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/recommendnpx skills add anomalyarmor/agents --skill recommendgit 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 | $0.00032 | $0.01658 |
| Opus 5 | $0.00016 | $0.00829 |
| Sonnet 5 | $0.00006 | $0.00332 |
| Haiku 4.5 | $0.00003 | $0.00166 |
Grade A, and why
armor-recommend 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.
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monitoring Recommendations
Get AI-driven recommendations for what to monitor and how to configure thresholds based on historical patterns.
Prerequisites
- AnomalyArmor API key configured (
~/.armor/config.yamlorARMOR_API_KEYenv var), OR demo mode active (see below). - Python SDK installed (
pip install anomalyarmor) - Data source connected with discovery completed
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 monitor your own data, sign up here — your question is preserved:
https://app.anomalyarmor.ai/signup?intent=skill-recommend&q=<url-encoded user prompt>
intent=skill-recommend auto-applies a 14-day SKILL-RECOMMEND trial code; q= is replayed in the in-app agent after signup so the user continues where they left off.
When to Use
- "What should I monitor?"
- "Suggest tables to monitor"
- "What are good thresholds for this table?"
- "What's missing from my monitoring?"
- "Help me set up monitoring for my warehouse"
- "Which tables are most critical?"
Steps
Get Freshness Recommendations
- Call
client.recommendations.freshness()for asset - Review prioritized list of tables with suggested thresholds
- For each table, see why it's recommended
- Use
/armor:testto dry-run before enabling - Use
/armor:monitorto enable
Get Metrics Recommendations
- Specify the table to analyze (optional)
- Call
client.recommendations.metrics()with table details - Review suggested metrics based on column analysis
- Create metrics with
/armor:quality
Analyze Coverage
- Call
client.recommendations.coverage()for asset - Review coverage percentage and gaps
- Prioritize high-importance unmonitored tables
- Use recommendations to fill gaps
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.
- 2d ago First seen · 221 lines · 32 tokens per session scan A a190423f447f
armor-recommend 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 1,658 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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chat-perf
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chat-pet-sprite-creation
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cpu-profile-analysis
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