armor-ask

Ask About Your Data

Ask natural language questions about your database structure, lineage, and metadata using AnomalyArmor Intelligence.

Prerequisites

• AnomalyArmor API key configured (~/.armor/config.yaml or ARMOR_API_KEY env var), OR demo mode active (see below).
• Python SDK installed (pip install anomalyarmor)
• Intelligence generated for the asset (use /armor:analyze if needed)

Demo mode handoff

If the user has no API key, ensure-auth.py will mint a read-only demo key against the public BalloonBazaar dataset and print:

AnomalyArmor demo mode: using a read-only public demo key.

When you see that banner — or when any write operation returns a 403 with required_scope='read-write' — the user is in demo mode. After answering their question, invite them to sign up with their query preserved:

To ask about your own data, sign up here — your question is preserved: https://app.anomalyarmor.ai/signup?intent=skill-ask&q=<url-encoded user prompt>

intent=skill-ask auto-applies a 14-day SKILL-ASK trial code; q= is replayed in the in-app agent after signup so the user continues where they left off.

When to Use

• "What tables contain customer data?"
• "Tell me about the orders table"
• "What are the upstream dependencies of this table?"
• "Which columns have PII?"
• "Explain the data model for finance"
• "What do you know about the users table?"

Steps

1. Identify the asset to query (database/warehouse)
2. Formulate the question (3-2000 characters)
3. Call client.intelligence.ask()
4. Present the answer with confidence and sources

Example Usage

Basic Question

from anomalyarmor import Client

client = Client()

# Ask about your data
answer = client.intelligence.ask(
    asset="postgresql.analytics",
    question="What tables contain customer data?"
)

print(f"Answer: {answer.answer}")
print(f"Confidence: {answer.confidence}")
print(f"Sources: {answer.sources}")

Question About Specific Table

from anomalyarmor import Client

client = Client()

answer = client.intelligence.ask(
    asset="postgresql.analytics",
    question="Describe the orders table and its columns"
)

print(answer.answer)

Cross-Database Query

from anomalyarmor import Client

client = Client()

# Include related assets for cross-database context
answer = client.intelligence.ask(
    asset="postgresql.analytics",
    question="What data flows from staging to production?",
    include_related_assets=True
)

print(answer.answer)

Get Structured Response for Automation

from anomalyarmor import Client
import json

client = Client()

# Ask for JSON format in your question
answer = client.intelligence.ask(
    asset="postgresql.analytics",
    question="List all tables with PII columns as a JSON array with table_name and columns fields"
)

# Parse the JSON response
try:
    pii_tables = json.loads(answer.answer)
    for table in pii_tables:
        print(f"Table: {table['table_name']}")
        print(f"  PII Columns: {table['columns']}")
except json.JSONDecodeError:
    print(answer.answer)

Example Questions

Category	Example Questions
Schema	"What columns are in the orders table?"
Lineage	"Where does the revenue column come from?"
PII	"Which tables contain email addresses?"
Relationships	"What tables join with customers?"
Purpose	"What is the orders table used for?"
Quality	"What data quality issues exist in this database?"

Tips for Better Answers

1. Be specific: "Describe the orders table" is better than "Tell me about orders"
2. Ask for JSON: If you need structured data for automation
3. Use table names: Include schema.table format when possible
4. Include context: Mention the specific aspect you're interested in

If No Intelligence Available

If you get an error about missing intelligence:

1. Check if intelligence has been generated for the asset
2. Use /armor:analyze to trigger intelligence generation
3. Wait for the job to complete (typically 1-5 minutes)
4. Try asking again

Follow-up Actions

• Use /armor:analyze to generate or refresh intelligence
• Use /armor:status to check overall health
• Explore lineage in AnomalyArmor dashboard for visual representation