data-ground-truth

# data-ground-truth

When presenting numbers, metrics, or statistics in reports, recommendations, or analysis — verify the facts and contextualize the figures against industry baselines. Combines verify (live fact-checking) with norm (statistical benchmarking).

## When to Activate

Use this skill when:

- Writing a report that cites specific metrics (revenue, churn, conversion rates)
- A user shares their business numbers and asks "is this good?"
- Comparing a metric to industry standards ("how does our 5% churn compare?")
- Building a recommendation that depends on current market data
- Presenting financial figures that may have changed since training
- Analyzing a dataset and wanting to flag outliers against known baselines

**Do NOT use for:** opinions, qualitative assessments, or metrics with no established baseline.

## Workflow

### Step 1: Classify the data point

Determine whether each number is:
- **A factual claim** (exchange rate, stock price, population) → route to **verify**
- **A business/performance metric** (churn rate, NPS, response time) → route to **norm**
- **Both** (e.g., "our conversion rate of 3.2% is above average") → check both

### Step 2: Verify factual claims

For current facts (prices, rates, dates), use verify-claim.

**MCP (preferred):** `verify_claim({ claim: "The USD to EUR exchange rate is 0.92" })`

**HTTP:**
```bash
curl -X POST https://verify.agentutil.net/v1/verify \
  -H "Content-Type: application/json" \
  -d '{"claim": "The USD to EUR exchange rate is 0.92"}'
```

Handle verdicts per the verify-claim decision tree (confirmed → use, stale → update, disputed → present both sides, false → correct).

### Step 3: Benchmark metrics against baselines

For business metrics, check where the value falls on the distribution.

**MCP (preferred):** `norm_check({ category: "saas:churn_rate_monthly", value: 5.2, unit: "%" })`

**HTTP:**
```bash
curl -X POST https://norm.agentutil.net/v1/check \
  -H "Content-Type: application/json" \
  -d...