Monitor Kalshi

Monitor Kalshi prediction markets and rank 24h trading changes into alerts.

6|2|Updated May 21, 2026
One-click install
npx skills add https://github.com/anajuliabit/aeon --skill monitor-kalshi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Monitor Kalshi
Source: https://github.com/anajuliabit/aeon/tree/main/skills/monitor-kalshi
Command: npx skills add https://github.com/anajuliabit/aeon --skill monitor-kalshi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns noisy Kalshi price tables into a decision-focused digest by identifying which prediction markets moved enough, with enough liquidity, to matter over the last 24 hours.

Core Features & Use Cases

  • Watchlist-driven monitoring: Watches a configured list of Kalshi event tickers (or a single ad-hoc ticker) and analyzes only open markets.
  • 24h move + liquidity scoring: Computes intraday change from candlesticks, ranks markets by a move score weighted by activity, and estimates conviction using orderbook spread and depth.
  • Noise suppression and alerts: Suppresses small/low-volume moves, highlights large non-thin-book swings as alerts, and demotes continued-from-yesterday repeats.

Quick Start

Configure your watchlist in skills/monitor-kalshi/watchlist.md (one event ticker per line) and run the skill to receive a ranked report for the last 24h with alerts for the biggest non-thin-book movers.

Frequently Asked Questions about Monitor Kalshi

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I monitor Kalshi prediction markets for meaningful 24h price moves?▼

To monitor Kalshi prediction markets, you can configure a watchlist of event tickers to fetch candlestick deltas and orderbook depth from the Kalshi public trade API, producing a ranked, low-noise digest of 24h trading changes and alerts for significant movers.

What is the best way to filter low-liquidity noise from Kalshi market monitoring?▼

Filtering low-liquidity noise from Kalshi market monitoring involves applying suppression and threshold rules to candlestick deltas and orderbook spread data, which demotes small-volume moves and highlights only large, non-thin-book swings as alerts.

Can I generate research alerts for Kalshi markets based on orderbook depth and implied probability?▼

Yes, you can generate research alerts for Kalshi markets by estimating conviction using orderbook spread and depth metrics, which calculates an activity-weighted move score to flag large non-thin-book swings and continued movers within an event watchlist.

How does a candlestick delta calculation work for Kalshi market surveillance?▼

Candlestick delta calculation for Kalshi market surveillance computes the intraday change in implied probability by fetching 24h trading data from the public trade API, ranking markets by a move score weighted by activity and liquidity signals.

How do I set up a watchlist for scheduled Kalshi market surveillance and morning briefs?▼

Set up a watchlist for scheduled Kalshi market surveillance by adding one event ticker per line in the configuration file, then run the skill to analyze open markets and receive a ranked report with alerts for the biggest 24h movers.

Why are some continued Kalshi market movers demoted in my monitoring alerts?▼

Continued Kalshi market movers are demoted in monitoring alerts because the noise suppression rules identify and lower the priority of repeats from yesterday, ensuring the digest focuses on new, high-conviction swings rather than ongoing trends.