Isolation_Forest
CheckedThis Isolation Forest isolates anomalies in data by recursively partitioning space, quickly identifying outliers for fraud detection or unusual event spotting.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add davidcastagnetoa/skills --skill isolation_forest -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
davidcastagnetoa
Taken from the source listing. We haven't verified who controls this account.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned davidcastagnetoa/skills at bd906f22af.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- bd906f22af
- Manifest
- skills/isolation_forest/SKILL.md
- Scanned
- 1 file, 30 lines
About this skill
What it does
This skill implements the Isolation Forest algorithm, a machine learning technique used for anomaly detection. It isolates anomalies rather than profiling normal data points, making it efficient for identifying rare events or outliers in datasets. The skill can be used to flag unusual data points based on their isolation path length within a forest of random trees.
When to use it
- Fraud Detection: Identify potentially fraudulent transactions by flagging those that deviate significantly from typical patterns.
- Network Intrusion Detection: Detect unusual network activity that might indicate a security breach or malicious attack.
- Predictive Maintenance: Find equipment malfunctions early by identifying anomalous sensor readings.
- Data Validation: Identify data points that are likely errors or inconsistencies within a dataset.
Key capabilities
- Anomaly detection using Isolation Forest algorithm
- Identification of outliers in datasets
- Efficient processing for large datasets
- Isolation path length calculation
Example prompts
- "Analyze this transaction history and flag any potentially fraudulent transactions."
- "Identify unusual network traffic patterns based on the provided logs."
- “Find anomalies in these sensor readings from a manufacturing machine.”
Tips & gotchas
The effectiveness of Isolation Forest depends heavily on appropriate parameter tuning (e.g., number of trees, contamination ratio). Consider experimenting with different settings to optimize performance for your specific dataset and use case.