| name | check-signals |
| description | Query and display recent AI trading signals from the database. Use when the user wants to review latest intelligence signals, check impact scores, or see what the AI discovery engine has found for congressional trading activity. |
Check Signals
Query the ai_intelligence_signals table in data/data.db and present results.
Default Query (latest 20 signals)
python -c "
import sqlite3, os
conn = sqlite3.connect('data/data.db')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('''
SELECT source_type, source_name, ticker, impact_score, sentiment,
recommended_execution, timestamp, logic_reasoning
FROM ai_intelligence_signals
ORDER BY timestamp DESC
LIMIT 20
''')
rows = c.fetchall()
if not rows:
print('No signals found.')
else:
for r in rows:
exe = r['recommended_execution'] or 'N/A'
ticker = r['ticker'] or 'N/A'
score = r['impact_score'] or 0
print(f\"[{r['timestamp']}] {r['source_name']} | {ticker} | Score: {score} | {r['sentiment']} | {exe}\")
if r['logic_reasoning']:
print(f\" -> {r['logic_reasoning'][:120]}\")
print()
conn.close()
"
Filtered Queries
Adapt the query based on user request:
- By politician: Add
WHERE source_name LIKE '%name%'
- High impact only: Add
WHERE impact_score >= 8
- By ticker: Add
WHERE ticker = 'XXXX'
- OPEN signals only: Add
WHERE recommended_execution = 'OPEN'
- Date range: Add
WHERE timestamp >= '2025-01-01'
Output Format
Present results as a markdown table with columns:
| Time | Politician | Ticker | Score | Sentiment | Action | Reasoning |
