Metrics Analyzer

Community

Mehdibargach

metrics-analyzer

Analyze product data from a CSV file or pasted data. Finds trends, anomalies, segments, and actionable insights. Ask questions about your data in plain English.

Overview

Publisher	Mehdibargach
Repository	`claude-code-pm-skills`
Skill name	`metrics-analyzer`
Stars	97
Forks	38
Bundled files	Instructions only
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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by Mehdibargach on GitHub. Read the source before you install it.

Installation

Install the Metrics Analyzer 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/Mehdibargach/claude-code-pm-skills/tree/main/skills/metrics-analyzer
Enable the skill in any chat where you want it available.

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/Mehdibargach/claude-code-pm-skills.git /tmp/claude-code-pm-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-pm-skills/skills/metrics-analyzer .claude/skills/metrics-analyzer

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

Use it in TypingMind

Enable Metrics Analyzer 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 Metrics Analyzer 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 Metrics Analyzer 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.

Metrics Analyzer

Turn raw product data into insights. No dashboards — just clear answers to "what happened, why, and what should we do about it."

Process

Load the data. Read the CSV file or parse pasted data. Identify columns, data types, date ranges, and row count.
Understand structure. Detect date columns, metric columns (numeric), and dimension columns (categorical). Identify the grain (daily, weekly, per-user, per-transaction).
Compute summary statistics. For each key metric: mean, median, min, max, standard deviation, and recent trend direction.
Detect trends. Compare recent period vs. prior period. Calculate week-over-week and month-over-month changes. Flag anything moving more than 10% as notable.
Flag anomalies. Identify data points more than 2 standard deviations from the mean. Check for sudden drops, spikes, or flatlines.
Segment analysis. If dimension columns exist, break metrics by segment. Find which segments are driving overall changes.
Generate insights. Synthesize findings into 3 actionable insights ranked by business impact.

Output Format

Data Summary

Rows: [N] | Columns: [N] | Date range: [start] to [end]
Grain: [daily/weekly/per-user/etc.]
Key metrics found: [list]
Dimensions found: [list]

Key Metrics Overview

Metric	Current	Prior Period	Change	Trend
[metric]	[value]	[value]	[+/-X%]	[up/down/stable]

Trends

[Metric 1]: [Direction] [magnitude] over [time period]. [One sentence context.]
[Metric 2]: [Direction] [magnitude] over [time period]. [One sentence context.]

Anomalies

[Date/period]: [What happened] — [metric] was [value] vs. expected [value]. Possible cause: [hypothesis].

Segment Breakdown

Segment	[Metric]	vs. Average	Notable
[segment]	[value]	[+/-X%]	[yes/no + why]

Top 3 Insights

[Insight title]: [What the data shows] — [Why it matters] — [Suggested action]
[Insight title]: [What the data shows] — [Why it matters] — [Suggested action]
[Insight title]: [What the data shows] — [Why it matters] — [Suggested action]

Recommended Next Analysis

[What to dig into next and why]

Rules

Always start by showing the data summary so the user can confirm the data loaded correctly.
Use Bash with Python (pandas) for any computation. Do not eyeball numbers — calculate them.
Trends must include magnitude ("up 12%"), not just direction ("going up").
Every anomaly must include a hypothesis for the cause, even if speculative. Label speculative causes clearly.
Insights must be actionable. "Revenue is up" is an observation. "Revenue is up 15% driven by Segment X — double down on acquisition there" is an insight.
If the data is messy (missing values, inconsistent formats), clean it and report what you did.
If the data is too small for meaningful analysis (under 30 data points), say so explicitly.
Do not generate charts — describe what a chart would show in words. The user can visualize later.
If the user asks a specific question about the data, answer that question FIRST, then provide the broader analysis.
Save the analysis to a file in the current working directory as metrics-analysis-[date].md.

Frequently asked questions

What does the Metrics Analyzer AI skill do?

Analyze product data from a CSV file or pasted data. Finds trends, anomalies, segments, and actionable insights. Ask questions about your data in plain English.

Why use Metrics Analyzer on TypingMind?

Because you install it once and use it with any model. Metrics Analyzer 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 Metrics Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mehdibargach/claude-code-pm-skills/tree/main/skills/metrics-analyzer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Metrics Analyzer?

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 Metrics Analyzer?

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

Is the Metrics Analyzer AI skill free?

It is published on GitHub by Mehdibargach. Check the repository for licensing terms. 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

Metrics Analyzer

Process

Output Format

Data Summary

Key Metrics Overview

Trends

Anomalies

Segment Breakdown

Top 3 Insights

Recommended Next Analysis

Rules

Frequently asked questions

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