Anomaly Investigation

Anomaly Investigation is a Data skill for Claude Code, published by gaasher in Agent-Loop-Skills.

166 stars19 forkson gaasher/Agent-Loop-SkillsAdded 2026/09/08+2% in starsRepository updated 2026/06/30

agent-skillsagentic-loopsagentic-workflowsai-agentsanthropicautoresearchclaudeclaude-codedata-analysisliterature-reviewllm-agentsmachine-learningml-autoresearchopen-sourceprompt-engineeringred-teamingscientific-writingskillssubagents

Install in seconds

Install Anomaly Investigation

Copy Anomaly Investigation into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/anomaly-investigation ~/.claude/skills/anomaly-investigation

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/gaasher/Agent-Loop-Skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: loops/anomaly-investigation/SKILL.md in gaasher/Agent-Loop-Skills
Installs to: ~/.claude/skills/anomaly-investigation
Collection: One of 25 skills cataloged from this repository
Category: Data — 812 skills

What Anomaly Investigation does

Anomaly Investigation diagnoses a known data anomaly by forming candidate causes, testing them against the data, and eliminating those the evidence refutes. Use it when you already have a spike, drop, or outlier and need the confirmed root cause and supporting evidence.

Anomaly Investigation is cataloged under Data on DirSkills. Anomaly Investigation comes from a repository tagged agent-skills, agentic-loops, agentic-workflows, ai-agents and anthropic.

Documentation

README

Excerpt

Anomaly Investigation Loop

A form → test → eliminate → confirm loop — root-cause analysis as a search. The artifact is an investigation log; the feedback signal is the count of live candidate explanations, driven down toward a single cause that is confirmed, not merely consistent. Each iteration you test one candidate against the data and drop the ones the data refutes, narrowing the field until one survives.

The discipline this enforces: a cause is "root" only when it both survives an honest attempt to refute it and makes a positive prediction that checks out (e.g. "if this is the cause, removing it restores normal" — and it does). A story that merely could explain the anomaly is a hypothesis, not a finding.

When to use

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Anomaly Investigation

What else does gaasher publish alongside Anomaly Investigation?
Anomaly Investigation is one of 25 skills that DirSkills catalogs from gaasher/Agent-Loop-Skills, the repository it ships in. Its siblings there include Alpha Evolve, Blue Team and Claim Verify Loop. Each one is a separate skill with its own page in this directory, installs the same way Anomaly Investigation does, and is maintained by gaasher in that same repository. The rest of the collection is listed on the gaasher/Agent-Loop-Skills page.
How does Anomaly Investigation compare to other Data skills?
Anomaly Investigation ranks #753 by stars among the 812 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Anomaly Investigation against them. Open each page to compare what they document and how they install.