Analyze

Skill anomalyarmor/agents/skills/analyze

Trigger AI analysis for your data assets. Handles "analyze my database", "generate intelligence", "refresh analysis", "update AI knowledge".From its SKILL.md

Install

npx -y skills add anomalyarmor/agents --skill analyze

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SKILL.md

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Analyze Your Data

Trigger AI-powered analysis to generate intelligence, descriptions, and knowledge base for your data assets.

Prerequisites

AnomalyArmor API key configured (~/.armor/config.yaml or ARMOR_API_KEY env var)
Python SDK installed (pip install anomalyarmor)
Data source connected (use /armor:connect first)

When to Use

"Analyze my database"
"Generate intelligence for the warehouse"
"Refresh AI analysis after schema changes"
"Update the knowledge base"
After connecting a new data source
After major schema changes

What Intelligence Includes

Table and column descriptions
Data type analysis
Relationship detection
Business context inference
Knowledge base for Q&A (/armor:ask)

Steps

Identify the asset to analyze
Trigger analysis with client.intelligence.generate()
Track progress with client.jobs.status()
Use /armor:ask once complete

Example Usage

Generate Intelligence for Full Asset

from anomalyarmor import Client
import time

client = Client()

# Trigger intelligence generation
result = client.intelligence.generate(
    asset="postgresql.analytics"
)

print(f"Job started: {result.job_id}")

# Poll for completion
while True:
    status = client.jobs.status(result.job_id)
    progress = status.get('progress', 0)
    state = status.get('status', 'unknown')

    print(f"Status: {state}, Progress: {progress}%")

    if state == 'completed':
        print("Intelligence generation complete!")
        break
    elif state == 'failed':
        print(f"Failed: {status.get('error')}")
        break

    time.sleep(10)  # Wait 10 seconds between checks

Analyze Specific Schemas

from anomalyarmor import Client

client = Client()

# Only analyze specific schemas
result = client.intelligence.generate(
    asset="postgresql.analytics",
    include_schemas="public,analytics"  # Comma-separated
)

print(f"Analyzing schemas: public, analytics")
print(f"Job ID: {result.job_id}")

Force Refresh After Changes

from anomalyarmor import Client

client = Client()

# Force regeneration even if intelligence exists
result = client.intelligence.generate(
    asset="postgresql.analytics",
    force_refresh=True
)

print(f"Forcing refresh: {result.job_id}")

Check Job Status

from anomalyarmor import Client

client = Client()

# Check status of any async job
status = client.jobs.status("job-uuid")

print(f"Job ID: {status.get('job_id')}")
print(f"Status: {status.get('status')}")  # pending, running, completed, failed
print(f"Progress: {status.get('progress')}%")
print(f"Workflow: {status.get('workflow_name')}")

if status.get('status') == 'failed':
    print(f"Error: {status.get('error')}")

Job States

State	Description
`pending`	Job queued, waiting to start
`running`	Job in progress
`completed`	Job finished successfully
`failed`	Job encountered an error

Timing

Small databases (< 100 tables): 1-2 minutes
Medium databases (100-500 tables): 2-5 minutes
Large databases (500+ tables): 5-15 minutes

When to Regenerate

After adding new tables or columns
After significant schema changes
After changing business context
Periodically (monthly) to keep context fresh

Follow-up Actions

Use /armor:ask to query the generated intelligence
Use /armor:status to check overall health
Review generated descriptions in AnomalyArmor dashboard