Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.
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Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install data-anomaly-detectorOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install data-anomaly-detectorβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/data-anomaly-detector/π‘Extract and place the folder at the path above, then restart your agent.
Category
πData & AnalyticsPlatforms
What Data Anomaly Detector can do for your AI workflow
Detect anomalies and outliers directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
Trusted by 3,419+ developers worldwide
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Data Anomaly Detector
Help me get started with Data Anomaly Detector
Explains what Data Anomaly Detector does, walks through the setup, and runs a quick demo based on your current project
Use Data Anomaly Detector to detect anomalies and outliers in construction data: unusual costs, ...
Invokes Data Anomaly Detector with the right parameters and returns the result directly in the conversation
What can I do with Data Anomaly Detector in my data & analytics workflow?
Lists the top use cases for Data Anomaly Detector, with example commands for each scenario
Guides & tutorials for AI skills
Data Anomaly Detector extends your AI assistant with the ability to detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using Data Anomaly Detector as its underlying capability.
Data Anomaly Detector works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Data Anomaly Detector once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
To install Data Anomaly Detector, copy the skill folder to `~/.claude/skills/data-anomaly-detector/` for use across all your projects, or `.claude/skills/data-anomaly-detector/` for a single project. Restart Claude and the skill is immediately active β invoke it with `/data-anomaly-detector` or just describe your goal and the AI will pick it up automatically.
Data Anomaly Detector has been installed 3,419 times, making it one of the more actively used skills in the Data & Analytics category. The install rate suggests it solves a real, recurring need rather than a niche edge case. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/data-anomaly-detector/ for personal use (all projects), or .claude/skills/data-anomaly-detector/ for project-specific use. Restart your AI client, then invoke with /data-anomaly-detector or let the AI discover it automatically.
Data Anomaly Detector supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Data Anomaly Detector is free to install. Check the repository for licensing information.
Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.
Automate my data & analytics tasks using Data Anomaly Detector
Identifies repetitive steps in your workflow and sets up Data Anomaly Detector to handle them automatically
MCP vs traditional plugins: what's the difference?
Data Anomaly Detector is categorized under Data & Analytics. These skills help AI agents perform specialized tasks in this domain.