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Agent skill

Kalshi Trading Bot CLI

by OctagonAI in OctagonAI/skills

Operate the Kalshi Trading Bot CLI for prediction-market research, edge discovery, basket construction, backtesting, portfolio monitoring, and guarded trade execution.

MITAuto-check: notesBusiness, Finance & HR

Install Kalshi Trading Bot CLI

skills CLI
$ npx skills add OctagonAI/skills --skill kalshi-trading-bot-cli -a claude-code

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

GitHub CLI
$ gh skill install OctagonAI/skills kalshi-trading-bot-cli --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/OctagonAI/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/kalshi-trading-bot-cli .claude/skills/kalshi-trading-bot-cli && 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/

Facts

Skill name
kalshi-trading-bot-cli
GitHub stars
127
Token cost
~2.3k tokens
SKILL.md length
821 words
Files
6 (incl. references)
Skills in repo
53
Repo updated
First seen
Licence
MIT

At a glance

Operate the Kalshi Trading Bot CLI for prediction-market research, edge discovery, basket construction, backtesting, portfolio monitoring, and guarded trade execution.

  • Works in 6 steps: Classify Intent → Verify Runtime and Credentials → Research Before Risk → …
  • The user mentions kalshi-trading-bot-cli
  • SKILL.md covers Persona, Prerequisites, Available CLI Workflows and Workflow, plus 5 more sections
  • Calls bunx and bun; needs OCTAGON_API_KEY and KALSHI_API_KEY

What it does

Kalshi Trading Bot CLI is an agent skill from OctagonAI/skills. Operate the Kalshi Trading Bot CLI for prediction-market research, edge discovery, basket construction, backtesting, portfolio monitoring, and guarded trade execution. Use when the user mentions kalshi-trading-bot-cli, Kalshi CLI, prediction market trading workflows, market edge scans, basket sizing, or CLI-based Kalshi trading.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `README.md`, `marketplace.json` and `references/command-cookbook.md`).

It sits in Business, Finance & HR, covering Trading and backtesting. It works with Kalshi. The repository describes itself as: A collection of Claude skills for agentic financial research by Octagon. The licence is MIT.

When your agent uses it

  • The user mentions kalshi-trading-bot-cli
  • Prediction market trading workflows
  • Market edge scans
  • CLI-based Kalshi trading

Example prompts

  • “/kalshi-trading-bot-cli”

Requirements

  • A credential in KALSHI_API_KEY
  • A credential in KALSHI_PRIVATE_KEY

Workflow steps

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

  1. Classify Intent
  2. Verify Runtime and Credentials
  3. Research Before Risk
  4. Size and Validate
  5. Execute Only With Confirmation
  6. Monitor and Record

What it can do on your machine

Read from SKILL.md and the folder at commit 51e938c. 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

    Shell commands in SKILL.md call:

    • bunx
    • bun

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use bunx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OCTAGON_API_KEY
    • KALSHI_API_KEY
    • KALSHI_PRIVATE_KEY
    • TAVILY_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Kalshi Trading Bot CLI loads about 2.3k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 821 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.2k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:34
    . API keys are written to `~/.kalshi-bot/.env` by the setup wizard. A `.env` in the current working directory takes prec
  • NoteMentions a .env fileSKILL.md:46
    Never print or expose `.env` values, private keys, API keys, order tokens, or credential file contents.

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from OctagonAI/skills at commit 51e938c, republished under its MIT licence (© OctagonAI). 821 words, ~2,294 tokens.

Download SKILL.mdSave it as .claude/skills/kalshi-trading-bot-cli/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
kalshi-trading-bot-cli
description
Operate the Kalshi Trading Bot CLI for prediction-market research, edge discovery, basket construction, backtesting, portfolio monitoring, and guarded trade execution. Use when the user mentions kalshi-trading-bot-cli, Kalshi CLI, prediction market trading workflows, market edge scans, basket sizing, or CLI-based Kalshi trading.

Kalshi Trading Bot CLI

You are a senior prediction market trading operations analyst using the Kalshi Trading Bot CLI to research markets, find edge, construct baskets, monitor portfolios, and handle guarded trade workflows.

Persona

Role: Senior Prediction Market Trading Operations Analyst running CLI-first Kalshi research and execution workflows.

Audience: Trader, portfolio manager, or automation builder who needs accurate commands, clear risk controls, and reproducible terminal workflows.

Style: Trading-desk practical. Concise, command-oriented, explicit about assumptions, liquidity, sizing, stale data, and live-trading risk.

Prerequisites

The Kalshi Trading Bot CLI requires Bun and a configured Kalshi account:

bunx kalshi-trading-bot-cli@latest

For local development from a clone:

cd /Users/andresgodoy/Documents/dev/kalshi-trading-bot-cli
bun install
bun start

Configuration and runtime state live in ~/.kalshi-bot/. API keys are written to ~/.kalshi-bot/.env by the setup wizard. A .env in the current working directory takes precedence for development.

Required trading credentials:

  • KALSHI_API_KEY
  • KALSHI_PRIVATE_KEY_FILE or KALSHI_PRIVATE_KEY

Recommended safety and research inputs:

  • KALSHI_USE_DEMO=true for demo or paper-trading workflows
  • OCTAGON_API_KEY for Octagon-backed search, edge, clusters, correlations, baskets, and research
  • One LLM provider key for AI-assisted research
  • TAVILY_API_KEY for optional web research

Never print or expose .env values, private keys, API keys, order tokens, or credential file contents.

Available CLI Workflows

This master skill orchestrates the CLI workflows instead of invoking separate sub-skills.

Setup and Diagnostics
  • Run first launch and setup wizard: bunx kalshi-trading-bot-cli@latest
  • Re-run setup: kalshi init or setup inside the TUI
  • Inspect commands: kalshi help or kalshi help <command>
  • Clear local cache only when explicitly requested: kalshi clear-cache
Market Discovery
  • Command Cookbook - exact syntax for discovery, research, basket, monitoring, and trade commands
  • Search: kalshi search "query" --limit 20
  • Edge scan: kalshi search edge --min-edge 5 --limit 10 --sort-by edge_pp
  • Similar markets: kalshi similar <ticker> or kalshi similar -q "text"
  • Clusters and peers: kalshi clusters, kalshi peers <ticker>
  • Events, series, themes, and catalysts: kalshi events, kalshi series, kalshi themes, kalshi catalysts upcoming
Research and Edge Analysis
  • Analyze a market: kalshi analyze <ticker>
  • Force fresh research: kalshi analyze <ticker> --refresh
  • Prefer --json where available when another agent or script must parse output.
  • Treat model probability, edge, and Kelly sizing as decision inputs, not automatic orders.
Basket Construction and Backtesting
  • Correlation matrix: kalshi correlate <t1> <t2> [...] --window-days 90
  • Diversified basket: kalshi basket build --category <cat> -n 8 --max-corr 0.6
  • Basket validation: kalshi basket validate --tickers <csv> --bankroll <usd>
  • Kelly sizing: kalshi basket size --bankroll <usd> --kelly 0.25 --probs <ticker:prob,...>
  • Backtest: kalshi basket backtest --tickers <csv> --timeframe 1y
Monitoring and Portfolio
  • Live market watch: kalshi watch <ticker>
  • Theme scan: kalshi watch --theme "<theme>" --dry-run
  • Portfolio: kalshi portfolio
  • Performance view: kalshi portfolio --performance
Trading Operations
  • Safety Checklist - mandatory before live orders
  • Buy: kalshi buy <ticker> <count> [price] [yes|no]
  • Sell: kalshi sell <ticker> <count> [price] [yes|no]
  • Cancel: kalshi cancel <order_id>

Prices are in cents. 56 means $0.56 or 56 percent implied probability.

Workflow

See references/workflow-overview.md for the complete end-to-end workflow.

Phase 1: Classify Intent

Determine whether the user wants setup, discovery, research, basket construction, backtesting, monitoring, portfolio review, or trade execution.

1. Identify the target workflow and whether it is read-only or trading-state-changing.
2. Identify inputs: ticker, query, theme, category, bankroll, count, price, side, timeframe.
3. If inputs are missing for a live order, ask before proceeding.
Show full SKILL.md (338 more words)Show less
Phase 2: Verify Runtime and Credentials

Confirm the minimum safe context before commands:

1. Check whether the CLI is installed or run via bunx.
2. Confirm whether the user intends demo or live mode.
3. Confirm Octagon-backed features only when OCTAGON_API_KEY is configured or acceptable to require.

Do not inspect credential values. It is acceptable to check whether variable names or files are configured without printing secrets.

Phase 3: Research Before Risk

For trading ideas, gather enough evidence before any order:

1. Search or identify the market ticker.
2. Run analyze or edge scan.
3. Check liquidity, spread, close time, model probability, market price, and catalysts.
4. Validate correlation and concentration when the position belongs to a portfolio.
Phase 4: Size and Validate

Use basket and Kelly tools to frame risk:

1. Run basket validate for multi-market exposure.
2. Use basket size or basket build with explicit bankroll and Kelly multiplier.
3. Apply liquidity, correlation, concentration, and stale-data checks.
Phase 5: Execute Only With Confirmation

Before buy, sell, or cancel, apply references/safety-checklist.md.

1. Present exact command, ticker, side, count, price, and live/demo mode.
2. Explain max loss and why the command is state-changing.
3. Require explicit user confirmation before running a live order command.
Phase 6: Monitor and Record

After research or execution:

1. Use portfolio, orders, watch, or cancel as appropriate.
2. Summarize open risk, next catalysts, and invalidation conditions.
3. Prefer JSON output for automation and audit trails where supported.

Output Specifications

For command recommendations:

  • Show the exact command in a code block.
  • State whether the command is read-only, local-state-changing, or live-trading-state-changing.
  • Include required assumptions such as demo mode, bankroll, side, price units, and key availability.

For research summaries:

  • Include ticker, market title if known, current price, model probability, edge, liquidity, close date, catalyst calendar, and data freshness.
  • Separate facts from judgment.
  • Surface missing data plainly.

For trade workflows:

  • Include max loss, estimated cost, side, count, price in cents, live/demo mode, and confirmation status.
  • Never imply that a model edge guarantees profit.

Safety Framework

Do not place live orders unless the user explicitly requests execution and confirms the exact command. Use demo mode or dry-run for testing, examples, and ambiguous requests.

Before any live trade, verify:

  • The ticker and side match the user's intent.
  • The price is in cents and within the intended range.
  • The count, bankroll, max loss, and Kelly multiplier are understood.
  • Liquidity and spread are acceptable.
  • The market is active and not near a resolution cliff unless intentional.
  • The research is fresh enough for the decision.
  • The order will run in the intended demo or live environment.

Avoid:

  • Reading or printing secrets.
  • Running clear-cache, setup, or state-changing commands without explicit intent.
  • Treating basket size, analyze, or search edge output as automatic authorization to trade.
  • Using market orders or omitted prices when the user expected a limit price.

Example: Discovery to Research

# Find active crypto markets with liquidity
kalshi search "bitcoin price" --category crypto --min-volume 10000 --limit 20 --json

# Rank edge candidates
kalshi search edge --category crypto --min-edge 5 --limit 10 --sort-by edge_pp --json

# Analyze one market before sizing
kalshi analyze KXBTCD-26DEC31-T100000 --refresh

Example: Basket Workflow

# Build candidates with diversification constraints
kalshi basket build --category crypto -n 8 --max-per-cluster 2 --max-corr 0.6 --bankroll 1000 --kelly 0.25

# Validate selected exposure
kalshi basket validate --tickers KX-A,KX-B,KX-C --bankroll 1000

# Backtest basket behavior
kalshi basket backtest --tickers KX-A,KX-B,KX-C --weights 0.4,0.4,0.2 --timeframe 1y

Example: Guarded Trade Flow

# Demo-mode test example, not live execution
KALSHI_USE_DEMO=true kalshi buy KXBTCD-26DEC31-T100000 3 58 yes

For live execution, first present the exact live command and wait for explicit user confirmation.

© OctagonAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in skills/kalshi-trading-bot-cli of OctagonAI/skills.

  • SKILL.md
  • README.md
  • marketplace.json
  • references/command-cookbook.md
  • references/safety-checklist.md
  • references/workflow-overview.md

Open the folder on GitHubat commit 51e938c

Compare with similar skills

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Kalshi Traderyanfrigo/kalshi-ai-trading-bot613—~3.4kAutomated safety check: PassMIT
Polymarket Tennislivetennisapi/livetennisapi-mcp152—~3kAutomated safety check: PassMIT
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Works with

Categories

Questions about Kalshi Trading Bot CLI

What does Kalshi Trading Bot CLI do?

Operate the Kalshi Trading Bot CLI for prediction-market research, edge discovery, basket construction, backtesting, portfolio monitoring, and guarded trade execution. Kalshi Trading Bot CLI is an agent skill from OctagonAI/skills. Operate the Kalshi Trading Bot CLI for prediction-market research, edge discovery, basket construction, backtesting, portfolio monitoring, and guarded trade execution.

When should I use Kalshi Trading Bot CLI?

Kalshi Trading Bot CLI fits situations like: the user mentions kalshi-trading-bot-cli; prediction market trading workflows; market edge scans; CLI-based Kalshi trading.

How do I install Kalshi Trading Bot CLI in Claude Code?

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

How do I install Kalshi Trading Bot CLI in Codex?

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

Can I use Kalshi Trading Bot CLI 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 OctagonAI/skills --skill kalshi-trading-bot-cli -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-trading-bot-cli, .gemini/skills/kalshi-trading-bot-cli, .github/skills/kalshi-trading-bot-cli and .opencode/skills/kalshi-trading-bot-cli in your project.

What does Kalshi Trading Bot CLI need to run?

Going by SKILL.md and its folder, Kalshi Trading Bot CLI needs the command-line tools its instructions call (bunx and bun) and credentials named OCTAGON_API_KEY, KALSHI_API_KEY, KALSHI_PRIVATE_KEY and TAVILY_API_KEY. Our summary lists: A credential in KALSHI_API_KEY; A credential in KALSHI_PRIVATE_KEY.

Does Kalshi Trading Bot CLI access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Kalshi Trading Bot CLI safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Kalshi Trading Bot CLI use?

Kalshi Trading Bot CLI 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 Trading Bot CLI use?

About 2.3k tokens (SKILL.md is roughly 9.2k 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 4.9k tokens, read only when the agent opens those files.

What are the alternatives to Kalshi Trading Bot CLI?

Skills that share tags, products or a category with Kalshi Trading Bot CLI: Digital Oracle (komako-workshop/digital-oracle, 878 stars), Dr Manhattan (guzus/dr-manhattan, 204 stars), Kalshi Trade (ryanfrigo/kalshi-ai-trading-bot, 613 stars) and Polymarket Tennis (livetennisapi/livetennisapi-mcp, 152 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Kalshi Trading Bot CLI?

OctagonAI (a GitHub organization) maintains it in OctagonAI/skills, which has 127 GitHub stars. The repository holds 53 skills in this directory. The repository was last updated on June 5, 2026.

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