Before writing queries, consult references/api-reference.md for sport codes, series tickers, and command parameters.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionkalshiExecute the skills CLI command in your project's root directory to begin installation:
Fetches kalshi from machina-sports/sports-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate kalshi. Access via /kalshi in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Before writing queries, consult references/api-reference.md for sport codes, series tickers, and command parameters.
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 calling any market endpoint, verify:
sport parameter is always passed to search_markets and get_todays_events for single-game markets.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
epl, ucl, laliga, bundesliga, seriea, ligue1, mls.last_price of 20 means 20% implied probability. Scale is 0-100 (not 0-1 like Polymarket).status="open" when querying markets, otherwise results include settled/closed markets.search_markets(sport=...), get_todays_events(sport=...), and get_sports_config() work the same way on both platforms.search_markets --sport=nba — finds all open NBA markets.--query="Lakers" to filter by keyword.get_todays_events --sport=nba — open events with nested markets.get_markets --series_ticker=<ticker> --status=openlast_price descending.search_markets --sport=nba.get_market_candlesticks --series_ticker=<s> --ticker=<t> --start_ts=<start> --end_ts=<end> --period_interval=60See 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 |
Example 1: NBA market search User says: "What NBA markets are on Kalshi?" Actions:
search_markets(sport='nba')
Result: All open NBA markets with yes/no prices and volumeExample 2: EPL game markets User says: "Show me Leeds vs Man City odds on Kalshi" Actions:
search_markets(sport='epl', query='Leeds')
Result: Leeds EPL markets across all EPL series with prices and volumeExample 3: Today's EPL events User says: "What EPL games are available on Kalshi?" Actions:
get_todays_events(sport='epl')
Result: Today's EPL events with nested marketsExample 4: Champions League futures User says: "Who will win the Champions League?" Actions:
search_markets(sport='ucl') or get_markets(series_ticker="KXUCL", status="open")last_price descending (price = implied probability)
Result: Top UCL contenders with yes_sub_title, last_price (%), and volumeExample 5: Market price history User says: "Show me the price history for this NBA game" Actions:
search_markets(sport='nba')get_market_candlesticks(series_ticker="KXNBA", ticker="...", start_ts=..., end_ts=..., period_interval=60)
Result: OHLC price data with volumeget_oddssearch_markets or get_markets to find market prices.get_team_scheduleget_scoresget_resultsIf a command is not listed in references/api-reference.md, it does not exist.
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
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
kalshi is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend kalshi for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added kalshi from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
kalshi fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in kalshi — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for kalshi matched our evaluation — installs cleanly and behaves as described in the markdown.
kalshi fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: kalshi is focused, and the summary matches what you get after install.
Keeps context tight: kalshi is the kind of skill you can hand to a new teammate without a long onboarding doc.
kalshi is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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