← 返回 Skills 市场
rsquaredsolutions2026

Bet Slip Parser

作者 rsquaredsolutions2026 · GitHub ↗ · v1.1.0 · MIT-0
cross-platform ⚠ suspicious
113
总下载
0
收藏
0
当前安装
2
版本数
在 OpenClaw 中安装
/install bet-slip-parser
功能描述
Parse bet slips from text, natural language, or screenshots into structured JSON. Extracts stake, odds, bet type, selection, and sportsbook. Supports singles...
使用说明 (SKILL.md)

Bet Slip Parser

Extract structured betting data from any bet slip format — pasted text, natural language, or screenshots.

When to Use

Use this skill when the user:

  • Pastes a bet confirmation or bet slip text
  • Describes a bet they placed in natural language
  • Shares a screenshot of a bet slip from any sportsbook
  • Asks to "log this bet" or "parse this bet slip"
  • Wants to convert a bet description into structured data
  • Says "I just bet..." or "I placed a bet on..."

Supported Bet Types

Bet Type Keywords
Moneyline moneyline, ML, to win, h2h
Spread spread, point spread, handicap, ATS, -3.5
Total total, over, under, O/U
Parlay parlay, accumulator, multi, combo
Teaser teaser, adjusted spread
Same-Game Parlay SGP, same game, same-game
Prop prop, player prop, over 24.5 pts
Future future, outright, to win championship

Supported Sportsbooks

Identify the sportsbook from branding, URLs, or mentions:

Sportsbook Identifiers
DraftKings draftkings, dk, DK Sportsbook
FanDuel fanduel, fd, FD Sportsbook
BetMGM betmgm, mgm, BetMGM Sportsbook
Caesars caesars, czr, Caesars Sportsbook
Pinnacle pinnacle, pin
Bet365 bet365, b365
BetRivers betrivers, rivers
PointsBet pointsbet
Polymarket polymarket, poly
Kalshi kalshi

If the sportsbook cannot be identified, set source to "unknown".

Operations

1. Parse Text Bet Slip

When the user pastes sportsbook confirmation text, extract all fields and output JSON.

Extraction steps:

  1. Identify sportsbook from text branding or URL
  2. Identify bet type from keywords (see table above)
  3. Extract team/player/event selections
  4. Extract odds — convert to American if decimal or fractional
  5. Extract stake amount and currency
  6. Calculate potential payout if not shown
  7. Extract timestamp if available

Odds conversion formulas (use inline when needed):

# Decimal to American
echo "$DECIMAL_ODDS" | python3 -c "
import sys
d = float(sys.stdin.read().strip())
if d >= 2.0:
    print(f'+{round((d - 1) * 100)}')
else:
    print(f'{round(-100 / (d - 1))}')"

# Fractional (e.g. 3/2) to American
echo "3/2" | python3 -c "
import sys
n, d = map(int, sys.stdin.read().strip().split('/'))
dec = (n / d) + 1
if dec >= 2.0:
    print(f'+{round((dec - 1) * 100)}')
else:
    print(f'{round(-100 / (dec - 1))}')"

# American to implied probability
echo "-110" | python3 -c "
import sys
odds = int(sys.stdin.read().strip())
if odds \x3C 0:
    prob = abs(odds) / (abs(odds) + 100)
else:
    prob = 100 / (odds + 100)
print(f'{prob:.4f}')"

# American to decimal
echo "-110" | python3 -c "
import sys
odds = int(sys.stdin.read().strip())
if odds \x3C 0:
    dec = 1 + (100 / abs(odds))
else:
    dec = 1 + (odds / 100)
print(f'{dec:.3f}')"

2. Parse Natural Language Bet

When the user describes a bet casually, extract the same fields.

Examples of natural language inputs and how to parse them:

  • "I bet $100 on the Lakers ML at +150 on DraftKings" → source: draftkings, bet_type: moneyline, selection: Los Angeles Lakers, odds: +150, stake: 100
  • "Put $25 on over 48.5 in Chiefs-Ravens at -110" → bet_type: total, selection: Over 48.5, odds: -110, stake: 25
  • "Parlayed Lakers ML and Chiefs -3.5, $50 to win $320 on FanDuel" → bet_type: parlay, 2 legs, stake: 50, potential_payout: 320, source: fanduel

Always ask for missing critical fields:

  • If no stake mentioned, ask "How much did you bet?"
  • If no odds mentioned, ask "What were the odds?"
  • If no sportsbook mentioned, set source to "unknown"

3. Parse Screenshot Bet Slip

When the user shares an image of a bet slip:

  1. Identify sportsbook from app branding/colors (DraftKings=green/black, FanDuel=blue/white, BetMGM=gold/black, Caesars=dark blue)
  2. Read all visible text on the bet slip
  3. Extract: event name, bet type, selection, odds, stake, potential payout
  4. For parlays, extract each individual leg
  5. Output in the standard JSON schema

Note: Screenshot parsing requires the LLM to have vision capabilities. If vision is not available, ask the user to paste the bet slip as text instead.

4. Validate Parsed Output

After parsing, validate the extracted data:

# Validate American odds format (must be + or - followed by digits)
echo "$ODDS" | grep -qE '^[+-][0-9]+$' && echo "VALID" || echo "INVALID: odds must be +NNN or -NNN"

# Validate stake is positive number
echo "$STAKE" | grep -qE '^[0-9]+(\.[0-9]{1,2})?$' && echo "VALID" || echo "INVALID: stake must be positive number"

# Validate bet type
echo "$BET_TYPE" | grep -qE '^(moneyline|spread|total|parlay|teaser|sgp|prop|future)$' && echo "VALID" || echo "INVALID: unknown bet type"

# Cross-check: potential payout should match odds × stake
echo "$STAKE $ODDS" | python3 -c "
import sys
parts = sys.stdin.read().strip().split()
stake, odds = float(parts[0]), int(parts[1])
if odds > 0:
    payout = stake + (stake * odds / 100)
else:
    payout = stake + (stake * 100 / abs(odds))
print(f'Expected payout: {payout:.2f}')"

Output Rules

  1. Always output a valid JSON object matching the schema
  2. All odds must include American format (convert if needed)
  3. Include decimal and implied probability for each leg
  4. If a field cannot be extracted, set it to null — never guess
  5. For parlays, each leg must be a separate entry in the legs array
  6. Always include the raw_input field with the original text or "screenshot"
  7. Potential payout must be calculated if not explicitly stated
  8. Timestamp should be ISO 8601 format; use current time if not on the slip

Error Handling

  • If the input is ambiguous and multiple interpretations exist, present options and ask the user to clarify
  • If the image is too blurry or cropped, ask for a clearer screenshot or text input
  • If odds format is unrecognized, ask the user to specify (American, decimal, or fractional)
  • If the bet type cannot be determined, default to "moneyline" for single selection and "parlay" for multiple selections
  • Always show the parsed JSON and ask "Does this look right?" before passing to downstream skills

About

Built by AgentBets — full tutorial at agentbets.ai/guides/openclaw-bet-slip-parser-skill/.

Part of the OpenClaw Skills series for the Agent Betting Stack.

安全使用建议
This skill's purpose (extracting structured bet data) is reasonable and it doesn't ask for credentials, but there are mismatches you should resolve before trusting it: - The SKILL.md uses python3 inline and expects OCR/vision for screenshots, yet the declared required binaries only list "bash" and there is no mention of python3, tesseract, or any OCR library. Ask the author to explicitly declare required binaries (e.g., python3, tesseract/pytesseract or an image-to-text API) or update the skill metadata. - The skill runs shell and python snippets. Even though the shown snippets are benign conversions, executing arbitrary shell/python carries risk if the skill is changed later. Only enable/run this skill in an environment you control, or require code review before use. - The SKILL.md appears truncated (timestamp rule cut off). Request the complete instructions to ensure there are no hidden behaviors. - For screenshots, confirm how OCR is implemented and whether images are sent to any external service (privacy risk). If OCR uses a third‑party API, you should know which endpoint and whether credentials or user data will be transmitted. If you plan to install/run this skill: 1) ensure python3 and an OCR tool are installed and declared; 2) test with non-sensitive sample data; 3) prefer running in an isolated environment; and 4) ask the publisher for the full SKILL.md and explicit dependency list. If the author cannot supply those, treat the skill as untrusted.
功能分析
Type: OpenClaw Skill Name: bet-slip-parser Version: 1.1.0 The bet-slip-parser skill is a utility designed to extract and structure sports betting data from text or images. It uses bash and python scripts for mathematical odds conversion and includes input validation steps (e.g., regex checks in SKILL.md) to ensure data integrity. No evidence of malicious intent, data exfiltration, or unauthorized execution was found; the logic is transparent and strictly aligned with the stated purpose.
能力评估
Purpose & Capability
Name and description match the instructions: the skill only parses bet-slip text, natural language, and screenshots into structured JSON. It does not request unrelated credentials or system access, and supported sportsbooks/bet types are coherent with the stated purpose.
Instruction Scope
The SKILL.md contains many inline shell/python code snippets and instructs the agent to parse screenshots (requires OCR/vision). However the instructions do not declare or explain how OCR/text-extraction will be performed or what OCR tool/library to use. Also the doc repeatedly uses python3 inline, but python3 is not listed as a required binary. The SKILL.md appears truncated mid-sentence (timestamp rule), reducing clarity about final output behavior.
Install Mechanism
This is instruction-only (no install step), which minimizes disk-writing risk. However, because it expects to run shell snippets (echo | python3) and read images, a runtime environment must provide python3 and some OCR/vision capability; those dependencies are not declared. That omission is a practical coherence issue and a security surface (the agent may execute arbitrary shell/python commands if invoked).
Credentials
The skill declares no environment variables, no credentials, and no config paths — appropriate for a parser that only processes user-provided bet slips. There is no request for unrelated secrets or cloud credentials.
Persistence & Privilege
The skill is not always-enabled, does not request persistence, and allows model invocation (normal default). There is no evidence it attempts to modify other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install bet-slip-parser
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /bet-slip-parser 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.1.0
Add attribution links to agentbets.ai guides
v1.0.0
Initial release — AgentBets OpenClaw Skills series
元数据
Slug bet-slip-parser
版本 1.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

Bet Slip Parser 是什么?

Parse bet slips from text, natural language, or screenshots into structured JSON. Extracts stake, odds, bet type, selection, and sportsbook. Supports singles... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 113 次。

如何安装 Bet Slip Parser?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install bet-slip-parser」即可一键安装,无需额外配置。

Bet Slip Parser 是免费的吗?

是的,Bet Slip Parser 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Bet Slip Parser 支持哪些平台?

Bet Slip Parser 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Bet Slip Parser?

由 rsquaredsolutions2026(@rsquaredsolutions2026)开发并维护,当前版本 v1.1.0。

💬 留言讨论