Skill: rsquaredsolutions2026/agentbets-reputation-tracker (version 1.1.0)

--- name: agent-reputation-tracker description: "Track and display your agent's betting reputation. Computes win rate, ROI, volume, streaks, max drawdown, and Sharpe proxy from local bet history. Formats output for Moltbook profiles or plaintext reputation cards. Use when asked about agent stats, performance, win rate, ROI, reputation, or track record." metadata: openclaw: emoji: "🏆" requires: bins: ["python3", "sqlite3"] credentials: - id: "moltbook-api-key" name: "Moltbook API Key" description: "Optional — API key from https://moltbook.com for publishing agent profiles" env: "MOLTBOOK_API_KEY" --- # Agent Reputation Tracker Compute and display your betting agent's performance metrics from local bet history. ## When to Use Use this skill when the user asks about: - Agent stats, performance, or track record - Win rate, ROI, or profit/loss summary - Current winning or losing streak - Rolling performance (last 7 days, 30 days, etc.) - Publishing or updating a Moltbook profile - Generating a reputation card to share - Comparing agent performance across time periods - Max drawdown or risk-adjusted performance ## Database Schema The skill expects a SQLite database at `~/.openclaw/data/bet_log.db` with this schema: ```sql CREATE TABLE IF NOT EXISTS bets ( id INTEGER PRIMARY KEY AUTOINCREMENT, timestamp TEXT NOT NULL, -- ISO 8601 format platform TEXT NOT NULL, -- e.g., 'draftkings', 'polymarket', 'kalshi' event TEXT, -- event description selection TEXT, -- what was bet on bet_type TEXT DEFAULT 'moneyline', -- moneyline, spread, total, prop, binary odds REAL NOT NULL, -- American odds for sportsbooks, decimal for prediction markets odds_format TEXT DEFAULT 'american', -- 'american' or 'decimal' stake REAL NOT NULL, -- amount wagered result TEXT, -- 'win', 'loss', 'push', 'pending' payout REAL DEFAULT 0, -- amount returned (including stake if won) notes TEXT ); ``` If the database doesn't exist, offer to create it with the schema above. ## Operations ### 1. Lifetime Stats Compute overall performance across all resolved bets: ```bash python3 -c " import sqlite3, json, math, os db = os.path.expanduser('~/.openclaw/data/bet_log.db') conn = sqlite3.connect(db) c = conn.cursor() c.execute(\"SELECT stake, payout, result FROM bets WHERE result IN ('win','loss')\") rows = c.fetchall() if not rows: print(json.dumps({'error': 'No resolved bets found'})) else: wins = sum(1 for r in rows if r[2]=='win') losses = sum(1 for r in rows if r[2]=='loss') total_staked = sum(r[0] for r in rows) total_payout = sum(r[1] for r in rows) profit = total_payout - total_staked roi = (profit / total_staked * 100) if total_staked > 0 else 0 returns = [(r[1]-r[0])/r[0] for r in rows if r[0]>0] avg_ret = sum(returns)/len(returns) if returns else 0 std_ret = (sum((x-avg_ret)**2 for x in returns)/len(returns))**0.5 if len(returns)>1 else 0 sharpe = avg_ret/std_ret if std_ret>0 else 0 # Max drawdown cumulative = 0; peak = 0; max_dd = 0 for r in rows: cumulative += r[1] - r[0] if cumulative > peak: peak = cumulative dd = peak - cumulative if dd > max_dd: max_dd = dd # Streaks streak = 0; max_w = 0; max_l = 0; cur = 0; cur_type = '' for r in rows: if r[2] == cur_type: cur += 1 else: cur_type = r[2]; cur = 1 if cur_type == 'win' and cur > max_w: max_w = cur if cur_type == 'loss' and cur > max_l: max_l = cur print(json.dumps({ 'total_bets': len(rows), 'wins': wins, 'losses': losses, 'win_rate': round(wins/(wins+losses)*100, 1), 'total_staked': round(total_staked, 2), 'total_payout': round(total_payout, 2), 'profit': round(profit, 2), 'roi_pct': round(roi, 1), 'sharpe_proxy': round(sharpe, 3), 'max_drawdown': round(max_dd, 2), 'longest_win_streak': max_w, 'longest_loss_streak': max_l, 'current_streak': f'{cur} {cur_type}s' if cur_type else 'N/A' }, indent=2)) conn.close() " ``` ### 2. Rolling Window Stats Compute stats for a recent time period. Replace DAYS with the window (7, 30, 90): ```bash python3 -c " import sqlite3, json, os from datetime import datetime, timedelta db = os.path.expanduser('~/.openclaw/data/bet_log.db') days = DAYS conn = sqlite3.connect(db) c = conn.cursor() cutoff = (datetime.utcnow() - timedelta(days=days)).isoformat() c.execute(\"SELECT stake, payout, result, platform FROM bets WHERE result IN ('win','loss') AND timestamp >= ?\", (cutoff,)) rows = c.fetchall() if not rows: print(json.dumps({'error': f'No resolved bets in last {days} days'})) else: wins = sum(1 for r in rows if r[2]=='win') losses = sum(1 for r in rows if r[2]=='loss') total_staked = sum(r[0] for r in rows) total_payout = sum(r[1] for r in rows) profit = total_payout - total_staked roi = (profit / total_staked * 100) if total_staked > 0 else 0 platforms = {} for r in rows: p = r[3] if p not in platforms: platforms[p] = {'bets':0,'profit':0} platforms[p]['bets'] += 1 platforms[p]['profit'] += r[1] - r[0] for p in platforms: platforms[p]['profit'] = round(platforms[p]['profit'], 2) print(json.dumps({ 'window_days': days, 'total_bets': len(rows), 'wins': wins, 'losses': losses, 'win_rate': round(wins/(wins+losses)*100, 1), 'profit': round(profit, 2), 'roi_pct': round(roi, 1), 'by_platform': platforms }, indent=2)) conn.close() " ``` ### 3. Moltbook Profile JSON Generate a JSON payload compatible with Moltbook's agent profile schema: ```bash python3 -c " import sqlite3, json, os from datetime import datetime db = os.path.expanduser('~/.openclaw/data/bet_log.db') conn = sqlite3.connect(db) c = conn.cursor() c.execute(\"SELECT stake, payout, result, platform, timestamp FROM bets WHERE result IN ('win','loss') ORDER BY timestamp\") rows = c.fetchall() if not rows: print(json.dumps({'error': 'No resolved bets found'})) else: wins = sum(1 for r in rows if r[2]=='win') losses = sum(1 for r in rows if r[2]=='loss') total_staked = sum(r[0] for r in rows) profit = sum(r[1] for r in rows) - total_staked roi = (profit / total_staked * 100) if total_staked > 0 else 0 platforms = list(set(r[3] for r in rows)) first_bet = rows[0][4] last_bet = rows[-1][4] # Monthly returns monthly = {} for r in rows: mo = r[4][:7] if mo not in monthly: monthly[mo] = {'staked':0, 'profit':0} monthly[mo]['staked'] += r[0] monthly[mo]['profit'] += r[1] - r[0] monthly_roi = {k: round(v['profit']/v['staked']*100, 1) if v['staked']>0 else 0 for k,v in monthly.items()} profile = { 'schema_version': '1.0', 'agent_type': 'openclaw', 'generated_at': datetime.utcnow().isoformat() + 'Z', 'stats': { 'total_bets': len(rows), 'win_rate': round(wins/(wins+losses)*100, 1), 'roi_pct': round(roi, 1), 'total_volume': round(total_staked, 2), 'net_profit': round(profit, 2), 'first_bet': first_bet, 'last_bet': last_bet, 'active_days': len(set(r[4][:10] for r in rows)) }, 'platforms': platforms, 'monthly_roi': monthly_roi } print(json.dumps(profile, indent=2)) conn.close() " ``` To publish to Moltbook (requires MOLTBOOK_API_KEY): ```bash # Save profile to file first, then push python3 -c \"<above script>\" > /tmp/agent_profile.json curl -s -X POST https://api.moltbook.com/v1/agents/profile \ -H "Authorization: Bearer $MOLTBOOK_API_KEY" \ -H "Content-Type: application/json" \ -d @/tmp/agent_profile.json | jq . ``` ### 4. Reputation Card (Plaintext) Generate a human-readable reputation card for sharing: ```bash python3 -c " import sqlite3, os db = os.path.expanduser('~/.openclaw/data/bet_log.db') conn = sqlite3.connect(db) c = conn.cursor() c.execute(\"SELECT stake, payout, result FROM bets WHERE result IN ('win','loss')\") rows = c.fetchall() if not rows: print('No resolved bets found.') else: wins = sum(1 for r in rows if r[2]=='win') losses = sum(1 for r in rows if r[2]=='loss') total_staked = sum(r[0] for r in rows) profit = sum(r[1] for r in rows) - total_staked roi = (profit / total_staked * 100) if total_staked > 0 else 0 streak = 0; cur_type = '' for r in rows: if r[2] == cur_type: streak += 1 else: cur_type = r[2]; streak = 1 card = f''' ╔══════════════════════════════════════╗ ║ AGENT REPUTATION CARD ║ ╠══════════════════════════════════════╣ ║ Record: {wins}W - {losses}L{' '*(21-len(f'{wins}W - {losses}L'))}║ ║ Win Rate: {wins/(wins+losses)*100:.1f}%{' '*(22-len(f'{wins/(wins+losses)*100:.1f}%'))}║ ║ ROI: {roi:+.1f}%{' '*(22-len(f'{roi:+.1f}%'))}║ ║ Volume: ${total_staked:,.0f}{' '*(22-len(f'${total_staked:,.0f}'))}║ ║ Profit: ${profit:+,.2f}{' '*(22-len(f'${profit:+,.2f}'))}║ ║ Streak: {streak} {cur_type}s{' '*(22-len(f'{streak} {cur_type}s'))}║ ║ Total Bets: {len(rows)}{' '*(22-len(str(len(rows))))}║ ╚══════════════════════════════════════╝ ''' print(card.strip()) conn.close() " ``` ## Output Rules 1. Always show win rate as a percentage with one decimal place 2. Always show ROI with a sign prefix (+/-) 3. Volume and profit should be in USD with commas 4. When showing rolling windows, always note the time period 5. For Moltbook profiles, validate JSON before publishing 6. If bet count is under 50, add a disclaimer: "Small sample size — stats may not be reliable" 7. Always show current streak in the reputation card ## Error Handling - If bet_log.db doesn't exist, offer to create it with the schema and explain how to log bets - If no resolved bets exist, report "No resolved bets found" and suggest logging some bets first - If MOLTBOOK_API_KEY is not set and user requests publishing, explain how to get a key at https://moltbook.com - If Moltbook API returns an error, display the error message and suggest checking the key - If database is corrupted, suggest running `sqlite3 ~/.openclaw/data/bet_log.db "PRAGMA integrity_check"` ## About Built by [AgentBets](https://agentbets.ai) — full tutorial at [agentbets.ai/guides/openclaw-agent-reputation-tracker-skill/](https://agentbets.ai/guides/openclaw-agent-reputation-tracker-skill/). Part of the [OpenClaw Skills series](https://agentbets.ai/guides/#openclaw-skills) for the [Agent Betting Stack](https://agentbets.ai/guides/agent-betting-stack/).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)