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Deep Research

Community
Undertone0809
deep-research

Conducts enterprise-grade research with multi-source synthesis, citation tracking, and verification. Produces citation-backed reports through a structured pipeline with source credibility scoring, and grounds abstract findings in concrete examples, cases, counterexamples, or mini-scenarios when helpful. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.

Overview

PublisherUndertone0809
Repositoryrudder
Skill namedeep-research
Stars
290
Forks
31
Bundled files
16
LicenseApache-2.0
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.

  • 16 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Undertone0809 on GitHub. Read the source before you install it.

Installation

Install the Deep Research 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.
  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/Undertone0809/rudder.git /tmp/rudder
mkdir -p .claude/skills
cp -r /tmp/rudder/server/resources/community-skills/deep-research .claude/skills/deep-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep Research 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 Research 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 Research 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 Research

Core Purpose

Deliver citation-backed, verified research reports through a structured pipeline with source credibility scoring, evidence persistence, and progressive context management.

Autonomy Principle: Operate independently. Infer assumptions from context. Only stop for critical errors or incomprehensible queries.

Accessibility Principle: Make dense analysis easier to grasp. When a finding is abstract, architectural, process-heavy, or strategically subtle, ground it in a concrete example, mini-case, counterexample, implementation scenario, or failure mode. Prefer real source-grounded cases; if you use an illustrative hypothetical, label it clearly as hypothetical and do not cite it as if it were sourced fact.


Decision Tree

Request Analysis
+-- Simple lookup? --> STOP: Use WebSearch
+-- Debugging? --> STOP: Use standard tools
+-- Complex analysis needed? --> CONTINUE

Mode Selection
+-- Initial exploration --> quick (3 phases, 2-5 min)
+-- Standard research --> standard (6 phases, 5-10 min) [DEFAULT]
+-- Critical decision --> deep (8 phases, 10-20 min)
+-- Comprehensive review --> ultradeep (8+ phases, 20-45 min)

Default assumptions: Technical query = technical audience. Comparison = balanced perspective. Trend = recent 1-2 years.

Default readability rule: For Standard mode and above, try to include at least one concrete case/example for each major finding unless the topic is purely numeric, the source material provides no credible case material, or confidentiality/safety considerations make cases inappropriate.


Workflow Overview

PhaseNameQuickStandardDeepUltraDeep
1SCOPEYYYY
2PLAN-YYY
3RETRIEVEYYYY
4TRIANGULATE-YYY
4.5OUTLINE REFINEMENT-YYY
5SYNTHESIZE-YYY
6CRITIQUE--YY
7REFINE--YY
8PACKAGEYYYY

Execution

On invocation, load relevant reference files:

  1. Phase 1-7: Load methodology.md for detailed phase instructions
  2. Phase 8 (Report): Load report-assembly.md for progressive generation
  3. HTML/PDF output: Load html-generation.md
  4. Quality checks: Load quality-gates.md
  5. Long reports (>18K words): Load continuation.md

Templates:

Scripts:

  • python scripts/validate_report.py --report [path]
  • python scripts/verify_citations.py --report [path]
  • python scripts/md_to_html.py [markdown_path]

Output Contract

Required sections:

  • Executive Summary (200-400 words)
  • Introduction (scope, methodology, assumptions)
  • Main Analysis (4-8 findings, 600-2,000 words each, cited, each grounded in concrete examples/cases when helpful)
  • Synthesis & Insights (patterns, implications)
  • Limitations & Caveats
  • Recommendations
  • Bibliography (COMPLETE - every citation, no placeholders)
  • Methodology Appendix

Output files (all to ~/Documents/[Topic]_Research_[YYYYMMDD]/):

  • Markdown (primary source)
  • HTML (McKinsey style, auto-opened)
  • PDF (professional print, auto-opened)

Quality standards:

  • 10+ sources, 3+ per major claim
  • All claims cited immediately [N]
  • No placeholders, no fabricated citations
  • Prose-first (>=80%), bullets sparingly
  • Use concrete examples, mini-cases, counterexamples, or applied scenarios to make major findings easier to understand
  • Source-ground real cases when available; clearly label hypothetical illustrative examples instead of citing them as factual evidence

When to Use / NOT Use

Use: Comprehensive analysis, technology comparisons, state-of-the-art reviews, multi-perspective investigation, market analysis.

Do NOT use: Simple lookups, debugging, 1-2 search answers, quick time-sensitive queries.

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 Deep Research AI skill do?

Conducts enterprise-grade research with multi-source synthesis, citation tracking, and verification. Produces citation-backed reports through a structured pipeline with source credibility scoring, and grounds abstract findings in concrete examples, cases, counterexamples, or mini-scenarios when helpful. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.

Why use Deep Research on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Undertone0809/rudder/tree/main/server/resources/community-skills/deep-research. 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 Deep Research?

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 Research?

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

Is the Deep Research AI skill free?

Yes. It is published on GitHub by Undertone0809 under the Apache-2.0 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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