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/asknpx skills add anomalyarmor/agents --skill askgit 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 | $0.00037 | $0.01081 |
| Opus 5 | $0.00018 | $0.00541 |
| Sonnet 5 | $0.00007 | $0.00216 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
armor-ask 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.yamlorARMOR_API_KEYenv var), OR demo mode active (see below). - Python SDK installed (
pip install anomalyarmor) - Intelligence generated for the asset (use
/armor:analyzeif 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
- Identify the asset to query (database/warehouse)
- Formulate the question (3-2000 characters)
- Call
client.intelligence.ask() - 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
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 · 162 lines · 37 tokens per session scan A e889822a91a4
armor-ask is a skill published in the GitHub repository anomalyarmor/agents (1 stars, last pushed 3mo ago), licensed MIT. It adds 37 tokens to every session and 1,081 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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