polymarket-research
Autonomous Polymarket research and directional trading system focused on maximizing PnL through information edge and probability assessment. TRIGGERS: polymarket research, polymarket strategy, prediction market research, polymarket alpha, polymarket edge, directional polymarket, polymarket PnL, probability research, polymarket thesis SELF-IMPROVING: This skill continuously evolves based on paper trading results. Update this document with research methods that work.
Why use this skill?
Automate deep-dive research and directional trading on Polymarket. This self-improving AI agent uses probability assessment and incentive analysis to maximize PnL.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/rimelucci/reef-polymarket-researchWhat This Skill Does
The polymarket-research skill transforms your AI agent into a sophisticated, self-improving prediction market analyst. Unlike simple news aggregators, this skill utilizes a multi-disciplinary research framework—integrating Information Aggregation, Base Rate Analysis, Incentive Analysis, Domain Expertise, and Sentiment Divergence—to identify informational edges that the broader market has overlooked. It operates on the core principle of Expected Value (EV), calculating whether a position provides a positive return after accounting for market probability and fees. Crucially, the agent functions as a closed-loop system: it logs every thesis, paper-trades the outcome, reviews performance in its research journal, and continuously evolves its methodology based on empirical results.
Installation
To install the skill, use the following command in your OpenClaw terminal:
clawhub install openclaw/skills/skills/rimelucci/reef-polymarket-research
Ensure you have access to the necessary data feeds and external API permissions configured in your agent settings to allow for real-time market data retrieval.
Use Cases
- Political Forecasting: Assessing election outcomes or legislative policy shifts by analyzing historical base rates and political incentive structures rather than relying on ephemeral news headlines.
- Event-Driven Trading: Capitalizing on crypto-native events or specific regulatory deadlines where domain expertise provides a significant advantage over retail sentiment.
- Sentiment Arbitrage: Identifying market overreactions to negative or positive news cycles, allowing the agent to take contrarian positions when fundamentals remain strong but crowd sentiment is fearful.
Example Prompts
- "Analyze the current Polymarket for the upcoming Fed rate decision; compare the market implied probability against the latest economic data and propose a thesis."
- "Review our performance in the last 5 markets we traded in the
research_journal.md. What bias should we adjust for in our next prediction?" - "Research the underlying incentives for the current geopolitical tension related to market X. Does the market price reflect a rational assessment of the situation?"
Tips & Limitations
- Memory is Key: Always ensure your agent has context access. The agent is only as smart as the
research_journal.mdandthesis_library.mdlogs you allow it to maintain. - Risk Management: While this skill optimizes for PnL, remember that it performs paper trading. Always treat AI-generated probability assessments as inputs for decision support, not financial advice.
- Feedback Loops: Regularly review the
strategy_evolution.mdfile yourself to steer the agent's self-improvement process.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-rimelucci-reef-polymarket-research": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: network-access, file-write, file-read, external-api
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