GPTSportswriter#

Overview#

Use this skill to generate structured sports betting research reports. It can operate in:

• premium mode with API-backed odds and news context
• free mode with public-source workflows and extraction helpers

The goal is to surface current matchups, betting angles, and relevant context in a clean format without pretending certainty.

Data Priority#

The skill supports three modes:

• auto (default): use paid sources when configured, otherwise fall back
• premium: require paid sources
• free: use only free/public sources

1. Primary source in premium/auto mode: The Odds API
   • Use it first for:
     • today's matchups
     • moneylines
     • spreads
     • totals
     • sportsbook availability
   • Treat this as the source of truth for what games and lines actually exist right now.
2. Secondary source in premium/auto mode: AskNews + web search
   • Use AskNews first for:
     • recent sports articles
     • matchup previews
     • injury/news context
     • broader same-day coverage summaries
   • Use web search as a fallback or supplement for:
     • betting previews
     • source consensus and disagreement
     • extra matchup-specific context
   • Do not let article coverage override current market reality when the odds data says otherwise.
3. Free mode fallback
   • Use public odds/news pages when the user does not want paid services or when no API keys are configured.
   • Prefer public pages from sources like:
     • Covers
     • OddsShark
     • Yahoo Sports
     • AP
     • ESPN
     • CBS Sports
   • Be explicit that free-mode odds can be more stale and less precise.

Workflow#

1. Identify the slate.
   • If the user says "today," use today's games only.
   • If the user names a league, focus there.
   • If the request is broad, cover major in-season sports.
   • For Premier League soccer, use the Odds API sport key soccer_epl.
2. Pull current odds and matchups first.
   • Use The Odds API to get the current slate.
   • Prefer commonly used U.S. books when comparing prices.
   • Note where prices differ meaningfully by book.
3. Search for context.
   • First use scripts/fetch_asknews.py for article context.
   • Then, if needed, search the web for combinations like:
     • best bets today [sport]
     • [league] picks today odds
     • [matchup] betting preview
     • [team] injuries betting line
   • Prefer same-day results.
   • Pull multiple sources before summarizing.
4. Cross-check before recommending anything.
   • Look for overlap between current odds and web consensus.
   • Separate consensus picks from one-off hot takes.
   • Note line sensitivity explicitly.
   • If a good angle depends on getting a better number at a specific book, say that.
5. Write the summary.
   • Include the event, market, current line range, and the reasoning in one or two lines.
   • Mention uncertainty, injuries, schedule spots, or stale-preview risk when relevant.
   • Keep it readable and useful, not breathless.

Required Structured Output#

Unless the user explicitly asks for a different format, use this exact output shape:

GPTSportswriter — Best Betting Advice of the Day Date: [today] Scope: Broad daily slate across in-season sports

Top Looks Today

1. [Event or matchup]
   • Market: [spread / moneyline / total / prop]
   • Lean: [the recommended side or angle]
   • Current odds: [best current range found]
   • Why this play: [1-2 short sentences]
   • Confidence: Low / Medium / High
2. [Event or matchup]
   • Market: [spread / moneyline / total / prop]
   • Lean: [the recommended side or angle]
   • Current odds: [best current range found]
   • Why this play: [1-2 short sentences]
   • Confidence: Low / Medium / High
3. [Event or matchup]
   • Market: [spread / moneyline / total / prop]
   • Lean: [the recommended side or angle]
   • Current odds: [best current range found]
   • Why this play: [1-2 short sentences]
   • Confidence: Low / Medium / High

Consensus Signals

• [short bullet on where multiple sources overlap]
• [short bullet on a repeated theme or angle]

Watch-Outs

• [injury/news uncertainty]
• [line movement or stale odds risk]
• [source disagreement or thin support]

Bottom Line

• [one short paragraph with the overall takeaway]

Fallback Rules#

• If The Odds API is unavailable, say that plainly and fall back to web-only summaries.
• If you only find one or two credible angles, do not force a third pick.
• If sources are weak or old, say that clearly in Bottom Line.
• If the user asks for only one best bet, still include:
  • Market
  • Lean
  • Current odds
  • Why this play
  • Main caveat
  • Confidence

Standards#

• Do not claim guaranteed wins.
• Do not fabricate odds, records, injuries, or sportsbook prices.
• Prefer "sources are leaning" over "this will hit."
• If the market has already moved away from the good number, say so.
• If the web results are thin or conflicting, say so plainly.

The Odds API Notes#

• Use the configured THE_ODDS_API_KEY from environment.
• Prefer current pregame markets where available.
• Compare multiple bookmakers before quoting a range.
• Be explicit when a price is widely available versus only available at one book.
• Use scripts/fetch_odds.py to fetch and normalize live odds before writing the summary.

Script usage#

Fetch normalized odds:

bash

python3 scripts/fetch_odds.py --mode auto --sports baseball_mlb basketball_nba --pretty

Generate an automatic ranked report:

bash

python3 scripts/generate_report.py --mode auto --sports baseball_mlb basketball_nba

Generate a deeper report with extra context:

bash

python3 scripts/generate_report.py --mode auto --sports baseball_mlb --detail deep

MLB deep mode currently adds:

• weather context
• article-based key players / injuries / trend context
• extra matchup summary for top picks

Force free mode:

bash

python3 scripts/generate_report.py --mode free --sports baseball_mlb

Get free-source hints directly:

bash

python3 scripts/fetch_free_context.py --sports baseball_mlb basketball_nba

Get free-mode odds-search queries:

bash

python3 scripts/fetch_free_odds.py --sports baseball_mlb basketball_nba

Parse rough public-odds text into event candidates:

bash

python3 scripts/parse_free_odds.py sample.txt

Public-page fetch pipeline:

bash

python3 scripts/free_pipeline.py --sport baseball_mlb

Targeted Covers MLB event extraction:

bash

python3 scripts/extract_covers_mlb.py page.html

Targeted Covers MLB line snapshot extraction:

bash

python3 scripts/extract_covers_mlb_lines.py page.html

Free-mode MLB prototype report:

bash

python3 scripts/generate_report.py --mode free --sports baseball_mlb

Fetch AskNews article context:

bash

python3 scripts/fetch_asknews.py 'Knicks Hornets betting preview March 26 2026' --n-articles 5

Send the report by email:

bash

bash scripts/send_daily_report.sh

Default behavior:

• reads THE_ODDS_API_KEY from environment

• queries MLB, NBA, and NHL by default

• compares FanDuel, DraftKings, and BetMGM

• fetch_odds.py returns normalized game objects with:

  • best available moneyline by team
  • spread ranges by team
  • total ranges for Over and Under
  • per-book market details

• generate_report.py builds:

  • best-price ranking
  • automatic per-game candidate selection
  • automatic top-3 formatted report output

Useful search habits#

• Use freshness filters when available.
• Search by sport + date + best bets.
• Search matchup previews separately when a pick looks interesting.
• If injury news may matter, search that team and player status directly before finalizing.