Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
BlackSnow ingests fragmented, legally accessible data exhaust from procurement, regulatory, labor, and logistics sources. It applies Bayesian evidence accumulation and ontology alignment to surface pre-event risk vectors before formal news or disclosures occur. Outputs are structured risk primitives consumable by financial, insurance, logistics, and policy systems.
Surfaces risk signals weeks before formal events by correlating individually weak public data points that news monitoring and sentiment analysis miss.
When to use it
- Detecting infrastructure stress signals before grid outages
- Flagging supply chain disruptions ahead of formal announcements
- Identifying regulatory drift before rules are published
- Generating risk primitives for insurance underwriting decisions
- Monitoring labor system stress indicators before strikes or freezes
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: What energy infrastructure risk signals exist in a target region over the next 30 days?
- 1Harvester collects public procurement notices, maintenance deferrals, and labor filings from approved domains
- 2Normalizer maps heterogeneous inputs into a unified risk ontology
- 3Accumulator performs Bayesian evidence accumulation across contributing domains over time
- 4Forecaster estimates outcome likelihoods and temporal windows
- 5Packager outputs a structured risk primitive with confidence score and tradability flags by market type
JSON risk vector with signal confidence, 21-45 day time horizon, contributing domains, likely outcomes (localized outage, price volatility, policy intervention), and tradability flags for insurance and commodities
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.