anomstack

name	anomstack
description	Debug the Anomstack demo instance on Fly.io - check schedules, jobs, LLM alerts, and PostHog tracking Use when this capability is needed.
metadata	{"author":"andrewm4894"}

Debug Demo Instance

This skill helps diagnose issues with the Anomstack demo instance running on Fly.io (app: anomstack-demo).

Quick Checks

1. Check Fly.io App Status

fly status -a anomstack-demo
fly logs -a anomstack-demo --no-tail | tail -50

2. List Fly.io Secrets

fly secrets list -a anomstack-demo

3. Check Dagster Schedule Status

SSH into the instance and list schedules:

fly ssh console -a anomstack-demo -C "sh -c 'cd /opt/dagster/app && dagster schedule list -m anomstack.main 2>&1'" | grep -i llmalert

4. Check Recent LLM Alert Runs

Query the Dagster run database:

fly ssh console -a anomstack-demo -C "python3 -c \"
import sqlite3
conn = sqlite3.connect('/data/dagster_storage/history/runs/index.db')
cur = conn.cursor()
cur.execute('''SELECT DISTINCT run_id, datetime(timestamp) FROM event_logs WHERE event LIKE \\\"%llmalert%\\\" ORDER BY timestamp DESC LIMIT 10''')
for row in cur.fetchall():
    print(row)
\""

5. Check a Specific Run's Status

Replace <run_id> with the actual run ID:

fly ssh console -a anomstack-demo -C "python3 -c \"
import sqlite3
conn = sqlite3.connect('/data/dagster_storage/history/runs/<run_id>.db')
cur = conn.cursor()
cur.execute('SELECT dagster_event_type, datetime(timestamp) FROM event_logs ORDER BY timestamp DESC LIMIT 10')
for row in cur.fetchall():
    print(row)
\""

6. Check LLM Alert Job Output

Check the result of the llmalert step:

fly ssh console -a anomstack-demo -C "python3 -c \"
import pickle
with open('/data/artifacts/storage/<run_id>/<batch>_llmalert/result', 'rb') as f:
    df = pickle.load(f)
print('Shape:', df.shape)
print('Columns:', list(df.columns))
if len(df) > 0:
    print(df.head())
else:
    print('DataFrame is empty - no anomalies detected')
\""

7. Test anomaly-agent Directly

Verify the LLM is working:

fly ssh console -a anomstack-demo -C "python3 -c \"
import pandas as pd
from anomaly_agent import AnomalyAgent

dates = pd.date_range(start='2026-01-01', periods=10, freq='h')
values = [10, 11, 10, 9, 10, 100, 10, 11, 9, 10]  # 100 is obvious anomaly
df = pd.DataFrame({'metric_timestamp': dates, 'metric_value': values})

agent = AnomalyAgent()
anomalies = agent.detect_anomalies(df, timestamp_col='metric_timestamp')
df_anomalies = agent.get_anomalies_df(anomalies)
print(f'Detected {len(df_anomalies)} anomalies')
print(df_anomalies)
\""

8. Check Environment Variables

fly ssh console -a anomstack-demo -C "python3 -c \"
import os
print('OPENAI_API_KEY set:', 'OPENAI_API_KEY' in os.environ)
print('ANOMSTACK_LLM_PLATFORM:', os.environ.get('ANOMSTACK_LLM_PLATFORM', 'not set'))
print('ANOMSTACK_OPENAI_MODEL:', os.environ.get('ANOMSTACK_OPENAI_MODEL', 'not set'))
print('POSTHOG_ENABLED:', os.environ.get('POSTHOG_ENABLED', 'not set'))
\""

9. Check PostHog Python Package

fly ssh console -a anomstack-demo -C "python3 -c \"
try:
    import posthog
    print(f'posthog version: {posthog.VERSION}')
except ImportError:
    print('posthog NOT installed - LLM tracking to PostHog will not work')
\""

10. Check Disk Usage

fly ssh console -a anomstack-demo -C "df -h"
fly ssh console -a anomstack-demo -C "du -sh /data/*"

PostHog Checks

Use the PostHog MCP tools to check LLM event tracking:

1. Switch to the correct project:

mcp__posthog__switch-project with projectId: 148051

2. Check LLM event definitions:

mcp__posthog__event-definitions-list with q: "llm"

3. Check LLM costs:

mcp__posthog__get-llm-total-costs-for-project with projectId: 148051, days: 30

Common Issues

LLM Alerts Not Sending Events to PostHog

• Cause: posthog Python package not installed
• Fix: Add posthog to requirements.txt and redeploy

LangSmith Authentication Errors

• Cause: Invalid/expired LANGSMITH_API_KEY
• Impact: Tracing won't work, but LLM calls still succeed
• Fix: Update the LANGSMITH_API_KEY secret or disable tracing

LLM Alert Jobs Return Empty Results

• Cause: The LLM may not detect anomalies in the data
• Check: Verify input data has actual anomalies to detect
• Debug: Check the get_llmalert_data step output to see what data was passed to the LLM

Schedules Show RUNNING but Jobs Don't Execute

• Check: Verify the Dagster daemon is running
• Check: Look for run timeouts or resource constraints

Deployment

After fixing issues, redeploy:

make fly-deploy-demo

For a fresh build (clears Docker cache):

make fly-deploy-demo-fresh

Key Paths on Fly.io Instance

• App directory: /opt/dagster/app/
• Data volume: /data/
• DuckDB database: /data/anomstack.db
• Dagster storage: /data/dagster_storage/
• Compute logs: /data/artifacts/storage/

---

Source: andrewm4894/anomstack — distributed by TomeVault.