kalshi

machina-sports/sports-skills · updated Apr 8, 2026

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$npx skills add https://github.com/machina-sports/sports-skills --skill kalshi
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summary

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

skill.md

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:

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):

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.
  • 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.

Command Description
get_sports_config Available sport codes and series tickers
get_todays_events Today's events for a sport with nested markets
search_markets Find markets by sport and/or keyword
get_markets Market listing (raw API)
get_event Event details
get_market Market details
get_trades Recent trades
get_market_candlesticks OHLC 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

how to use kalshi

How to use kalshi on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add kalshi
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/machina-sports/sports-skills --skill kalshi

The skills CLI fetches kalshi from GitHub repository machina-sports/sports-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/kalshi

Reload or restart Cursor to activate kalshi. Access the skill through slash commands (e.g., /kalshi) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.756 reviews
  • Arya Lopez· Dec 28, 2024

    kalshi is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Michael Srinivasan· Dec 28, 2024

    I recommend kalshi for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ava Tandon· Dec 24, 2024

    We added kalshi from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Shikha Mishra· Dec 12, 2024

    kalshi fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Chen Rahman· Dec 12, 2024

    Useful defaults in kalshi — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Carlos Singh· Dec 4, 2024

    Registry listing for kalshi matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Valentina Thomas· Nov 19, 2024

    kalshi fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Mei Gonzalez· Nov 19, 2024

    Solid pick for teams standardizing on skills: kalshi is focused, and the summary matches what you get after install.

  • Kwame Singh· Nov 15, 2024

    Keeps context tight: kalshi is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Yash Thakker· Nov 3, 2024

    kalshi is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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