Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Productivity
football-value-bets
Professional football bet analysis skill. Generates data-driven bet slips based on form, H2H, standings, injuries and value analysis. Includes result trackin...
skill openclawclawhub Free
Professional football bet analysis skill. Generates data-driven bet slips based on form, H2H, standings, injuries and value analysis. Includes result trackin...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- config/settings.json, scripts/match_fetcher.py, scripts/bet_tracker.py, SKILL.md, skill.json
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- nandichi
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Football Value Bets
Generate professional, data-driven football bet slips. No gut feelings, purely statistics and value analysis.
When to activate
Activate this skill when the user: Asks for a football bet, bet slip, accumulator, or picks Asks for match analysis for betting purposes Asks for bet statistics, results, or ROI Asks for a pick or tip for football
Prerequisites
Install dependencies (one-time): pip install requests Set API key (free via football-data.org/client/register): export FOOTBALL_DATA_API_KEY="your-key-here" Or fill in the key at {baseDir}/config/settings.json under api_key.
Step 1 -- Fetch data
Fetch current match data: python3 {baseDir}/scripts/match_fetcher.py --mode full This returns a JSON package with: All scheduled matches for today and tomorrow League standings and form data per team Head-to-head history (last 5 encounters) Coverage: Premier League, Bundesliga, Serie A, La Liga, Ligue 1, Eredivisie, Champions League. If the user wants a specific date: python3 {baseDir}/scripts/match_fetcher.py --mode full --date-from 2026-02-15 --date-to 2026-02-15
Step 2 -- Analyse each match
Analyse EVERY match from the data on these factors (in order of importance): A. Form (last 5-10 matches) Use the form field from the standings data (e.g. "W,W,D,L,W") Calculate win percentage, goals per match Pay attention to home form vs away form trends B. Home/away statistics Home team: goals for/against at home Away team: goals for/against away Compare goal difference at home vs on the road C. Head-to-head (H2H) Review the h2h data: who wins historically? Look for patterns: does one team always score? Are these low- or high-scoring games? D. Table position and motivation Top of the table (title/CL spots): high motivation, consistent Mid-table: unpredictable, be cautious Bottom (relegation): can be surprisingly strong or weak Difference in table position as an indicator E. Additional factors (via web search) After the data analysis, search the web for EVERY shortlisted match: Injuries and suspensions of key players (FotMob, Transfermarkt) Recent news (manager change, internal issues, transfer saga) xG (expected goals) data from recent matches Weather conditions for outdoor matches in winter months Use search terms like: "[Team A] vs [Team B] team news injuries" and "[Team A] xG stats 2025-26" F. Odds estimation Estimate realistic odds per outcome (based on your analysis) Compare with common bookmaker odds (search the web if needed) VALUE = when your estimated probability is higher than the implied probability of the odds Implied probability = 1 / odds (e.g. odds 2.00 = 50% implied)
Step 3 -- Pick selection
Select ONLY picks that meet all criteria: Estimated win probability > 45% Odds per pick: 1.70 - 2.50 (value range) Clear motivation based on data No more than 1 pick per match Allowed pick types: 1 / X / 2: Home win, draw, away win 1X / X2: Double chance BTTS Yes/No: Both Teams To Score Over/Under 2.5 goals Handicap -1: Team wins by 2+ goals
Step 4 -- Honesty check
If you find FEWER than 3 picks with real value: Do NOT generate a bet slip. Say honestly: "No strong bet slip possible today. The matches offer insufficient value based on the data. Reasons: [brief explanation]. Wait for better odds or the next matchday." This is MANDATORY. Never force it. Honesty > revenue.
Step 5 -- Generate output
Generate TWO variants: Variant A: Main slip (3-4 picks, total odds 8x-15x) MatchPickOddsMotivationProb %RiskAjax - PSVBTTS Yes1.85Both teams scored in 8/10 recent matches, H2H always goals52%LowArsenal - ChelseaOver 2.51.90Avg 3.2 goals per match this season, both attack-minded48%Medium.................. Total odds: XX.XX Recommended stake: 5-10 EUR Potential payout: XX.XX EUR Variant B: Backup (2-3 picks, total odds 4x-8x) Same table format but with safer picks (higher probabilities, lower odds). Always include at the bottom: Estimated hitrate per variant (e.g. "Main slip: ~15% chance of all picks hitting, individual picks ~50% average") Alternative advice: "Consider singles if you want to play it safer" Disclaimer: "Gamble responsibly. Max stake 5-10 EUR per slip, monthly limit 75 EUR."
Step 6 -- Save bet
Automatically save the bet slip for tracking: python3 {baseDir}/scripts/bet_tracker.py --mode save --data '{ "type": "main", "stake": 5.0, "picks": [ { "match": "Ajax - PSV", "competition": "Eredivisie", "pick": "BTTS Yes", "odds": 1.85, "estimated_probability": 0.52, "motivation": "Both teams scored in 8/10 recent matches", "risk": "low" } ], "total_odds": 12.50, "notes": "" }' Repeat for the backup slip with "type": "backup".
Mode 2: Record results
When the user says a bet has won or lost: # Mark entire slip python3 {baseDir}/scripts/bet_tracker.py --mode result --date 2026-02-15 --slip-idx 1 --result win # Mark specific pick python3 {baseDir}/scripts/bet_tracker.py --mode result --date 2026-02-15 --slip-idx 1 --pick-idx 2 --result loss Result options: win, loss, void Always ask the user for the date and which slip (main or backup) it concerns.
Mode 3: View statistics
When the user asks about results, stats, ROI, or hitrate: python3 {baseDir}/scripts/bet_tracker.py --mode stats Present the JSON output as a readable summary: All-time hitrate and ROI Current month: staked, returned, profit/loss, budget remaining Best and worst month Warn if the monthly budget is almost spent (>80% used).
Mode 4: View history
python3 {baseDir}/scripts/bet_tracker.py --mode history --days 30 Show an overview of all bets from the last N days.
Analysis quality rules
These rules are HARD. Always follow them: Never more than 5 picks per slip. More picks = exponentially lower chance. Never odds below 1.50 or above 3.00 per pick. Too low = no value, too high = too risky. Never 2 picks from the same match. Correlation destroys value. Never a pick without data backing. "Gut feeling" is not an argument. Always run the honesty check. No slip is better than a bad slip. Avoid derbies and cup matches unless the data is overwhelming. Too unpredictable. Always check injuries. A team without key players is a different team. Factor in the season phase. Early season = unreliable data. End of season = varying motivation.
Budget and responsible gambling
Configuration is in {baseDir}/config/settings.json: Per bet: 5-10 EUR Monthly limit: 75 EUR Always check the stats for monthly spending before generating a new slip If the monthly budget is at or nearly at its limit (>90%): Inform the user Advise waiting until next month Do NOT generate a new slip unless the user explicitly states they want to proceed
Tone and style
Output in English Professional but accessible No hype, no promises Data and percentages front and center Honest about risks
Category context
Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Scripts2 Config1 Docs
- SKILL.md
- scripts/bet_tracker.py
- scripts/match_fetcher.py
- config/settings.json
- skill.json