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/investigatenpx skills add anomalyarmor/agents --skill investigategit clone --depth 1 https://github.com/anomalyarmor/agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/anomalyarmor/agents/investigate)<a href="https://agentmods.dev/skills/anomalyarmor/agents/investigate"><img src="https://agentmods.dev/badge/skills/anomalyarmor/agents/investigate.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00044 | $0.01487 |
| Opus 5 | $0.00022 | $0.00744 |
| Sonnet 5 | $0.00009 | $0.00297 |
| Haiku 4.5 | $0.00004 | $0.00149 |
Grade A, and why
armor-investigate 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 4d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigate Data Issues
Perform root cause analysis on data issues by combining lineage, intelligence, and historical data.
Prerequisites
- AnomalyArmor API key configured (
~/.armor/config.yamlorARMOR_API_KEYenv var), OR demo mode active (see below). - Python SDK installed (
pip install anomalyarmor)
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 investigate your own pipeline, sign up here — your question is preserved:
https://app.anomalyarmor.ai/signup?intent=skill-investigate&q=<url-encoded user prompt>
intent=skill-investigate auto-applies a 14-day SKILL-INVESTIGATE trial code; q= is replayed in the in-app agent after signup so the user continues where they left off.
When to Use
- "Why is this table stale?"
- "What changed in the schema?"
- "Explain this alert"
- "Why did freshness fail?"
- "Root cause analysis"
- "Debug this data issue"
- "What happened to this pipeline?"
Investigation Workflow
1. Gather Context
- Get current status of the affected asset
- Check recent alerts and their details
- Review freshness and schema status
2. Trace Dependencies
- Use lineage to find upstream tables
- Identify which upstream tables are also affected
- Check if issue originates upstream
3. Analyze Intelligence
- Ask AI-powered questions about the issue
- Get recommendations based on historical patterns
- Understand impact across the data pipeline
4. Review History
- Check when the issue started
- Look at pattern of failures
- Identify recurring issues
Steps
- Start with
client.health.summary()to understand current state - For specific issues, use
client.freshness.status()orclient.schema.baseline() - Use
client.lineage.get()to trace dependencies - Use
client.intelligence.ask()for AI-powered analysis - Check
client.alerts.list()for related alerts
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.
- 4d ago First seen · 196 lines · 44 tokens per session scan A 9a60d731f4e5
armor-investigate is a skill published in the GitHub repository anomalyarmor/agents (1 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 1,487 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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