Root Cause Investigation

Community

nimrodfisher

root-cause-investigation

Systematic investigation of metric changes and anomalies. Use when a metric unexpectedly changes, investigating business metric drops, explaining performance variations, or drilling into aggregated metric drivers.

Overview

Publisher	nimrodfisher
Repository	`data-analytics-skills`
Skill name	`root-cause-investigation`
Stars	493
Forks	87
Bundled files	4
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by nimrodfisher on GitHub. Read the source before you install it.

Installation

Install the Root Cause Investigation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/root-cause-investigation
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/root-cause-investigation

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git /tmp/data-analytics-skills
mkdir -p .claude/skills
cp -r /tmp/data-analytics-skills/03-data-analysis-investigation/root-cause-investigation .claude/skills/root-cause-investigation

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Root Cause Investigation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Root Cause Investigation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Root Cause Investigation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Root Cause Investigation

When to use

A key metric dropped (or spiked) unexpectedly and the team needs an explanation
Stakeholders are asking "why did X happen?" and need an evidence-based answer
A metric change has been observed but the team is unsure whether it's noise or signal
Preparing a post-mortem after an incident that affected business metrics
A trend change happened weeks ago and needs retrospective investigation

Process

Validate the change — confirm the metric changed beyond normal variance using a z-score or simple comparison to the rolling average. If the change is within ±1.5 standard deviations, document it as within normal range and close. Use scripts/drilldown_analyzer.py --validate.
Establish a timeline — plot the metric over time to pinpoint when the change started. A sudden step change suggests a specific event; a gradual drift suggests a structural shift.
Decompose the metric — break the metric into its constituent parts (e.g., revenue = volume × price × mix). Determine which component is driving the change before drilling into dimensions.
Drill down systematically — compare the metric before vs. after the change across available dimensions (geography, platform, channel, product category, user segment). Sort by absolute contribution to identify the primary driver. Use scripts/drilldown_analyzer.py --drilldown. See references/rca_framework.md for the structured approach.
Test hypotheses — generate explicit hypotheses (volume drop, mix shift, per-unit quality change, data issue) and accept or reject each with evidence. Correlate the timeline with known events from references/hypothesis_testing_guide.md.
Write the root cause report — document the primary driver (quantified share of impact), supporting evidence, rejected hypotheses, and tiered recommendations (immediate / short-term / long-term). Use assets/rca_report_template.md.

Inputs the skill needs

Metric name and historical values (at least 30 days before the change)
Granular data with dimensional breakdowns (geography, platform, segment, etc.)
The date or date range when the change was noticed
A change log or incident log for the same period (product releases, campaigns, outages)
The business context: what decisions depend on this metric

Output

scripts/drilldown_analyzer.py — validates the change, computes dimensional drill-downs, and ranks contributors by impact
references/rca_framework.md — structured five-step RCA method with decision rules
references/hypothesis_testing_guide.md — checklist of common root causes and how to test each
assets/rca_report_template.md — report template: what changed, when, primary driver, supporting evidence, timeline, recommendations

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Root Cause Investigation AI skill do?

Why use Root Cause Investigation on TypingMind?

Because you install it once and use it with any model. Root Cause Investigation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Root Cause Investigation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/root-cause-investigation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Root Cause Investigation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Root Cause Investigation?

As many as you like. As long as a model supports skills, you can use Root Cause Investigation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Root Cause Investigation AI skill free?

Yes. It is published on GitHub by nimrodfisher under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Root Cause Investigation

When to use

Process

Inputs the skill needs

Output

Bundled files

Frequently asked questions

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