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:
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:
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):
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:
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):
# 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:
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
- Always show win rate as a percentage with one decimal place
- Always show ROI with a sign prefix (+/-)
- Volume and profit should be in USD with commas
- When showing rolling windows, always note the time period
- For Moltbook profiles, validate JSON before publishing
- If bet count is under 50, add a disclaimer: "Small sample size — stats may not be reliable"
- 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 — full tutorial at agentbets.ai/guides/openclaw-agent-reputation-tracker-skill/.
Part of the OpenClaw Skills series for the Agent Betting Stack.
