armor-analyze

A skill for asking AnomalyArmor to analyze connected data sources and build descriptions, detected relationships, inferred business context, and a knowledge base. AnomalyArmor is a data-analysis service.

In plain words
What is it for?
Starting and tracking analysis jobs, refreshing analysis, and preparing a connected data source for natural-language questions.
Why use it?
It turns a connected database or warehouse into information that can support later questions, including after schema changes.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/anomalyarmor/agents/analyze
Any agent
npx skills add anomalyarmor/agents --skill analyze
Clone the repo
git clone --depth 1 https://github.com/anomalyarmor/agents

Made for: Claude Code, Codex.

Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 902 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00032 $0.00902
Opus 5 $0.00016 $0.00451
Sonnet 5 $0.00006 $0.00180
Haiku 4.5 $0.00003 $0.00090

Measured 2d ago against content hash 5b915e91bf6e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

armor-analyze scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/analyze/SKILL.md · 161 lines

How it starts

The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

  1. Identify the asset to analyze
  2. Trigger analysis with client.intelligence.generate()
  3. Track progress with client.jobs.status()
  4. 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}")

Read the full file on GitHub · 161 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 161 lines · 32 tokens per session scan A 5b915e91bf6e

Subscribe to this mod's changes

armor-analyze is a skill published in the GitHub repository anomalyarmor/agents (1 stars, last pushed 3mo ago), licensed MIT. It adds 32 tokens to every session and 902 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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