markets

Original：🇺🇸 English

Translated

Markets orchestration — connects ESPN live schedules with Kalshi & Polymarket prediction markets. Unified dashboards, odds comparison, entity search, and bet evaluation across platforms. Use when: user wants to see prediction market odds alongside ESPN game schedules, compare odds across platforms, search for a team/player on Kalshi or Polymarket, check for arbitrage between ESPN odds and prediction markets, or evaluate a specific game's market value. Don't use when: user wants raw prediction market data without ESPN context — use polymarket or kalshi directly. For pure odds math (conversion, de-vigging, Kelly) — use betting. For live scores without market data — use the sport-specific skill.

3installs

Sourcemachina-sports/sports-skills

Added on2026-03-01

NPX Install

npx skill4agent add machina-sports/sports-skills markets

SKILL.md Content

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Markets Orchestration

Bridges ESPN live schedules (NBA, NFL, MLB, NHL, WNBA, CFB, CBB) with Kalshi and Polymarket prediction markets. Passes sport context to both platforms for accurate single-game market matching.

Quick Start

bash

# Today's games with matching prediction markets
sports-skills markets get_todays_markets --sport=nba

# Search for a team across both exchanges
sports-skills markets search_entity --query="Lakers" --sport=nba

# Compare ESPN odds vs prediction market prices for a game
sports-skills markets compare_odds --sport=nba --event_id=401234567

# Sports-filtered market listing (no political/weather noise)
sports-skills markets get_sport_markets --sport=nfl

# Unified ESPN schedule
sports-skills markets get_sport_schedule --sport=nba

# Normalize a price from any source
sports-skills markets normalize_price --price=0.65 --source=polymarket

# Full evaluation: ESPN odds + market price → devig → edge → Kelly
sports-skills markets evaluate_market --sport=nba --event_id=401234567

Python SDK:

python

from sports_skills import markets

markets.get_todays_markets(sport="nba")
markets.search_entity(query="Lakers", sport="nba")
markets.compare_odds(sport="nba", event_id="401234567")
markets.get_sport_markets(sport="nfl")
markets.get_sport_schedule(sport="nba", date="2025-02-26")
markets.normalize_price(price=0.65, source="polymarket")
markets.evaluate_market(sport="nba", event_id="401234567")

Important Notes

Sport context is passed through. When you specify
```
--sport=nba
```
, the orchestrator maps it to the correct Polymarket sport code and Kalshi series ticker automatically.
Both platforms use sport-aware search. Polymarket uses its
```
/sports
```
config to resolve sport→series_id; Kalshi uses
```
KXNBA
```
,
```
KXNFL
```
, etc.
Prices are normalized. ESPN uses American odds, Polymarket uses 0-1 probability, Kalshi uses 0-100 integer. The orchestrator normalizes everything to implied probability for comparison.

Commands

Command	Required	Optional	Description
`get_todays_markets`		sport, date	Fetch ESPN schedule → search both exchanges with sport context → unified dashboard
`search_entity`	query	sport	Search Kalshi + Polymarket for a team/player/event name (passes sport to both platforms)
`compare_odds`	sport, event_id		ESPN odds + prediction market prices → normalized side-by-side + arb check
`get_sport_markets`	sport	status, limit	Sport-filtered market listing on both platforms (uses sport code, not text query)
`get_sport_schedule`		sport, date	Unified ESPN schedule across one or all sports
`normalize_price`	price, source		Convert any source format to common {implied_prob, american, decimal}
`evaluate_market`	sport, event_id	token_id, kalshi_ticker, outcome	ESPN odds + market price → devig → edge → Kelly

Supported Sports

US Sports (with ESPN schedules)

Sport	Key	Kalshi Series	Polymarket Code
NFL	`nfl`	KXNFL	`nfl`
NBA	`nba`	KXNBA	`nba`
MLB	`mlb`	KXMLB	`mlb`
NHL	`nhl`	KXNHL	`nhl`
WNBA	`wnba`	KXWNBA	`wnba`
College Football	`cfb`	KXCFB	`cfb`
College Basketball	`cbb`	KXCBB	`cbb`

Football (prediction markets only — no ESPN schedule)

League	Key	Kalshi Series	Polymarket Code
English Premier League	`epl`	KXEPLGAME	`epl`
Champions League	`ucl`	KXUCL	`ucl`
La Liga	`laliga`	KXLALIGA	`lal`
Bundesliga	`bundesliga`	KXBUNDESLIGA	`bun`
Serie A	`seriea`	KXSERIEA	`sea`
Ligue 1	`ligue1`	KXLIGUE1	`fl1`
MLS	`mls`	KXMLSGAME	`mls`

Workflows

Today's NBA Dashboard

Show all NBA games today with matching prediction market odds.

bash

sports-skills markets get_todays_markets --sport=nba

Returns each game with:

ESPN game info (teams, time, status)
ESPN DraftKings odds (American format)
Matching Kalshi markets (via
```
search_markets(sport='nba')
```
)
Matching Polymarket markets (via
```
search_markets(sport='nba')
```
)

Find Arb on a Specific Game

Get the ESPN event ID from the schedule:

sports-skills markets get_sport_schedule --sport=nba

Compare odds across all sources:

sports-skills markets compare_odds --sport=nba --event_id=401234567

If arbitrage is detected, the response includes allocation percentages and guaranteed ROI.

Compare ESPN vs Polymarket

sports-skills markets compare_odds --sport=nba --event_id=401234567

Response includes ESPN odds normalized to probability + Polymarket prices side-by-side
Arbitrage check runs automatically using
```
betting.find_arbitrage
```

Full Bet Evaluation

sports-skills markets evaluate_market --sport=nba --event_id=401234567

Fetches ESPN odds, searches for matching prediction market price
Pipes through
```
betting.evaluate_bet
```
: devig → edge → Kelly
Returns fair probability, edge percentage, EV, Kelly fraction, and recommendation

Search for a Team

Find all prediction markets related to a team:

bash

sports-skills markets search_entity --query="Chiefs" --sport=nfl

Returns Kalshi events (via

search_markets(sport='nfl', query='Chiefs')

) and Polymarket markets (via

search_markets(sport='nfl', query='Chiefs')

Price Normalization

Different sources use different formats:

Source	Format	Example	Meaning
ESPN	American odds	`-150`	Favorite, implied 60%
Polymarket	Probability (0-1)	`0.65`	65% implied probability
Kalshi	Integer (0-100)	`65`	65% implied probability

normalize_price

converts any format to a common structure:

json

{
  "implied_probability": 0.65,
  "american": -185.7,
  "decimal": 1.5385,
  "source": "polymarket"
}

Examples

User: "What NBA games are on today and what are the prediction market odds?" →

markets.get_todays_markets(sport="nba")

User: "Find me Lakers markets on Kalshi and Polymarket" →

markets.search_entity(query="Lakers", sport="nba")

User: "Compare the odds for this Celtics game across ESPN and Polymarket" →

markets.compare_odds(sport="nba", event_id="<id>")

User: "Is there edge on the Chiefs game?" →

markets.evaluate_market(sport="nfl", event_id="<id>")

User: "Show me all NFL prediction markets" →

markets.get_sport_markets(sport="nfl")

User: "Convert a Polymarket price of 65 cents to American odds" →

markets.normalize_price(price=0.65, source="polymarket")

Commands that DO NOT exist — never call these

~~get_odds~~
— does not exist. Use
```
compare_odds
```
to see odds across sources, or use the polymarket/kalshi skill directly.
~~search_markets~~
— does not exist on the markets module. Use
```
search_entity
```
instead. For raw market search, use the polymarket or kalshi skill directly.
~~get_schedule~~
— does not exist. Use
```
get_sport_schedule
```
instead.

If a command is not listed in the Commands section above, it does not exist.

Partial Results

If one source is unavailable (e.g., Kalshi is down), the module returns what it has with warnings:

json

{
  "status": true,
  "data": {
    "games": [...],
    "warnings": ["Kalshi search failed: connection timeout"]
  }
}

This ensures you always get usable data even when a source is temporarily unavailable.

markets

NPX Install

Tags

SKILL.md Content

Markets Orchestration

Quick Start

Important Notes

Commands

Supported Sports

US Sports (with ESPN schedules)

Football (prediction markets only — no ESPN schedule)

Workflows

Today's NBA Dashboard

Find Arb on a Specific Game

Compare ESPN vs Polymarket

Full Bet Evaluation

Search for a Team

Price Normalization

Examples

Commands that DO NOT exist — never call these

Partial Results