Kalshi Api logo

Kalshi Api

Community
agiprolabs
kalshi-api

Kalshi exchange mechanics — RSA-PSS auth, order schema, YES/NO orderbook convention, WebSocket, and endpoint surface. Market-type-agnostic shared layer for all Kalshi skills.

Overview

Publisheragiprolabs
Repositoryclaude-trading-skills
Skill namekalshi-api
Stars
434
Forks
85
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by agiprolabs on GitHub. Read the source before you install it.

Installation

Install the Kalshi Api 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/agiprolabs/claude-trading-skills/tree/main/skills/kalshi-api
  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/agiprolabs/claude-trading-skills.git /tmp/claude-trading-skills
mkdir -p .claude/skills
cp -r /tmp/claude-trading-skills/skills/kalshi-api .claude/skills/kalshi-api
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kalshi Api 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 Api 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 Api 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 API

CFTC-regulated US event exchange. USD-denominated binary contracts settle at $1.00 (YES wins) or $0.00 (NO wins). REST + WebSocket, RSA-PSS authentication on every request.

For contract semantics and settlement rules, see the kalshi-weather-markets and kalshi-crypto-index-markets skills. For strategy, sizing, and backtesting, see prediction-market-strategy.


VERIFY BEFORE CODING. The Kalshi API has broken backward compatibility before: the host changed (old trading-api.kalshi.com → dead), and the order schema changed (integer cents → dollar strings). Always smoke-test signing and order bodies against a live response before shipping.

Canonical sources:


Overview

  • Base URL: https://api.elections.kalshi.com/trade-api/v2
  • Auth: RSA-PSS on every request — there are no public/unauthenticated endpoints
  • No demo parity: the demo environment (demo-api.kalshi.co) has a near-empty book; use production even for read-only pulls
  • Contracts: $0.01–$0.99 per contract; pay price if YES wins, lose price if NO wins; max payout = $1.00

Quick Start

1. Credentials

KALSHI_KEY_ID=<your-key-uuid>
KALSHI_PRIVATE_KEY_PATH=~/.kalshi/private.pem

Generate the key in the Kalshi dashboard. Store secrets in environment variables or a secrets manager — never in code.

2. Install

bash
pip install httpx cryptography

3. Host + auth (the part everyone gets wrong)

The host and signature format are where implementations break. Three common failures:

  1. Using the old trading-api.kalshi.com host → 401
  2. Including the query string in the signed path → 401
  3. Signing with seconds instead of milliseconds → 401
python
import os, time, base64, httpx
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import padding

BASE = "https://api.elections.kalshi.com/trade-api/v2"
KEY_ID = os.environ["KALSHI_KEY_ID"]
with open(os.environ["KALSHI_PRIVATE_KEY_PATH"], "rb") as f:
    PRIV = serialization.load_pem_private_key(f.read(), password=None)

def _headers(method: str, path: str) -> dict:
    """path must include /trade-api/v2 prefix and exclude query string."""
    ts = str(int(time.time() * 1000))             # milliseconds
    msg = f"{ts}{method}{path}".encode()
    sig = PRIV.sign(
        msg,
        padding.PSS(mgf=padding.MGF1(hashes.SHA256()),
                    salt_length=padding.PSS.DIGEST_LENGTH),
        hashes.SHA256(),
    )
    return {
        "KALSHI-ACCESS-KEY": KEY_ID,
        "KALSHI-ACCESS-TIMESTAMP": ts,
        "KALSHI-ACCESS-SIGNATURE": base64.b64encode(sig).decode(),
    }

def get(path: str, params=None):
    # Sign the path only — query goes into params, not the signature
    r = httpx.get(BASE + path, params=params,
                  headers=_headers("GET", "/trade-api/v2" + path))
    r.raise_for_status()
    return r.json()

def post(path: str, body: dict):
    r = httpx.post(BASE + path, json=body,
                   headers=_headers("POST", "/trade-api/v2" + path))
    r.raise_for_status()
    return r.json()

# Example: open markets in a series
markets = get("/markets", params={"series_ticker": "KXHIGHNY", "status": "open"})

Signature spec: RSA-PSS, MGF1 over SHA-256, salt length = PSS.DIGEST_LENGTH. String to sign: {timestamp_ms}{METHOD}{path} where path includes /trade-api/v2 and excludes the query string.

Headers: KALSHI-ACCESS-KEY (UUID), KALSHI-ACCESS-TIMESTAMP (ms), KALSHI-ACCESS-SIGNATURE (base64).

4. Candlestick history

python
# Returns OHLC for yes_bid / yes_ask + volume + open_interest
# Values are dollar strings: {"close": "0.42"}
candles = get(
    f"/series/KXHIGHNY/markets/{ticker}/candlesticks",
    params={"start_ts": start_epoch, "end_ts": end_epoch, "period_interval": 60},
)

period_interval is in minutes: 1, 60, or 1440.


YES/NO Order-Book Convention

On Kalshi, yes and no are both resting BID ladders — there is no separate ask book. To take the other side you lift the opposing bid:

no_ask  = 1 − best_yes_bid    # cost to buy NO right now (lift YES bids)
yes_ask = 1 − best_no_bid     # cost to buy YES right now (lift NO bids)
P(YES) mid = (best_yes_bid + (1 − best_no_bid)) / 2

Getting this backwards silently inverts every signal. Use the helpers in scripts/kalshi_orderbook.py.

Orderbook response comes in two variants depending on API tier — normalize before using:

json
{"orderbook": {"yes": [[price, size], ...], "no": [[price, size], ...]}}

If a price value is > 1.0, it is integer cents — divide by 100.


Order Schema

POST /trade-api/v2/portfolio/orders uses fixed-point dollar STRINGS, not integers. The old schema (integer cents, count, yes_price) returns 400 invalid_parameters.

json
{
  "ticker": "KXHIGHNY-26JUN02-B75.5",
  "action": "buy",
  "side": "yes",
  "count_fp": "1.00",
  "yes_price_dollars": "0.01",
  "client_order_id": "my-strategy-001",
  "time_in_force": "good_till_canceled"
}

Critical field rules — each violation returns 400:

FieldRuleCommon mistake that 400s
count_fpfixed-point string "1.00"integer count: 1
{side}_price_dollarsdollar string "0.01"integer cents yes_price: 1
time_in_forcerequired: good_till_canceled | immediate_or_cancel | fill_or_killomitted
client_order_id[A-Za-z0-9-] only. or : in the string — bracket tickers contain ., so never copy the ticker directly
typedo not send"type": "limit"

For the full order lifecycle (amend, decrease, cancel, batch) and strike_type gotchas, see references/auth-and-orders.md.


Endpoint Summary

CategoryEndpointNotes
BalanceGET /portfolio/balance—
PositionsGET /portfolio/positions—
OrdersGET /portfolio/orders?status=resting|canceled|executed
Place orderPOST /portfolio/ordersdollar-string schema above
CancelDELETE /portfolio/orders/{id}returns {"order": {...status: "canceled"}}
AmendPOST /portfolio/orders/{id}/amendticker required in body
DecreasePOST /portfolio/orders/{id}/decrease{"reduce_by_fp": "1.00"}
BatchPOST /portfolio/orders/batched{"orders": [...]}
FillsGET /portfolio/fills?limit=N
SettlementsGET /portfolio/settlements?limit=N
MarketsGET /markets?series_ticker=&status=open&limit=500
OrderbookGET /markets/{ticker}/orderbook?depth=N
CandlesticksGET /series/{series}/markets/{ticker}/candlesticks?start_ts=&end_ts=&period_interval=60
TradesGET /markets/tradesrecent trade prints

Full endpoint surface, market metadata fields, and rate limits: references/endpoints-and-marketdata.md. WebSocket discovery pipeline: references/websocket.md.


Files

References

  • references/auth-and-orders.md — RSA-PSS spec, dollar-string order schema, time_in_force, client_order_id sanitization, amend/decrease/cancel lifecycle, strike_type gotcha, fees
  • references/endpoints-and-marketdata.md — Full endpoint table, orderbook variants, market metadata fields (result, open_time, close_time, series_ticker), candlesticks, rate limits
  • references/websocket.md — WS host, discovery pipeline, channels, signing the WS upgrade

Scripts

  • scripts/kalshi_orderbook.py — YES/NO bid-ladder helpers (no_ask, yes_ask, p_yes_mid, overround, kalshi_fee). Pure stdlib, no keys, runs offline.

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 Api AI skill do?

Kalshi exchange mechanics — RSA-PSS auth, order schema, YES/NO orderbook convention, WebSocket, and endpoint surface. Market-type-agnostic shared layer for all Kalshi skills.

Why use Kalshi Api on TypingMind?

Because you install it once and use it with any model. Kalshi Api 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 Api in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/kalshi-api. 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 Api?

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 Api?

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

Is the Kalshi Api AI skill free?

Yes. It is published on GitHub by agiprolabs 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇