oraclaw-anomaly

oraclaw-anomaly is a skill for Claude Code, Codex from Whatsonyourmind/oraclaw. It costs 43 tokens per session (577 once invoked), scanned A, original, MIT.

An outlier-detection tool that checks whether values are unusually far from the rest of a dataset or data stream. It uses statistical methods such as z-scores and the interquartile range.

In plain words
What is it for?
Use it to check whether a metric is abnormal, find outliers in stored data, monitor incoming values, and trigger alerts for unusual readings.
Why use it?
It helps agents spot unusual measurements without relying on guesswork. It can identify spikes, errors, or other values that need attention.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: positional $N argument; built for openclaw.

Good fit Use it to check whether a metric is abnormal, find outliers in stored data, monitor incoming values, and trigger alerts for unusual readings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/whatsonyourmind/oraclaw/oraclaw-anomaly
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.

Any agent
npx skills add Whatsonyourmind/oraclaw --skill oraclaw-anomaly
Clone the repo
git clone --depth 1 https://github.com/Whatsonyourmind/oraclaw

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for oraclaw-anomaly

README.md
[![agentmods](https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-anomaly/github.svg)](https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-anomaly)
Your own site
<a href="https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-anomaly"><img src="https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-anomaly/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for oraclaw-anomaly

Your own site · 80×15
<a href="https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-anomaly"><img src="https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-anomaly.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 577 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00043 $0.00577
Opus 5 $0.00022 $0.00289
Sonnet 5 $0.00009 $0.00115
Haiku 4.5 $0.00004 $0.00058

Measured 12d ago against content hash 2968e8341663, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

oraclaw-anomaly 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 12d 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.

mission-control/packages/clawhub-skills/oraclaw-anomaly/SKILL.md · 72 lines

What it actually says

OraClaw Anomaly — Outlier Detection for Agents

You are a monitoring agent that detects anomalies in data using statistical methods.

When to Use This Skill

Use when the user or agent needs to:

  • Check if a data point is abnormal ("is this metric spiking?")
  • Find outliers in a dataset
  • Monitor a data stream for anomalies in real-time
  • Set up alerts for unusual values

Tool: detect_anomaly

Z-Score method (default, best for normally distributed data):

{
  "data": [10, 12, 11, 13, 10, 12, 11, 100, 12, 10],
  "method": "zscore",
  "threshold": 3
}

Returns: anomaly indices, z-scores, mean, stdDev. The value 100 would be flagged (z-score >> 3).

IQR method (robust to skewed data):

{
  "data": [10, 12, 11, 13, 10, 12, 11, 100, 12, 10],
  "method": "iqr",
  "threshold": 1.5
}

Returns: anomaly indices, Q1, Q3, IQR, bounds.

Rules

  1. Z-score: threshold=3 catches ~0.3% outliers (3 sigma). Use 2 for more sensitive detection.
  2. IQR: threshold=1.5 is standard (Tukey's fences). Use 3.0 for extreme outliers only.
  3. Z-score assumes normal distribution. Use IQR for skewed data.
  4. Minimum 10 data points for reliable detection.
  5. For real-time monitoring, send batches of recent values (last 100 points).

Pricing

$0.02 per detection call. USDC on Base via x402. Free tier: 3,000 calls/month.

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 12d ago First seen · 72 lines · 43 tokens per session scan A 2968e8341663

Subscribe to this mod's changes

oraclaw-anomaly is a skill published in the GitHub repository Whatsonyourmind/oraclaw (13 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 577 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 2026-08-30.

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