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Deep Analyzer

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MAGA2010
deep-analyzer

Optional modelling stage: regression, mixed effects, clustering, dimensionality reduction, forecasting. Only runs when the design supports it; every model carries diagnostics.

Overview

PublisherMAGA2010
Repositorydata-master
Skill namedeep-analyzer
Stars
55
Forks
1
Bundled files
Instructions only
LicenseMIT
Links
  • 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 MAGA2010 on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/MAGA2010/data-master/tree/main/skills/deep-analyzer
  4. 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.
2

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/MAGA2010/data-master.git /tmp/data-master
mkdir -p .claude/skills
cp -r /tmp/data-master/skills/deep-analyzer .claude/skills/deep-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep 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 Deep 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 Deep 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.

deep-analyzer

Stage 2d of the data-master pipeline. The router in ../../SKILL.md owns the end-to-end run; this skill owns one stage and one gate. Read the parent contract before changing anything here: the invariants in ../../SKILL.md (additive-only, types decided once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

When to invoke

Only when the user asks for a model, or when a stated question cannot be answered by a group comparison. Never the default: a model adds assumptions that must be defended.

Inputs

  • cleaned data
  • a declared model specification: outcome, predictors, random effects, family, link

Outputs

  • analysis/model.json: coefficients with CIs, fit statistics, diagnostics, n, unit_of_analysis, rule_source
  • analysis/model_residuals.png when diagnostics are visual

Commands

powershell
# v1 ships no model-fitting script: a deep-analysis run is an explicit,
# supervised step that must leave analysis/model.json in place.
python -c "import json; d=json.load(open('outputs/analysis/model.json',encoding='utf-8')); assert d.get('rule_source'); print('model artifact OK')"

Gate (default FAIL)

Every coefficient has an estimate, a CI and the unit it applies to. Every model reports at least one diagnostic appropriate to its family (residual vs fitted, overdispersion, VIF, convergence). A model that did not converge is reported as such, never as a fit.

Failure modes seen in real runs

  • Pseudo-replication. Rows that share a subject are modelled as independent, so every standard error is too small. Random effects are required when the design is nested.
  • A converged-looking fit with separation. Logistic regression on a rare outcome can produce enormous coefficients; the diagnostic must be printed, not skipped.
  • Comparing models on different n. AIC across different complete-case sets is not a comparison.

Do not

  • do not run a model because it is available;
  • do not report a coefficient without its scale and CI;
  • do not drop the diagnostics to keep the table short.

Upstream / downstream

  • previous: ../statistical-analyzer/SKILL.md
  • next: ../visualizer/SKILL.md
  • contract: ../../references/workflow-contract.md

Frequently asked questions

What does the Deep Analyzer AI skill do?

Optional modelling stage: regression, mixed effects, clustering, dimensionality reduction, forecasting. Only runs when the design supports it; every model carries diagnostics.

Why use Deep Analyzer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MAGA2010/data-master/tree/main/skills/deep-analyzer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deep 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 Deep Analyzer?

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

Is the Deep Analyzer AI skill free?

Yes. It is published on GitHub by MAGA2010 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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