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    "article": "\n# chainstacklabs/polyclaw\n\n- **Archetype**: domain-skill-pack\n- **Stars**: 301\n- **Last pushed**: 2026-02-27\n- **License**: Apache-2.0\n- **Discovered**: 2026-04-12\n- **Source**: ClawHub skills (published as \"chainstacklabs/polyclaw\", maps to \"joelchance/polymarket\" ClawHub listing)\n- **Skills found**: 1 SKILL.md\n- **Fork**: No\n\n## Summary\n\nTrading-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.\n\nKey features:\n- Market browsing (trending, search, details) with JSON output\n- On-chain trading via split + CLOB execution (buy YES/NO positions)\n- Position tracking with entry price, current price, P&L (stored in ~/.openclaw/polyclaw/positions.json)\n- Wallet management (status, approvals)\n- Hedge discovery using LLM-powered contrapositive logic (coverage tiers T1-T3)\n- Requires Chainstack node, private key, and OpenRouter API key\n\n## Assessment\n\nLOW 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.\n\n**Extraction priority**: LOW\n\n# Extractable Value: chainstacklabs/polyclaw\n\n## Processes\n\n### 1. Prediction Market Research\n- **Source**: Market browsing + hedge discovery analysis\n- **Placement**: `specializations/business/prediction-market-research.js`\n- **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.\n\n## Plugin Ideas\n\n### 1. Prediction Market Signal Plugin\n- **Category**: Knowledge Management\n- **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.\n\n## Library Mapping\n\n| Extractable Process | Library Status | Action | Existing Path | Target Placement |\n|-------------------|----------------|--------|---------------|------------------|\n| Prediction Market Research | NEW | Research prediction markets for probability assessments and market sentiment analysis | - | specializations/business/prediction-market-research.js |\n| LLM-Powered Logical Analysis | NEW | Contrapositive analysis pattern for distinguishing causation from correlation | - | specializations/shared/llm-logical-analysis.js |\n| Coverage Tier Risk Assessment | NEW | Graduated confidence levels (T1/T2/T3) for risk assessment processes | - | specializations/shared/coverage-tier-risk-assessment.js |\n\n## Plugin Marketplace Mapping\n\n| Plugin Idea | Marketplace Status | Action | Existing Plugin | Target Placement |\n|-------------|-------------------|--------|-----------------|------------------|\n| Prediction Market Signal | NEW | Read-only market browsing and probability signal sourcing for research processes | - | plugins/a5c/marketplace/plugins/prediction-market-signal/ |\n\n## Implicit Procedural Knowledge\n\n- **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.\n- **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.\n",
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