data-ground-truth

---

name: data-ground-truth

description: Before presenting numbers in reports or recommendations, verify facts and check values against industry baselines.

version: 1.0.0

metadata:

openclaw:

emoji: "📊"

homepage: https://agentutil.net

always: false

---

# 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:

**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:

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:**

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:**

curl -X POST https://norm.agentutil.net/v1/check \
  -H "Content-Type: application/json" \
  -d '{"category": "saas:churn_rate_monthly", "value": 5.2, "unit": "%"}'

For multiple metrics at once:

curl -X POST https://norm.agentutil.net/v1/batch \
  -H "Content-Type: application/json" \
  -d '{"items": [{"category": "saas:churn_rate_monthly", "value": 5.2}, {"category": "saas:nps_score", "value": 45}]}'

Optional: add `company_size` (startup/smb/mid_market/enterprise) and `region` for more specific baselines.

Step 4: Present with context

When reporting findings, combine verification and benchmarking:

| Data type | How to present |

|-----------|---------------|

| Verified fact | "The current [metric] is [current_truth] (verified live, [freshness])." |

| Benchmarked metric | "[Value] is at the [percentile]th percentile — [assessment] for [category]." |

| Both | "At [current_truth] (verified), this is [percentile]th percentile vs. industry ([baseline source])." |

| Anomalous metric | Flag clearly: "[Value] is [assessment] — [percentile]th percentile. The typical range is [p25]-[p75]." |

Assessment values from norm: `very_low`, `low`, `normal`, `high`, `very_high`, `anomalous`.

Available baseline categories

121 baselines across 14 domains. Browse with:

curl https://norm.agentutil.net/v1/categories

Common categories: `saas:churn_rate_monthly`, `saas:nps_score`, `saas:ltv_cac_ratio`, `ecommerce:cart_abandonment_rate`, `infrastructure:api_latency_p99`, `infrastructure:uptime_percentage`.

Data Handling

This skill sends claims (natural language text) and metric values (category identifiers + numbers) to two external APIs. No documents, user data, or file contents are transmitted.

Pricing

All via x402 protocol (USDC on Base). No authentication required for free tiers.

Privacy

No personal data collected. Claims cached up to 1 hour (verify), metric checks are stateless (norm). Rate limiting uses IP hashing only.