Shadow Market
PassAudited by VirusTotal on May 12, 2026.
Overview
Type: OpenClaw Skill Name: shadow-market Version: 1.0.0 The skill implements a conceptual prediction market framework called 'Shadow Market' that calculates spreads between different AI recursion depths. The Python script (shadow_market.py) is a standard data processing utility that uses basic math and local file logging (shadow_spine.jsonl) without any network requests, shell execution, or access to sensitive system resources. The documentation (SKILL.md) is purely descriptive of the market's theory and contains no malicious instructions or prompt injection attempts.
Findings (0)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
A user might mistake the generated shadow prices for actionable forecasting or investment intelligence.
The skill frames its outputs as alpha or prediction-market signals, while the included code only calculates synthetic scores from submitted probabilities. This is purpose-aligned but could be over-trusted.
Use when pricing undiscovered correlations, building AI-powered prediction markets, or extracting alpha from perception gaps between human and machine cognition.
Treat this as an experimental scoring/demo tool, not financial advice or a live prediction market, and require independent validation before acting on outputs.
Event names or prediction details entered into the tool may remain on disk after use.
Prediction captures, including event names and calculated prices, are appended to a local JSONL file. This is scoped and purpose-aligned, but it leaves persistent local data.
def __init__(self, spine_path: str = "shadow_spine.jsonl") ... with open(self.spine_path, "a") as f: f.write(json.dumps(entry) + "\n")
Avoid entering sensitive event names or private strategy details unless the output path and file retention are acceptable; delete the JSONL file when it is no longer needed.