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.
npx agentmods add skills/anomalyarmor/agents/testnpx skills add anomalyarmor/agents --skill testgit clone --depth 1 https://github.com/anomalyarmor/agentsWhat 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.
| Model | Per session | Once 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 |
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.
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.yamlorARMOR_API_KEYenv 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
- Specify the table and proposed threshold
- Call
client.freshness.dry_run()with proposed config - Review predicted alerts over historical data
- Adjust threshold if too many/few alerts predicted
- When satisfied, create the actual schedule
Preview Alert Rules
- Specify the event types to filter
- Call
client.alerts.preview()with event types - See what historical events would have triggered alerts
- Evaluate alert frequency
- 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}")
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.
- yesterday First seen · 171 lines · 31 tokens per session scan A 3c298b7a51b8
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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