIN(created_at))AS cohort_month
FROM orders GROUPBY1),cohort_data AS(SELECT f.cohort_month, date_trunc('month', o.created_at)AS order_month,COUNT(DISTINCT o.customer_id)AS customers
FROM orders o
JOIN first_orders f ON o.customer_id = f.customer_id
GROUPBY1,2)SELECT cohort_month, order_month, EXTRACT(MONTHFROM AGE(order_month, cohort_month))AS months_since, customers
FROM cohort_data ORDERBY1,2;
Window functions (running total + previous order):
Design rules: Start Y-axis at zero for bar charts. Use <= 7 colors. Label axes. Include benchmarks or targets for context. Avoid 3D charts and pie charts with > 5 slices.
from scipy import stats
import numpy as np
defcompare_groups(a: np.ndarray, b: np.ndarray, alpha:float=0.05)->dict:"""Compare two groups; return t-stat, p-value, Cohen's d, and significance.""" stat, p = stats.ttest_ind(a, b) d =(a.mean()- b.mean())/ np.sqrt((a.std()**2+ b.std()**2)/2)return{"t_statistic": stat,"p_value": p,"cohens_d": d,"significant": p < alpha}
## [Headline: action-oriented finding]**What:** One-sentence description of the observation.
**So What:** Why this matters to the business (revenue, retention, cost).
**Now What:** Recommended action with expected impact.
**Evidence:** [Chart or table supporting the finding]
**Confidence:** High / Medium / Low
Analysis Framework
# Analysis: [Topic]## Business Question -- What are we trying to answer?## Hypothesis -- What do we expect to find?## Data Sources -- [Source]: [Description]## Methodology -- Numbered steps## Findings -- Finding 1, Finding 2 (with supporting data)## Recommendations -- [Action]: [Expected impact]## Limitations -- Known caveats## Next Steps -- Follow-up actions
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
