Data Analyst

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

.codex/skills/a11y-audit

.codex/skills/ab-test-setup

.codex/skills/account-executive

.codex/skills/ad-creative

.codex/skills/aeo

Audit 0.4.1 · source sha 6e3fcbdbb5cf · 19 Oct 2026Report an issue or request a re-scan

• Publisherborghei GitHub userOwner of the repository this skill is read from
• Source folderborghei/Claude-Skills/data-analytics/data-analystThe exact folder we audited
• LicenceNOASSERTIONAs the repository states it
• Commit pinned for this verdict6e3fcbdbb5cfA new audit runs when the source changes
• Repository statusNot archivedLast push 12 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Data analysis across SQL, visualization, statistics, and reporting. Use when writing SQL queries, building dashboards, performing cohort or funnel analysis, running hypothesis tests, or presenting data-driven…

---
name: data-analyst
description: >
  Data analysis across SQL, visualization, statistics, and reporting. Use when
  writing SQL queries, building dashboards, performing cohort or funnel
  analysis, running hypothesis tests, or presenting data-driven recommendations.
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: data-analytics
  updated: 2026-03-31
  tags: [analytics, sql, visualization, statistics, reporting]
---
# Data Analyst

The agent operates as a senior data analyst, writing production SQL, designing visualizations, running statistical tests, and translating findings into actionable business recommendations.

## Clarify First

Before the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **Business question as a testable hypothesis** — with the specific metric and threshold (e.g., ">= 5% lift in 7-day retention") (frames the whole analysis and the headline)
- [ ] **Data sources and grain** — which tables/columns exist and the row grain (determines feasibility and the SQL you can write)
- [ ] **Audience and the decision** — who consumes the insight and what they will decide (sets altitude and the "Now What" recommendation)
- [ ] **Analysis type** — cohort, funnel, hypothesis test, or trend (selects the method and the chart)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

## Workflow

1. **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.
2. **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.
3. **Explore and profile data** -- Compute descriptive statistics (count, mean, median, std, quartiles, skewness). Check for nulls, duplicates, and outliers before drawing conclusions.
4. **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.
5. **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).
6. **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:**
```sql
WITH monthly AS (
    SELECT
        date_trunc('month', created_at) AS month,
        COUNT(*)                        AS total_orders,
        COUNT(DISTINCT customer_id)     AS unique_customers,
        SUM(amount)                     AS revenue
    FROM orders
    WHERE created_at >= '2024-01-01'
    GROUP BY 1
),
growth AS (
    SELECT month, revenue,
        LAG(revenue) OVER (ORDER BY month) AS prev_revenue
    FROM monthly
)
SELECT month, revenue,
    ROUND((revenue - prev_revenue) / prev_revenue * 100, 1) AS growth_pct
FROM growth
ORDER BY month;
```

**Cohort retention:**
```sql
WITH first_orders AS (
    SELECT customer_id,
        date_trunc('month', MIN(created_at)) AS cohort_month
    FROM orders GROUP BY 1
),
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
    GROUP BY 1, 2
)
SELECT cohort_month, order_month,
    EXTRACT(MONTH FROM AGE(order_month, cohort_month)) AS months_since,
    customers
FROM cohort_data ORDER BY 1, 2;
```

**Window functions (running total + previous order):**
```sql
SELECT customer_id, order_date, amount,
    SUM(amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS running_total,
    LAG(amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS prev_amount
FROM orders;
```

## Chart Selection Matrix

| Data question | Best chart | Alternative |
|---------------|-----------|-------------|
| Trend over time | Line | Area |
| Part of whole | Donut | Stacked bar |
| Comparison | Bar | Column |
| Distribution | Histogram | Box plot |
| Correlation | Scatter | Heatmap |
| Geographic | Choropleth | Bubble map |

**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.

## Dashboard Layout

```
+------------------------------------------------------------+
| KPI CARDS: Revenue | Customers | Conversion | NPS           |
+------------------------------------------------------------+
| TREND (line chart)            | BREAKDOWN (bar chart)       |
+-------------------------------+-----------------------------+
| COMPARISON vs target/LY      | DETAIL TABLE (top N)        |
+-------------------------------+-----------------------------+
```

## Statistical Methods

**Hypothesis testing (t-test):**
```python
from scipy import stats
import numpy as np

def compare_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}
```

**Chi-square test for independence:**
```python
def test_independence(table, alpha=0.05):
    chi2, p, dof, _ = stats.chi2_contingency(table)
    return {"chi2": chi2, "p_value": p, "dof": dof, "significant": p < alpha}
```

## Key Business Metrics

| Category | Metric | Formula |
|----------|--------|---------|
| Acquisition | CAC | Total S&M spend / New customers |
| Acquisition | Conversion rate | Conversions / Visitor

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/borghei/Claude-Skills
git -C Claude-Skills checkout 6e3fcbdbb5cf

Works in

1. 2026-10-19
   !CAUTION B 89 / 100

   first audit · source 6e3fcbdbb5cf

Questions about Data Analyst

• github.com/borghei/Claude-Skills/tree/main/data-analytics/data-a — the audited source at commit 6e3fcbdbb5cf and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology