armor-test

A testing tool for previewing monitoring rules before they are enabled. It can show which alerts a proposed threshold or rule would have triggered using historical data.

In plain words
What is it for?
Dry-running data-freshness thresholds, previewing schema-drift or other alert rules, estimating alert frequency, and adjusting configurations before deployment.
Why use it?
It helps catch thresholds that would miss important events or create too many notifications. This reduces unnecessary alert noise before a monitoring configuration goes live.

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

Made for: Claude Code, Codex.

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,148 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 $0.00031 $0.01148
Opus 5 $0.00015 $0.00574
Sonnet 5 $0.00006 $0.00230
Haiku 4.5 $0.00003 $0.00115

Measured yesterday against content hash 3c298b7a51b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

armor-test 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 yesterday.

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/test/SKILL.md · 171 lines

How it starts

The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Test Before Deploying

Preview what alerts would fire with proposed configurations. Avoid alert fatigue by testing thresholds before enabling.

Prerequisites

  • AnomalyArmor API key configured (~/.armor/config.yaml or ARMOR_API_KEY env var)
  • Python SDK installed (pip install anomalyarmor)

When to Use

  • "Test this freshness threshold before I enable it"
  • "What alerts would fire if I set the threshold to 4 hours?"
  • "Dry-run the schema drift check"
  • "Preview the impact of this rule"
  • "Will this configuration cause too many alerts?"

Steps

Dry-Run Freshness Threshold

  1. Specify the table and proposed threshold
  2. Call client.freshness.dry_run() with proposed config
  3. Review predicted alerts over historical data
  4. Adjust threshold if too many/few alerts predicted
  5. When satisfied, create the actual schedule

Preview Alert Rules

  1. Specify the event types to filter
  2. Call client.alerts.preview() with event types
  3. See what historical events would have triggered alerts
  4. Evaluate alert frequency
  5. Adjust configuration as needed

Example Usage

Dry-Run Freshness Threshold

from anomalyarmor import Client

client = Client()

# Test what would happen with a 4-hour freshness threshold
# Uses historical data to predict alert frequency
result = client.freshness.dry_run(
    asset_id="asset-uuid",
    table_path="public.orders",
    expected_interval_hours=4,
    lookback_days=7
)

print(f"Configuration: Alert if stale for {result.threshold_hours} hours")
print(f"Historical period: {result.lookback_days} days")
print()
print(f"Total checks analyzed: {result.total_checks}")
print(f"Would alert count: {result.would_alert_count}")
print(f"Alert rate: {result.alert_rate_percent:.1f}%")
print()

if result.would_alert_now:
    print(f"Current status: Would alert NOW (age: {result.current_age_hours:.1f}h)")
else:
    print(f"Current status: OK (age: {result.current_age_hours:.1f}h)")

print(f"\nRecommendation: {result.recommendation}")

Read the full file on GitHub · 171 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. yesterday First seen · 171 lines · 31 tokens per session scan A 3c298b7a51b8

Subscribe to this mod's changes

armor-test is a skill published in the GitHub repository anomalyarmor/agents (1 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 1,148 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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