Agent skill

Kalshi API

by agiprolabs in agiprolabs/claude-trading-skills

Kalshi exchange mechanics — RSA-PSS auth, order schema, YES/NO orderbook convention, WebSocket, and endpoint surface.

Install Kalshi API

skills CLI

$ npx skills add agiprolabs/claude-trading-skills --skill kalshi-api -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install agiprolabs/claude-trading-skills kalshi-api --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/kalshi-api .claude/skills/kalshi-api && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "kalshi-api" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/kalshi-api into .claude/skills/kalshi-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kalshi-api", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/kalshi-api

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add agiprolabs/claude-trading-skills --skill kalshi-api -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install agiprolabs/claude-trading-skills kalshi-api --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/kalshi-api .agents/skills/kalshi-api && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "kalshi-api" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/kalshi-api into .agents/skills/kalshi-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kalshi-api", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add agiprolabs/claude-trading-skills --skill kalshi-api -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install agiprolabs/claude-trading-skills kalshi-api --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/kalshi-api .cursor/skills/kalshi-api && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "kalshi-api" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/kalshi-api into .cursor/skills/kalshi-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kalshi-api", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/agiprolabs/claude-trading-skills.git --path skills/kalshi-api

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add agiprolabs/claude-trading-skills --skill kalshi-api -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install agiprolabs/claude-trading-skills kalshi-api --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/kalshi-api .gemini/skills/kalshi-api && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "kalshi-api" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/kalshi-api into .gemini/skills/kalshi-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kalshi-api", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install agiprolabs/claude-trading-skills kalshi-api

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add agiprolabs/claude-trading-skills --skill kalshi-api -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/kalshi-api .github/skills/kalshi-api && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "kalshi-api" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/kalshi-api into .github/skills/kalshi-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kalshi-api", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add agiprolabs/claude-trading-skills --skill kalshi-api -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install agiprolabs/claude-trading-skills kalshi-api --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/kalshi-api .opencode/skills/kalshi-api && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "kalshi-api" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/kalshi-api into .opencode/skills/kalshi-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kalshi-api", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: kalshi-api
GitHub stars: 410
Token cost: ~2k tokens
SKILL.md length: 552 words
Files: 5 (incl. scripts, references)
Skills in repo: 68
Repo updated: Sep 3, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Kalshi exchange mechanics — RSA-PSS auth, order schema, YES/NO orderbook convention, WebSocket, and endpoint surface.

Works in 4 steps: Credentials → Install → Host + auth (the part everyone gets wrong) → …
Tasks that involve Realtime and WebSockets
SKILL.md covers Overview, Quick Start, YES/NO Order-Book Convention and Order Schema, plus 2 more sections
Runs Python scripts from its folder; calls pip; reaches api.elections.kalshi.com; needs KALSHI_KEY_ID

What it does

Kalshi API is an agent skill from agiprolabs/claude-trading-skills. 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.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/auth-and-orders.md`, `references/endpoints-and-marketdata.md` and `references/websocket.md`).

It sits in Backend & APIs, covering Realtime and WebSockets. It works with Kalshi. The repository describes itself as: 68 trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools. The licence is MIT.

When your agent uses it

Tasks that involve Realtime and WebSockets

Example prompts

“/kalshi-api”

Requirements

Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

Credentials
Install
Host + auth (the part everyone gets wrong)
Candlestick history

What it can do on your machine

Read from SKILL.md and the folder at commit 981e1d7. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- api.elections.kalshi.com
Also links to:
- docs.kalshi.com
- trading-api.readme.io
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- KALSHI_KEY_ID
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Kalshi API loads about 2k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 552 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~46
When it runs · the whole SKILL.md, loaded when a task matches: ~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~5.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 552 words, ~2,012 tokens.

Download SKILL.mdSave it as .claude/skills/kalshi-api/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.

name: kalshi-api
description: 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.

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:

API reference: https://docs.kalshi.com (legacy mirror: https://trading-api.readme.io)

Official Python starter: https://github.com/Kalshi/kalshi-starter-code-python

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:

Using the old trading-api.kalshi.com host → 401
Including the query string in the signed path → 401
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.

Show full SKILL.md (233 more words)Show less

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:

Field	Rule	Common mistake that 400s
`count_fp`	fixed-point string `"1.00"`	integer `count: 1`
`{side}_price_dollars`	dollar string `"0.01"`	integer cents `yes_price: 1`
`time_in_force`	required: `good_till_canceled` \| `immediate_or_cancel` \| `fill_or_kill`	omitted
`client_order_id`	`[A-Za-z0-9-]` only	`.` or `:` in the string — bracket tickers contain `.`, so never copy the ticker directly
`type`	do 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

Category	Endpoint	Notes
Balance	`GET /portfolio/balance`	—
Positions	`GET /portfolio/positions`	—
Orders	`GET /portfolio/orders`	`?status=resting\|canceled\|executed`
Place order	`POST /portfolio/orders`	dollar-string schema above
Cancel	`DELETE /portfolio/orders/{id}`	returns `{"order": {...status: "canceled"}}`
Amend	`POST /portfolio/orders/{id}/amend`	`ticker` required in body
Decrease	`POST /portfolio/orders/{id}/decrease`	`{"reduce_by_fp": "1.00"}`
Batch	`POST /portfolio/orders/batched`	`{"orders": [...]}`
Fills	`GET /portfolio/fills`	`?limit=N`
Settlements	`GET /portfolio/settlements`	`?limit=N`
Markets	`GET /markets`	`?series_ticker=&status=open&limit=500`
Orderbook	`GET /markets/{ticker}/orderbook`	`?depth=N`
Candlesticks	`GET /series/{series}/markets/{ticker}/candlesticks`	`?start_ts=&end_ts=&period_interval=60`
Trades	`GET /markets/trades`	recent 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.

Files

SKILL.md and 4 other files (scripts, references) in skills/kalshi-api of agiprolabs/claude-trading-skills.

SKILL.md
references/auth-and-orders.md
references/endpoints-and-marketdata.md
references/websocket.md
scripts/kalshi_orderbook.py

Open the folder on GitHubat commit 981e1d7

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Questions about Kalshi API

What does Kalshi API do?

Kalshi exchange mechanics — RSA-PSS auth, order schema, YES/NO orderbook convention, WebSocket, and endpoint surface. Kalshi API is an agent skill from agiprolabs/claude-trading-skills. Kalshi exchange mechanics — RSA-PSS auth, order schema, YES/NO orderbook convention, WebSocket, and endpoint surface.

When should I use Kalshi API?

Kalshi API fits situations like: tasks that involve Realtime and WebSockets.

How do I install Kalshi API in Claude Code?

Run `npx skills add agiprolabs/claude-trading-skills --skill kalshi-api -a claude-code`. Or copy the skill folder (skills/kalshi-api in agiprolabs/claude-trading-skills) into .claude/skills/kalshi-api in your project. Claude Code loads it when a task matches its description.

How do I install Kalshi API in Codex?

Run `npx skills add agiprolabs/claude-trading-skills --skill kalshi-api -a codex`. Or copy the skill folder (skills/kalshi-api in agiprolabs/claude-trading-skills) into .agents/skills/kalshi-api in your project. Codex loads it when a task matches its description.

Can I use Kalshi API in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agiprolabs/claude-trading-skills --skill kalshi-api -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kalshi-api, .gemini/skills/kalshi-api, .github/skills/kalshi-api and .opencode/skills/kalshi-api in your project.

What does Kalshi API need to run?

Going by SKILL.md and its folder, Kalshi API needs Python for the scripts in its folder, the command-line tools its instructions call (pip) and credentials named KALSHI_KEY_ID. Our summary lists: Python 3.

Does Kalshi API access the network?

SKILL.md names 4 domains. In commands or code: api.elections.kalshi.com; the agent is likely to contact it when it follows the instructions. As links in the text: docs.kalshi.com, trading-api.readme.io and github.com. This is read from the text; nothing was executed.

Is Kalshi API safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Kalshi API use?

Kalshi API is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Kalshi API use?

About 2k tokens (SKILL.md is roughly 8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.3k tokens, read only when the agent opens those files.

What are the alternatives to Kalshi API?

Skills that share tags, products or a category with Kalshi API: Feeds (alsk1992/CloddsBot, 2.9k stars), Supabase Development and Debugging (supabase/agent-skills, 2.7k stars), Use Yaak (mountain-loop/yaak, 19k stars) and Gemini Live API Dev (google-gemini/gemini-skills, 4.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Kalshi API?

agiprolabs (a GitHub user) maintains it in agiprolabs/claude-trading-skills, which has 410 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on September 3, 2026.

Source: agiprolabs/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Kalshi API

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Kalshi API

Overview

Quick Start

1. Credentials

2. Install

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

4. Candlestick history

YES/NO Order-Book Convention

Order Schema

Endpoint Summary

Files

References

Scripts

Files

Compare with similar skills

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