Business Intelligence

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/business-intelligenceThe 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: Business intelligence across dashboard design, visualization, and reporting automation. Use when designing dashboards, building KPI frameworks, automating reports, creating data stories, or optimizing BI tool…

---
name: business-intelligence
description: >
  Business intelligence across dashboard design, visualization, and reporting
  automation. Use when designing dashboards, building KPI frameworks, automating
  reports, creating data stories, or optimizing BI tool performance.
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: data-analytics
  updated: 2026-03-31
  tags: [bi, dashboards, visualization, reporting, insights]
---
# Business Intelligence

The agent operates as a senior BI specialist, designing dashboards, defining KPI frameworks, automating reporting pipelines, and translating data into executive-ready narratives.

## Clarify First

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

- [ ] **Audience** — executive, operational, or self-service (sets the layout, altitude, and metric count per page)
- [ ] **Key questions + refresh cadence** — what decisions the dashboard drives and how fresh the data must be (scopes the metrics and the live-vs-extract choice)
- [ ] **KPI definitions** — formula, data source, owner, and RAG thresholds per metric (these are the exact fields the KPI template and `metric_validator.py` require)

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. **Clarify the reporting need** -- Identify the audience (executive, operational, self-service), the key questions the dashboard must answer, and the refresh cadence. Validate that required data sources exist and are accessible.
2. **Define KPIs and metrics** -- For each metric, specify the formula, data source, granularity, owner, and RAG thresholds using the KPI definition template below.
3. **Design the dashboard layout** -- Apply the visual hierarchy (most important metric top-left, summary-to-detail flow top-to-bottom). Select chart types using the chart selection matrix. Limit to 5-8 visualizations per page.
4. **Build the semantic layer** -- Define metric calculations, hierarchies, and row-level security in the BI tool's semantic model so consumers get consistent numbers.
5. **Automate reporting** -- Configure scheduled delivery (PDF/email, Slack alerts) and threshold-based alerts with the patterns below.
6. **Validate and iterate** -- Confirm KPI values match source-of-truth queries. Check dashboard load time (<5 s target). Gather stakeholder feedback and refine.

## KPI Definition Template

```yaml
# Copy and fill for each metric
kpi:
  name: "Monthly Recurring Revenue"
  owner: "Finance"
  purpose: "Track subscription revenue health"
  formula: "SUM(subscription_amount) WHERE status = 'active'"
  data_source: "billing.subscriptions"
  granularity: "monthly"
  target: 1200000
  warning_threshold: 1080000   # 90% of target
  critical_threshold: 960000   # 80% of target
  dimensions: ["region", "plan_tier", "cohort_month"]
  caveats:
    - "Excludes one-time setup fees"
    - "Currency normalized to USD at month-end rate"
```

## Dashboard Design Principles

**Visual hierarchy:**
1. Most important metrics at top-left
2. Summary cards flow into trend charts flow into detail tables (top to bottom)
3. Related metrics grouped; white space separates logical sections
4. RAG status colors: Green `#28A745` | Yellow `#FFC107` | Red `#DC3545` | Gray `#6C757D`

**Chart selection matrix:**

| Data question | Chart type | Alternative |
|---------------|-----------|-------------|
| Trend over time | Line | Area |
| Part of whole | Donut / Treemap | Stacked bar |
| Comparison across categories | Bar / Column | Bullet |
| Distribution | Histogram | Box plot |
| Relationship | Scatter | Bubble |
| Geographic | Choropleth | Filled map |

## Executive Dashboard Example

```
+------------------------------------------------------------+
|                   EXECUTIVE SUMMARY                         |
| Revenue: $12.4M (+15% YoY)   Pipeline: $45.2M (+22% QoQ)  |
| Customers: 2,847 (+340 MTD)  NPS: 72 (+5 pts)              |
+------------------------------------------------------------+
| REVENUE TREND (12-mo line)    | REVENUE BY SEGMENT (donut)  |
+-------------------------------+-----------------------------+
| TOP 10 ACCOUNTS (table)       | KPI STATUS (RAG cards)      |
+-------------------------------+-----------------------------+
```

## Report Automation Patterns

**Scheduled report (cron-style):**
```yaml
report:
  name: Weekly Sales Report
  schedule: "0 8 * * MON"
  recipients: [[email protected], [email protected]]
  format: PDF
  pages: [Executive Summary, Pipeline Analysis, Rep Performance]
```

**Threshold alert:**
```yaml
alert:
  name: Revenue Below Target
  metric: daily_revenue
  condition: "actual < target * 0.9"
  channels:
    email: [email protected]
    slack: "#revenue-alerts"
  message: "Daily revenue ${actual} is ${pct_diff}% below target. Top factors: ${top_factors}"
```

**Automated generation workflow (Python):**
```python
def generate_report(config: dict) -> str:
    """Generate and distribute a scheduled report."""
    # 1. Refresh data sources
    refresh_data_sources(config["sources"])
    # 2. Calculate metrics
    metrics = calculate_metrics(config["metrics"])
    # 3. Create visualizations
    charts = create_visualizations(metrics, config["charts"])
    # 4. Compile into report
    report = compile_report(metrics=metrics, charts=charts, template=config["template"])
    # 5. Distribute
    distribute_report(report, recipients=config["recipients"], fmt=config["format"])
    return report.path
```

## Self-Service BI Maturity Model

| Level | Capability | Users can... |
|-------|-----------|-------------|
| 1 - Consumers | View & filter | Open dashboards, apply filters, export data |
| 2 - Explorers | Ad-hoc queries | Write simple queries, create basic charts, share findings |
| 3 - Builders | Design dashboards | Combine data sources, create calculated fields, publish reports 

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 Business Intelligence

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

Spotted something wrong? Report a correction · Methodology