- 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:
- 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:
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/categoriesCommon 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
- Verify: 25 free/day, then $0.004/query
- Norm: free category listing, $0.002/check or $0.001/batch item
- Full ground-truth check (verify + norm): ~$0.006 per data point
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.