Kalshi logo

Kalshi

Organization
machina-sports
kalshi

Kalshi prediction markets — events, series, markets, trades, and candlestick data. Public API, no auth required for reads. US-regulated exchange (CFTC). Covers football (EPL, UCL, La Liga), basketball, baseball, tennis, NFL, hockey event contracts. Use when: user asks about Kalshi-specific markets, event contracts, CFTC-regulated prediction markets, or candlestick/OHLC price history on sports outcomes. Don't use when: user asks about actual match results, scores, or statistics — use the sport-specific skill: football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data (NHL), mlb-data (MLB), tennis-data (tennis), golf-data (golf), cricket-data (cricket), cfb-data (college football), cbb-data (college basketball), or fastf1 (F1). Don't use for general "who will win" questions unless Kalshi is specifically mentioned — try polymarket first (broader sports coverage). Don't use for news — use sports-news instead.

Overview

Publishermachina-sports
Repositorysports-skills
Skill namekalshi
Stars
255
Forks
38
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by machina-sports on GitHub. Read the source before you install it.

Installation

Install the Kalshi AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/machina-sports/sports-skills/tree/main/skills/kalshi
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/machina-sports/sports-skills.git /tmp/sports-skills
mkdir -p .claude/skills
cp -r /tmp/sports-skills/skills/kalshi .claude/skills/kalshi
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kalshi in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kalshi on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kalshi is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Kalshi — Prediction Markets

Before writing queries, consult references/api-reference.md for sport codes, series tickers, and command parameters.

Quick Start

Prefer the CLI — it avoids Python import path issues:

bash
sports-skills kalshi search_markets --sport=nba
sports-skills kalshi get_todays_events --sport=nba
sports-skills kalshi get_sports_config
sports-skills kalshi get_markets --series_ticker=KXNBA --status=open

Python SDK (alternative):

python
from sports_skills import kalshi

kalshi.search_markets(sport='nba')
kalshi.search_markets(sport='nba', query='Lakers')
kalshi.get_todays_events(sport='nba')
kalshi.get_sports_config()
kalshi.get_markets(series_ticker="KXNBA", status="open")

CRITICAL: Before Any Query

CRITICAL: Before calling any market endpoint, verify:

  • The sport parameter is always passed to search_markets and get_todays_events for single-game markets.
  • Prices are on a 0-100 integer scale (20 = 20% implied probability) — do not treat as American odds.
  • status="open" is used when querying markets to exclude settled/closed markets.

Without the sport parameter:

WRONG: search_markets(query="Leeds")           → 0 results
RIGHT: search_markets(sport='epl', query='Leeds') → returns all Leeds markets

Important Notes

  • On Kalshi, "Football" = NFL. For football/soccer (EPL, La Liga, etc.), use sport codes: epl, ucl, laliga, bundesliga, seriea, ligue1, mls, worldcup.
  • Formula 1 uses sport code f1 (drivers'/constructors' champion plus per-Grand-Prix race, podium, pole, fastest-lap, head-to-head and sprint series).
  • Market tickers are not always <event_ticker>-<suffix>. Kalshi mints some with a different stem, e.g. KXATP-26-SHA sits inside event KXATP-26USO (2026 US Open men's singles) next to KXATP-26USO-SIN. That is Kalshi's real ticker: pass it verbatim to get_market, and group markets by their event_ticker field, never by parsing the ticker.
  • Prices are probabilities. A last_price of 20 means 20% implied probability. Scale is 0-100 (not 0-1 like Polymarket).
  • Always use status="open" when querying markets, otherwise results include settled/closed markets.
  • Shared interface with Polymarket: search_markets(sport=...), get_todays_events(sport=...), and get_sports_config() work the same way on both platforms.

Workflows

Sport Market Search (Recommended)

  1. search_markets --sport=nba — finds all open NBA markets.
  2. Optionally add --query="Lakers" to filter by keyword.
  3. Results include yes_bid, no_bid, volume for each market.

Today's Events

  1. get_todays_events --sport=nba — open events with nested markets.
  2. Present events with prices (price = implied probability, 0-100 scale).

Futures Market Check

  1. get_markets --series_ticker=<ticker> --status=open
  2. Sort by last_price descending.
  3. Present top contenders with probability and volume.

Market Price History

  1. Get market ticker from search_markets --sport=nba.
  2. get_market_candlesticks --series_ticker=<s> --ticker=<t> --start_ts=<start> --end_ts=<end> --period_interval=60
  3. Present OHLC with volume.

Commands

See references/api-reference.md for the full command list with parameters.

CommandDescription
get_sports_configAvailable sport codes and series tickers
get_todays_eventsToday's events for a sport with nested markets
search_marketsFind markets by sport and/or keyword
get_esports_oddsEsports markets (cs2/lol/dota2): prices in cents (0-100) plus implied_probability (0-1) and decimal_odds
get_marketsMarket listing (raw API)
get_eventEvent details
get_marketMarket details
get_tradesRecent trades
get_market_candlesticksOHLC price history

Examples

Example 1: NBA market search User says: "What NBA markets are on Kalshi?" Actions:

  1. Call search_markets(sport='nba') Result: All open NBA markets with yes/no prices and volume

Example 2: EPL game markets User says: "Show me Leeds vs Man City odds on Kalshi" Actions:

  1. Call search_markets(sport='epl', query='Leeds') Result: Leeds EPL markets across all EPL series with prices and volume

Example 3: Today's EPL events User says: "What EPL games are available on Kalshi?" Actions:

  1. Call get_todays_events(sport='epl') Result: Today's EPL events with nested markets

Example 4: Champions League futures User says: "Who will win the Champions League?" Actions:

  1. Call search_markets(sport='ucl') or get_markets(series_ticker="KXUCL", status="open")
  2. Sort by last_price descending (price = implied probability) Result: Top UCL contenders with yes_sub_title, last_price (%), and volume

Example 5: Market price history User says: "Show me the price history for this NBA game" Actions:

  1. Get market ticker from search_markets(sport='nba')
  2. Call get_market_candlesticks(series_ticker="KXNBA", ticker="...", start_ts=..., end_ts=..., period_interval=60) Result: OHLC price data with volume

Commands that DO NOT exist — never call these

  • get_odds — does not exist. Use search_markets or get_markets to find market prices.
  • get_team_schedule — does not exist. Kalshi has markets, not schedules. Use the sport-specific skill for schedules.
  • get_scores / get_results — does not exist. Kalshi is a prediction market. Use the sport-specific skill.

If a command is not listed in references/api-reference.md, it does not exist.

Troubleshooting

Error: search_markets returns 0 results Cause: The sport parameter is missing — without it, search only returns high-volume futures and misses single-game markets Solution: Always pass sport='<code>' to search_markets. Check references/api-reference.md for valid sport codes

Error: Markets returned include settled/expired contracts Cause: status parameter is not set Solution: Always pass status="open" to filter to open markets only

Error: Series ticker returns no results Cause: The series ticker may be incorrect or have no open markets Solution: Call get_series_list() to discover available tickers, or check references/series-tickers.md

Error: Football/soccer markets not found when searching "Football" Cause: On Kalshi, "Football" refers to NFL — soccer uses league-specific codes Solution: Use sport='epl', sport='ucl', sport='laliga', etc. for soccer leagues

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Kalshi AI skill do?

Kalshi prediction markets — events, series, markets, trades, and candlestick data. Public API, no auth required for reads. US-regulated exchange (CFTC). Covers football (EPL, UCL, La Liga), basketball, baseball, tennis, NFL, hockey event contracts. Use when: user asks about Kalshi-specific markets, event contracts, CFTC-regulated prediction markets, or candlestick/OHLC price history on sports outcomes. Don't use when: user asks about actual match results, scores, or statistics — use the sport-specific skill: football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data...

Why use Kalshi on TypingMind?

Because you install it once and use it with any model. Kalshi is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kalshi in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/machina-sports/sports-skills/tree/main/skills/kalshi. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Kalshi?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kalshi?

As many as you like. As long as a model supports skills, you can use Kalshi with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kalshi AI skill free?

Yes. It is published on GitHub by machina-sports under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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