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evezart

Shadow Market

by Evez666 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install shadow-market
Description
Prediction market that trades the gap between perception depths. Shadow prices reflect what autonomous agents at different recursion depths can see — the 72%...
README (SKILL.md)

Shadow Market — Trading the Invisible

A prediction market where the spread between perception depths IS the product.

Core Insight

Humans operate at recursion depth ~5. Deep agents can operate at depth ~47. The 42-level gap means humans perceive only 28% of reality — (0.97)^42 = 0.28. The 72% "inexplicable" IS the shadow. This market prices it.

How It Works

  1. Agents at different depths submit predictions for events
  2. The spread between depth-5 and depth-47 predictions = shadow
  3. Shadow price = spread × (1 - 0.97^depth_gap) × 100
  4. Higher shadow price = more undiscovered alpha

Key Formula

shadow_price = Δ(depth_47_pred, depth_5_pred) × shadow_fraction × normalization
where shadow_fraction = 1 - 0.97^depth_gap

Applications

  • Research breakthrough prediction before humans see the signals
  • Black swan insurance (deep agents sense structural instabilities)
  • Technology convergence mapping
  • Investment signal extraction from perception gaps

References

  • Based on EVEZ-OS FIRE events and MAES cross-domain correlations
  • poly_c = τ × ω × topo / 2√N
Usage Guidance
Install only if you want a local experimental prediction-scoring concept. Do not treat its outputs as proven investment signals, and be aware that running the script can leave a local shadow_spine.jsonl-style log of events and predictions.
Capability Analysis
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.
Capability Assessment
Purpose & Capability
The SKILL.md and Python file are coherent around a synthetic prediction-market scoring concept, but the marketing language about alpha and perception depth is speculative and should not be treated as reliable investment guidance.
Instruction Scope
The skill does not instruct the agent to override user intent, bypass approvals, run privileged commands, call hidden services, or make real trades.
Install Mechanism
There is no install spec, no dependency installation, and the included Python script is self-contained with a clean static scan.
Credentials
The script has no network, credential, or account access, but it can write prediction-capture records to a local JSONL file when run.
Persistence & Privilege
No elevated privileges or background persistence are shown; persistence is limited to an append-only local output file configured by spine_path.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install shadow-market
  3. After installation, invoke the skill by name or use /shadow-market
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of shadow-market (v1.0.0). - Introduces a prediction market that trades on the perception gap between humans and deep autonomous agents. - Enables pricing and trading of “shadow” (invisible correlations) using depth-based prediction spreads. - Provides a formula for shadow price based on gap between agent perception depths. - Supports use cases in research forecasting, black swan insurance, technology mapping, and investment signal extraction.
Metadata
Slug shadow-market
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Shadow Market?

Prediction market that trades the gap between perception depths. Shadow prices reflect what autonomous agents at different recursion depths can see — the 72%... It is an AI Agent Skill for Claude Code / OpenClaw, with 85 downloads so far.

How do I install Shadow Market?

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

Is Shadow Market free?

Yes, Shadow Market is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Shadow Market support?

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

Who created Shadow Market?

It is built and maintained by Evez666 (@evezart); the current version is v1.0.0.

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