Description

Detects pre-news ambient risk signals across human, legal, and operational systems and converts them into machine-readable, tradable risk primitives.

README (SKILL.md)

BlackSnow

Name: Blacksnow
Author: sieershafilone

Invisible Risk Exhaust → Tradable Signal Engine

BlackSnow is an economic sensor skill that ingests fragmented, low-signal, legally accessible data exhaust from multiple non-obvious domains. It applies ontology alignment, weak-signal Bayesian accumulation, and horizon forecasting to surface early risk vectors before formal events, news, or disclosures occur.

Outputs are structured for automated consumption by financial, insurance, logistics, and policy systems.

Core Capabilities

Ambient Risk Detection: Surfaces pre-event signals invisible to traditional monitoring
Weak-Signal Correlation: Connects individually meaningless data points into predictive patterns
Cross-Domain Ontology Fusion: Aligns heterogeneous inputs into unified risk primitives
Probabilistic Forecasting: Estimates outcome likelihoods and temporal windows
Tradable Signal Packaging: Converts internal risk states into sellable primitives

Non-Capabilities

❌ Insider information
❌ Sentiment analysis
❌ News aggregation
❌ Price prediction
❌ Decision execution

What BlackSnow Detects

Signals that exist weeks earlier, fragmented across obscure, low-signal sources:

Micro-Behavioral Shifts

Municipal procurement wording changes
Infrastructure maintenance deferrals
Insurance clause revisions
Supply contract force-majeure language

Operational Anomalies

Unexpected overtime tenders
Silent vendor substitutions
Emergency inventory buffering

Legal Entropy

Draft regulation language drift
Repeated consultation extensions
Committee member attendance decay

Human System Stress

Attrition spikes in critical roles
Hiring freezes masked as "role realignment"
Union grievance language tone shifts

Output Schema

{
  "risk_vector": "infra.energy.grid",
  "signal_confidence": 0.87,
  "time_horizon_days": "21-45",
  "contributing_domains": ["procurement", "maintenance", "labor"],
  "likely_outcomes": [
    "localized outage",
    "price volatility",
    "policy intervention"
  ],
  "tradability": {
    "insurance": true,
    "commodities": true,
    "logistics": true,
    "policy": false
  }
}

Agents

Agent	Role	Description
`harvester`	Ingestion	Collects obscure, legally accessible data exhaust from approved domains
`normalizer`	Semantic Alignment	Maps heterogeneous inputs into a unified risk ontology
`accumulator`	Probabilistic Reasoning	Performs Bayesian evidence accumulation over time
`forecaster`	Horizon Modeling	Estimates outcome likelihoods and temporal windows
`packager`	Monetization Interface	Converts internal risk states into sellable signal primitives

Data Sources

Allowed

Public procurement notices
Regulatory draft documents
Contract language revisions
Maintenance and tender logs
Labor and union filings
Hiring and attrition metadata
Inventory and logistics metadata

Forbidden

Private communications
Leaked documents
Paywalled sources without license
Personal identifiable information

Monetization Tiers

Tier	Access	Price
Observer	Aggregated heatmaps	$99/mo
Operator	Raw risk vectors	$1,500/mo
Fund/API	Real-time streaming signals	$10k–50k/mo
Sovereign	Custom domains & exclusivity	$250k+/yr

Add-ons

Region exclusivity
Early-signal SLA
Historical backtesting
Compliance attestation

Integration

Compatible skills:

tradebot
hedgecore
logistics-router
policy-simulator

Chaining mode: async

Constraints

Legal

GDPR compliant
No personal data storage
No market manipulation intent

Ethical

No targeted individual profiling
No civilian harm forecasting

Operational

Explainability not guaranteed
Probabilistic outputs only

Risk Disclaimer

BlackSnow provides probabilistic risk intelligence, not predictions or advice. Users are solely responsible for downstream decisions and compliance.

Status

Deployment: Sandbox
Onboarding: Gated
Audit Required: Yes

Usage Guidance

This skill bundles code that scrapes/harvests, stores memory, and sends webhooks but declares no required credentials or install steps — that's a red flag. Before installing: 1) Review the scripts (harvester*, pipeline.py, memory.py, webhook.py) to confirm what endpoints are contacted, what is persisted, and whether any default URLs or keys are embedded. 2) Verify how the skill enforces its 'forbidden' list (no PII, no paywalled sources) — there is no technical proof in SKILL.md. 3) If you don't want autonomous network activity, set disableModelInvocation: true or otherwise require manual invocation. 4) Require an audit or code review for GDPR/PII handling and confirm where webhook targets will send data. 5) Ask the publisher which environment variables/credentials are actually needed and why they are not declared. These steps will reduce the risk of unexpected data collection or exfiltration.

Capability Analysis

Type: OpenClaw Skill Name: blacksnow Version: 0.1.0 The skill is classified as suspicious primarily due to insecure network communication practices. The `scripts/harvester.py` and `scripts/harvester_extended.py` files disable SSL certificate verification (`ctx.check_hostname = False`, `ctx.verify_mode = ssl.CERT_NONE`) when fetching data from public APIs. This makes the data collection vulnerable to Man-in-the-Middle (MITM) attacks, potentially compromising the integrity of the public data ingested by the skill, which could lead to the generation of manipulated or misleading risk primitives. While there is no clear evidence of intentional credential theft, unauthorized execution, or prompt injection against the agent, this significant security weakness in data acquisition constitutes a risky capability.

Capability Assessment

⚠ Purpose & Capability

Name/description match the included scripts (harvester, pipeline, webhook, memory). However, the SKILL.md declares no required credentials or config paths while the codebase implies network I/O, data storage, and potential external integrations (monetization, streaming). The monetization and integration claims (real-time streaming, tradebot/hedgecore integration) suggest external API keys and credentials which are not declared — an incoherence that reduces transparency.

⚠ Instruction Scope

SKILL.md gives high-level agent roles but not bounded runtime instructions. Phrases like 'collects obscure, legally accessible data exhaust from approved domains' grant the agent broad latitude about what to fetch and from where. The skill claims to forbid private or paywalled sources, but there is no concrete enforcement mechanism described. Presence of memory.py and webhook.py suggests the runtime could persist or exfiltrate data or open network endpoints; those operations are not scoped or constrained in the instructions.

✓ Install Mechanism

No install spec is provided — the skill is instruction/code-only and does not download arbitrary binaries during install. That lowers installation risk. All code is bundled with the skill (scripts/*), so there are no external download URLs in the manifest to flag.

⚠ Credentials

The manifest declares no required environment variables or primary credentials, yet the functionality (webhooks, streaming outputs, integrations with trading/monetization endpoints) implies the need for API keys, access tokens, or destination URLs. The lack of declared env requirements is disproportionate and reduces the user's ability to audit what secrets the skill will need or access.

ℹ Persistence & Privilege

always is not set and disableModelInvocation is not set (default enabled), so the model could invoke this skill autonomously. That is common for integration skills, but given this skill's potential to collect, store, and forward ambient signals, you should be aware the agent may trigger network I/O and data storage without additional explicit settings. The skill does include a memory component, indicating persistence capability.

Version History

v0.1.0

BlackSnow 0.1.0 – Initial Release - Launches an economic sensor skill for early, pre-news ambient risk detection. - Ingests fragmented, legally accessible data across operational, legal, and human systems. - Applies ontology fusion, Bayesian accumulation, and forecasting for predictive risk vector surfacing. - Structures outputs as machine-readable, tradable primitives for finance, insurance, logistics, and policy use. - Clearly defines allowed/forbidden data sources, monetization tiers, and compliance/ethical constraints. - Lists integration points and status as sandbox, gated onboarding, and requiring audit.

Metadata

Slug blacksnow

Version 0.1.0

License —

All-time Installs 0

Active Installs 0

Total Versions 1

Frequently Asked Questions

What is Blacksnow?

Detects pre-news ambient risk signals across human, legal, and operational systems and converts them into machine-readable, tradable risk primitives. It is an AI Agent Skill for Claude Code / OpenClaw, with 1607 downloads so far.

How do I install Blacksnow?

Run "/install blacksnow" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Blacksnow free?

Yes, Blacksnow is completely free (open-source). You can download, install and use it at no cost.

Which platforms does Blacksnow support?

Blacksnow is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Blacksnow?

It is built and maintained by Sieer Shafi Lone (@sieershafilone); the current version is v0.1.0.

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