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chainstacklabs/polyclaw reference

Trading-enabled Polymarket prediction markets skill for OpenClaw. Written in Python with uv dependency management. Provides market browsing, wallet management, on-chain trading (split + CLOB execution on Polygon), position tracking with P&L, and LLM-powered hedge discovery.

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chainstacklabs/polyclaw

  • **Archetype**: domain-skill-pack
  • **Stars**: 301
  • **Last pushed**: 2026-02-27
  • **License**: Apache-2.0
  • **Discovered**: 2026-04-12
  • **Source**: ClawHub skills (published as "chainstacklabs/polyclaw", maps to "joelchance/polymarket" ClawHub listing)
  • **Skills found**: 1 SKILL.md
  • **Fork**: No

Summary

Trading-enabled Polymarket prediction markets skill for OpenClaw. Written in Python with uv dependency management. Provides market browsing, wallet management, on-chain trading (split + CLOB execution on Polygon), position tracking with P&L, and LLM-powered hedge discovery.

Key features:

  • Market browsing (trending, search, details) with JSON output
  • On-chain trading via split + CLOB execution (buy YES/NO positions)
  • Position tracking with entry price, current price, P&L (stored in ~/.openclaw/polyclaw/positions.json)
  • Wallet management (status, approvals)
  • Hedge discovery using LLM-powered contrapositive logic (coverage tiers T1-T3)
  • Requires Chainstack node, private key, and OpenRouter API key

Assessment

LOW extractable value for babysitter. This is a domain-specific financial trading skill. The hedge discovery using LLM-powered logical analysis is intellectually interesting but highly niche. The on-chain trading patterns are not generalizable. However, the prediction market data could be useful as a signal source in research processes.

**Extraction priority**: LOW

Extractable Value: chainstacklabs/polyclaw

Processes

1. Prediction Market Research

  • **Source**: Market browsing + hedge discovery analysis
  • **Placement**: specializations/business/prediction-market-research.js
  • **Description**: Process for researching prediction markets: search for markets by topic -> fetch market details and current prices -> analyze correlated markets for hedging opportunities -> generate research briefing with probability assessments and market sentiment. Breakpoint for user review before any position-taking recommendations. Read-only, no trading.

Plugin Ideas

1. Prediction Market Signal Plugin

  • **Category**: Knowledge Management
  • **install.md**: Installs polyclaw Python dependencies (uv sync), configures Chainstack node URL (free tier). Read-only mode: provides babysitter tasks for browsing Polymarket markets, fetching current probabilities, and searching by topic. No trading keys required for read-only use. Useful as a probability signal source in research and decision-making processes.

Library Mapping

Extractable ProcessLibrary StatusActionExisting PathTarget Placement
Prediction Market ResearchNEWResearch prediction markets for probability assessments and market sentiment analysis-specializations/business/prediction-market-research.js
LLM-Powered Logical AnalysisNEWContrapositive analysis pattern for distinguishing causation from correlation-specializations/shared/llm-logical-analysis.js
Coverage Tier Risk AssessmentNEWGraduated confidence levels (T1/T2/T3) for risk assessment processes-specializations/shared/coverage-tier-risk-assessment.js

Plugin Marketplace Mapping

Plugin IdeaMarketplace StatusActionExisting PluginTarget Placement
Prediction Market SignalNEWRead-only market browsing and probability signal sourcing for research processes-plugins/a5c/marketplace/plugins/prediction-market-signal/

Implicit Procedural Knowledge

  • **LLM-powered logical analysis for hedging**: Using an LLM to find contrapositive implications between prediction markets (only logically necessary implications accepted, not correlations). This strict logical filtering pattern is applicable to any LLM-powered analysis where you need to distinguish causation from correlation.
  • **Coverage tier classification**: The T1/T2/T3 tier system for rating hedge quality (>=95%, 90-95%, 85-90%) is a pattern for graduated confidence levels in any risk assessment process.

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