markets: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

Markets orchestration — connects ESPN live schedules with Kalshi and 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.

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Markets Orchestration

Bridges ESPN live schedules (NBA, NFL, MLB, NHL, WNBA, CFB, CBB) with Kalshi and Polymarket prediction markets. Before writing queries, consult references/api-reference.md for supported sport codes, command parameters, and price normalization formats.

Quick Start

sports-skills markets get_todays_markets --sport=nba
sports-skills markets search_entity --query="Lakers" --sport=nba
sports-skills markets compare_odds --sport=nba --event_id=401234567
sports-skills markets get_sport_markets --sport=nfl
sports-skills markets get_sport_schedule --sport=nba
sports-skills markets normalize_price --price=0.65 --source=polymarket
sports-skills markets evaluate_market --sport=nba --event_id=401234567
sports-skills markets match_markets --sport=mlb --date=2026-06-06
sports-skills markets get_market_price --venue=kalshi --ticker=KXMENWORLDCUP-26-FR
sports-skills markets get_price_history --venue=kalshi --ticker=KXMENWORLDCUP-26-FR --interval=1d

Python SDK:

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")
markets.match_markets(sport="mlb", date="2026-06-06")
markets.get_market_price(venue="kalshi", ticker="KXMENWORLDCUP-26-FR", at_time="2026-05-01T12:00:00+00:00")
markets.get_price_history(venue="polymarket", token_id="<token_id>", interval="1h")

CRITICAL: Before Any Query

CRITICAL: Before calling any orchestration command, verify:

A sport code is provided for sport-aware commands (get_todays_markets, compare_odds, get_sport_markets, evaluate_market).
Price sources are identified correctly before normalization: espn = American odds, polymarket = 0-1 probability, kalshi = 0-100 integer.

Important Notes

Sport context is passed through. --sport=nba maps automatically to the correct Polymarket sport code and Kalshi series ticker.
Both platforms use sport-aware search. Polymarket uses sport → series_id; Kalshi uses KXNBA, KXNFL, etc.
Prices are normalized. Everything is converted to implied probability for comparison.

Workflows

Today's NBA Dashboard

sports-skills markets get_todays_markets --sport=nba

Returns each game with ESPN info, DraftKings odds, matching Kalshi markets, and matching Polymarket markets.

Find Arb on a Specific Game

Get the ESPN event ID: get_sport_schedule --sport=nba
Compare odds: compare_odds --sport=nba --event_id=<id>
If arbitrage detected, response includes allocation percentages and guaranteed ROI.

Full Bet Evaluation

evaluate_market --sport=nba --event_id=<id>
Fetches ESPN odds and matching prediction market price
Pipes through betting.evaluate_bet: devig → edge → Kelly
Returns fair probability, edge, EV, Kelly fraction, and recommendation

Same Game on Both Venues

match_markets --sport=mlb --date=2026-06-06
Each match pairs the Kalshi event (with market tickers) and the Polymarket event (with moneyline token IDs) for the same game — joined deterministically on date + team codes, fuzzy title match as fallback.
Feed kalshi.market_tickers[i] and polymarket.markets[i].token_ids[j] straight into get_market_price to compare prices.

Price Movement Over Time

get_market_price --venue=kalshi --ticker=<ticker> --at_time=2026-05-01 for a single point-in-time price (both yes/no sides, 0-1).
get_price_history --venue=kalshi --ticker=<ticker> --interval=1d for the full series — same {timestamp, price} shape on either venue.

Examples

Example 1: Today's games with prediction market odds User says: "What NBA games are on today and what are the prediction market odds?" Actions:

Call get_todays_markets(sport="nba") Result: Unified dashboard with each game's ESPN info and Kalshi/Polymarket prices

Example 2: Cross-platform team search User says: "Find me Lakers markets on Kalshi and Polymarket" Actions:

Call search_entity(query="Lakers", sport="nba") Result: All Lakers markets across both exchanges with prices and volume

Example 3: Odds comparison for a specific game User says: "Compare the odds for this Celtics game across ESPN and Polymarket" Actions:

Get event_id from get_sport_schedule(sport="nba")
Call compare_odds(sport="nba", event_id="<id>") Result: Normalized side-by-side comparison with automatic arbitrage check

Example 4: Full market evaluation User says: "Is there edge on the Chiefs game?" Actions:

Get event_id from get_sport_schedule(sport="nfl")
Call evaluate_market(sport="nfl", event_id="<id>") Result: Fair probability, edge percentage, EV, Kelly fraction, and bet recommendation

Example 5: Browse all markets for a sport User says: "Show me all NFL prediction markets" Actions:

Call get_sport_markets(sport="nfl") Result: All open NFL markets across Kalshi and Polymarket

Example 6: Price conversion User says: "Convert a Polymarket price of 65 cents to American odds" Actions:

Call normalize_price(price=0.65, source="polymarket") Result: Common structure with implied probability (0.65), American odds (-185.7), and decimal (1.54)

Example 7: Pair a game across venues User says: "Find the Mets game on both Kalshi and Polymarket" Actions:

Call match_markets(sport="mlb", date="<game date>") Result: The game paired across venues — Kalshi market tickers and Polymarket moneyline token IDs side by side

Example 8: Historical price User says: "What was France's World Cup price a month ago?" Actions:

Call get_market_price(venue="kalshi", ticker="KXMENWORLDCUP-26-FR", at_time="2026-05-03T12:00:00+00:00") Result: Yes/no prices (0-1) as of that moment; use get_price_history for the full curve

Commands that DO NOT exist — never call these

~~get_odds~~ — does not exist. Use compare_odds to see odds across sources.
~~search_markets~~ — does not exist on the markets module. Use search_entity instead.
~~get_schedule~~ — does not exist. Use get_sport_schedule instead.

If a command is not listed in references/api-reference.md, it does not exist.

Troubleshooting

Error: No markets returned for a sport Cause: Sport code may be missing or incorrect Solution: Check references/api-reference.md for valid sport codes. Use the exact code (e.g., nba, epl, laliga)

Error: compare_odds returns no data for an event Cause: The event_id is incorrect or the game has not been indexed yet Solution: Call get_sport_schedule(sport=...) to retrieve the correct event_id first

Error: One source shows warnings in the response Cause: Kalshi or Polymarket is temporarily unavailable Solution: The module returns partial results — use what is available. Retry the unavailable source separately using the kalshi or polymarket skill directly

Error: normalize_price returns unexpected American odds value Cause: Wrong source parameter — Kalshi uses 0-100 integers, Polymarket uses 0-1 decimals Solution: Verify the source. Kalshi price of 65 requires source="kalshi", Polymarket price of 0.65 requires source="polymarket"

View source ↗

File metadata

name: markets
description: |
  Markets orchestration — connects ESPN live schedules with Kalshi and 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.
license: MIT
metadata:
  author: machina-sports
  version: "0.3.0"

View original text

---
name: markets
description: |
  Markets orchestration — connects ESPN live schedules with Kalshi and 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.
license: MIT
metadata:
  author: machina-sports
  version: "0.3.0"
---

# Markets Orchestration

Bridges ESPN live schedules (NBA, NFL, MLB, NHL, WNBA, CFB, CBB) with Kalshi and Polymarket prediction markets. Before writing queries, consult `references/api-reference.md` for supported sport codes, command parameters, and price normalization formats.

## Quick Start

```bash
sports-skills markets get_todays_markets --sport=nba
sports-skills markets search_entity --query="Lakers" --sport=nba
sports-skills markets compare_odds --sport=nba --event_id=401234567
sports-skills markets get_sport_markets --sport=nfl
sports-skills markets get_sport_schedule --sport=nba
sports-skills markets normalize_price --price=0.65 --source=polymarket
sports-skills markets evaluate_market --sport=nba --event_id=401234567
sports-skills markets match_markets --sport=mlb --date=2026-06-06
sports-skills markets get_market_price --venue=kalshi --ticker=KXMENWORLDCUP-26-FR
sports-skills markets get_price_history --venue=kalshi --ticker=KXMENWORLDCUP-26-FR --interval=1d
```

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")
markets.match_markets(sport="mlb", date="2026-06-06")
markets.get_market_price(venue="kalshi", ticker="KXMENWORLDCUP-26-FR", at_time="2026-05-01T12:00:00+00:00")
markets.get_price_history(venue="polymarket", token_id="<token_id>", interval="1h")
```

## CRITICAL: Before Any Query

CRITICAL: Before calling any orchestration command, verify:
- A `sport` code is provided for sport-aware commands (`get_todays_markets`, `compare_odds`, `get_sport_markets`, `evaluate_market`).
- Price sources are identified correctly before normalization: `espn` = American odds, `polymarket` = 0-1 probability, `kalshi` = 0-100 integer.

## Important Notes

- **Sport context is passed through.** `--sport=nba` maps automatically to the correct Polymarket sport code and Kalshi series ticker.
- **Both platforms use sport-aware search.** Polymarket uses `sport` → series_id; Kalshi uses `KXNBA`, `KXNFL`, etc.
- **Prices are normalized.** Everything is converted to implied probability for comparison.

## Workflows

### Today's NBA Dashboard

```bash
sports-skills markets get_todays_markets --sport=nba
```
Returns each game with ESPN info, DraftKings odds, matching Kalshi markets, and matching Polymarket markets.

### Find Arb on a Specific Game

1. Get the ESPN event ID: `get_sport_schedule --sport=nba`
2. Compare odds: `compare_odds --sport=nba --event_id=<id>`
3. If arbitrage detected, response includes allocation percentages and guaranteed ROI.

### Full Bet Evaluation

1. `evaluate_market --sport=nba --event_id=<id>`
2. Fetches ESPN odds and matching prediction market price
3. Pipes through `betting.evaluate_bet`: devig → edge → Kelly
4. Returns fair probability, edge, EV, Kelly fraction, and recommendation

### Same Game on Both Venues

1. `match_markets --sport=mlb --date=2026-06-06`
2. Each match pairs the Kalshi event (with market tickers) and the Polymarket event (with moneyline token IDs) for the same game — joined deterministically on date + team codes, fuzzy title match as fallback.
3. Feed `kalshi.market_tickers[i]` and `polymarket.markets[i].token_ids[j]` straight into `get_market_price` to compare prices.

### Price Movement Over Time

1. `get_market_price --venue=kalshi --ticker=<ticker> --at_time=2026-05-01` for a single point-in-time price (both `yes`/`no` sides, 0-1).
2. `get_price_history --venue=kalshi --ticker=<ticker> --interval=1d` for the full series — same `{timestamp, price}` shape on either venue.

## Examples

Example 1: Today's games with prediction market odds
User says: "What NBA games are on today and what are the prediction market odds?"
Actions:
1. Call `get_todays_markets(sport="nba")`
Result: Unified dashboard with each game's ESPN info and Kalshi/Polymarket prices

Example 2: Cross-platform team search
User says: "Find me Lakers markets on Kalshi and Polymarket"
Actions:
1. Call `search_entity(query="Lakers", sport="nba")`
Result: All Lakers markets across both exchanges with prices and volume

Example 3: Odds comparison for a specific game
User says: "Compare the odds for this Celtics game across ESPN and Polymarket"
Actions:
1. Get event_id from `get_sport_schedule(sport="nba")`
2. Call `compare_odds(sport="nba", event_id="<id>")`
Result: Normalized side-by-side comparison with automatic arbitrage check

Example 4: Full market evaluation
User says: "Is there edge on the Chiefs game?"
Actions:
1. Get event_id from `get_sport_schedule(sport="nfl")`
2. Call `evaluate_market(sport="nfl", event_id="<id>")`
Result: Fair probability, edge percentage, EV, Kelly fraction, and bet recommendation

Example 5: Browse all markets for a sport
User says: "Show me all NFL prediction markets"
Actions:
1. Call `get_sport_markets(sport="nfl")`
Result: All open NFL markets across Kalshi and Polymarket

Example 6: Price conversion
User says: "Convert a Polymarket price of 65 cents to American odds"
Actions:
1. Call `normalize_price(price=0.65, source="polymarket")`
Result: Common structure with implied probability (0.65), American odds (-185.7), and decimal (1.54)

Example 7: Pair a game across venues
User says: "Find the Mets game on both Kalshi and Polymarket"
Actions:
1. Call `match_markets(sport="mlb", date="<game date>")`
Result: The game paired across venues — Kalshi market tickers and Polymarket moneyline token IDs side by side

Example 8: Historical price
User says: "What was France's World Cup price a month ago?"
Actions:
1. Call `get_market_price(venue="kalshi", ticker="KXMENWORLDCUP-26-FR", at_time="2026-05-03T12:00:00+00:00")`
Result: Yes/no prices (0-1) as of that moment; use `get_price_history` for the full curve

## Commands that DO NOT exist — never call these

- ~~`get_odds`~~ — does not exist. Use `compare_odds` to see odds across sources.
- ~~`search_markets`~~ — does not exist on the markets module. Use `search_entity` instead.
- ~~`get_schedule`~~ — does not exist. Use `get_sport_schedule` instead.

If a command is not listed in `references/api-reference.md`, it does not exist.

## Troubleshooting

Error: No markets returned for a sport
Cause: Sport code may be missing or incorrect
Solution: Check `references/api-reference.md` for valid sport codes. Use the exact code (e.g., `nba`, `epl`, `laliga`)

Error: `compare_odds` returns no data for an event
Cause: The event_id is incorrect or the game has not been indexed yet
Solution: Call `get_sport_schedule(sport=...)` to retrieve the correct event_id first

Error: One source shows warnings in the response
Cause: Kalshi or Polymarket is temporarily unavailable
Solution: The module returns partial results — use what is available. Retry the unavailable source separately using the kalshi or polymarket skill directly

Error: `normalize_price` returns unexpected American odds value
Cause: Wrong `source` parameter — Kalshi uses 0-100 integers, Polymarket uses 0-1 decimals
Solution: Verify the source. Kalshi price of 65 requires `source="kalshi"`, Polymarket price of 0.65 requires `source="polymarket"`

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License: MIT
Version: 1.0.0
Last GitHub push: Sep 1, 2026
Registry updated: Sep 3, 2026
Instruction path: skills/markets/SKILL.md @ 24131b3ace46

Version reported in registry metadata; check source releases before relying on it.

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      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 211 stars, 30 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 79,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 211 stars, 30 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 70,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "7d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "mvanhorn-last30days-skill",
      "name": "Last30days Skill",
      "url": "https://www.openagentskill.com/skills/mvanhorn-last30days-skill",
      "stars": 60956,
      "install_command": "",
      "trust_score": 94,
      "audit_score": 95
    },
    {
      "slug": "yanliudesign-mono-color-skill",
      "name": "mono-color",
      "url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
      "stars": 1919,
      "install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
      "trust_score": 85,
      "audit_score": 93
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Financial research output is not financial advice; require human review before any live investment decision."
  ],
  "agent_contract": {
    "task_input": "Use markets in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 74/100 Strong shortlist",
      "Audit: 79/100 Needs review",
      "Safety: 39/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "machina-sports-markets (markets)",
      "install_command": "npx skills add machina-sports/sports-skills --skill markets",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "machina-sports-markets",
      "task": "Use markets in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/machina-sports-markets",
    "api": "https://www.openagentskill.com/api/agent/skills/machina-sports-markets",
    "audit": "https://www.openagentskill.com/skills/machina-sports-markets/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=machina-sports-markets&task=Use%20markets%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20markets%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20markets%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/machina-sports-markets/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/machina-sports-markets"
  }
}

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Creator: machina-sports
Source: machina-sports/sports-skills
Indexed by: OpenAgentSkill community index

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