qbr-intelligence-engine

Files (12)

• QUICK_REFERENCE.md
• README.md
• SKILL.md
• dashboard_theme.css · assets/
• email_templates.txt · assets/
• qbr_dashboard_template.html · assets/
• KPI_BENCHMARKS.json · references/
• RISK_FRAMEWORK.json · references/
• STRATEGIC_FRAMEWORKS.json · references/
• forecast_engine.py · scripts/
• kpi_calculator.py · scripts/
• risk_scorer.py · scripts/

SKILL.md (first lines)

# QBR Intelligence Engine

## Purpose

Transform scattered business metrics into a cohesive, executive-ready Quarterly Business Review (QBR) that tells your company's story, identifies critical trends, predicts risks, and recommends strategic actions. This skill turns raw data into the insights C-suite needs to make confident quarterly decisions.

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## Input Requirements

The AI should gather the following business data:

### Company Context

- Company name and industry
- Fiscal quarter (Q1-Q4, what year)
- Company stage (startup, growth, mature)
- Number of employees
- Geographic markets served
- Primary business model (SaaS, marketplace, DTC, B2B services, etc.)

### Financial Metrics

- Revenue (current quarter vs. last quarter vs. year-ago quarter)
- Gross profit / Gross margin %
- Operating expenses (broken down by category: R&D, Sales/Marketing, G&A, Other)
- Operating income / Operating margin %
- Cash position / Runway (in months)
- Headcount (current vs. target)
- Customer Acquisition Cost (CAC)
- Lifetime Value (LTV)
- Burn rate (if pre-revenue or pre-profitability)

### Product/Customer Metrics

- Total customers / Monthly Active Users
- Net Revenue Retention (NRR)
- Churn rate %
- New customer acquisition
- Customer acquisition by channel (% breakdown)
- Product usage metrics (daily active, sessions, feature adoption)
- Customer satisfaction (NPS, CSAT, or similar)
- Support metrics (ticket volume, resolution time, satisfaction)

### Key Initiatives & Execution

- Top 3 initiatives launched this quarter
- Progress on quarterly OKRs (% completed)
- Key wins (major customer, partnership, milestone)
- Key challenges faced
- Any product/market misalignment signals
- Competitive landscape changes

### Looking Ahead

- Priorities for next quarter
- Hiring plans
- Budget allocation changes
- Market expansion plans
- Known risks/concerns

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## Processing Steps

The AI should perform the following analysis:

### Step 1: Normalize & Validate Data

- Convert all metrics to consistent formats
- Identify data gaps and flag for stakeholder
- Calculate missing-but-essential metrics (e.g., if MRR given, calculate quarterly)
- Note data quality concerns

### Step 2: Calculate Performance Metrics

- Quarter-over-Quarter (QoQ) growth %
- Year-over-Year (YoY) growth %
- Trend direction (accelerating, maintaining, decelerating)
- Benchmark against industry standards (reference: KPI_BENCHMARKS.json)
- Calculate health scores (revenue health, customer health, team health)

### Step 3: Identify Trends & Patterns

- Revenue momentum (accelerating or decelerating?)
- Unit economics health (CAC, LTV, payback period)
- Retention trends (stable, improving, concerning?)
- Efficiency trends (burn rate, operating margin trajectory)
- Customer concentration risk (top customer % of revenue)
- Geographic/channel concentration risk

### Step 4: Predictive Analysis

- Revenue forecast (next 2 quarters using trend extrapolation from current metrics)
- Runway projection (if pre-profitability)
- Churn forecast (if applicable)
- Hiring impact on margins
- Identify 1-3 leading indicators that predict next quarter performance

### Step 5: Risk Assessment

- Score risks using reference: RISK_FRAMEWORK.json
- Identify 5-7 key risks (customer concentration, market, execution, financing, retention)
- Rate each: High/Medium/Low + impact if realized + mitigation strategy
- Highlight any "red flags" requiring immediate attention

### Step 6: Competitive Context

- How metrics position company vs. industry benchmarks
- Major competitive moves this quarter
- Market share implications
- Differentiation strengths/weaknesses

### Step 7: Strategic Recommendations

- 3-5 high-impact strategic actions for next quarter
- Prioritization framework (impact × effort)
- Resource allocation recommendations
- Quick wins vs. longer-term bets

From baralmanish/AI-Agent-Skills, MIT.

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