Deep Research Agent logo

Deep Research Agent

Organization
shobcoder
deep-research-agent

Comprehensive research agent for in-depth investigation. Use when users ask for deep research, comprehensive analysis, market research, academic surveys, competitive analysis, technology trends, or any topic requiring 100+ source verification. Triggers on requests like "investigate", "research", "analyze", "create a report", "comprehensive report", "deep dive", "thorough analysis".

Overview

Publishershobcoder
Repositoryshob
Skill namedeep-research-agent
Stars
576
Forks
3
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 shobcoder on GitHub. Read the source before you install it.

Installation

Install the Deep Research Agent 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/shobcoder/shob/tree/main/skills/deep-research-agent
  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/shobcoder/shob.git /tmp/shob
mkdir -p .claude/skills
cp -r /tmp/shob/skills/deep-research-agent .claude/skills/deep-research-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep Research Agent 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 Agent 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 Agent 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.

DeepResearch Agent

Autonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.

Core Workflow

Phase 1: Query Decomposition & Planning

Input: User's research query (natural language)

Process:

  1. Analyze the query intent

    • Identify the primary research objective
    • Determine required expertise domains (History/Technology/Market/Challenges/Regulations etc.)
    • Assess depth requirements (surface-level vs comprehensive)
  2. Generate multi-dimensional search queries

    • Historical context queries (when applicable)
    • Technical specification queries
    • Market/industry trend queries
    • Challenge/pain point queries
    • Regulatory/compliance queries (if applicable)
    • Future outlook/prediction queries
  3. Build investigation roadmap

    • Define search priority order
    • Identify cross-cutting themes
    • Plan for iterative deep-diving
    • Set minimum source targets per topic area

Output: research_plan object containing:

json
{
  "primary_topic": "string",
  "sub_topics": ["string"],
  "search_queries": [{"query": "string", "domain": "string", "priority": 1}],
  "target_sources": 100,
  "timeline_phases": ["phase1", "phase2", "phase3"]
}

Phase 2: Autonomous Information Gathering

Tools Used: batch_web_search, extract_content_from_websites

Process:

  1. Initial breadth search

    • Execute parallel searches across all primary query dimensions
    • Gather minimum 20-30 URLs per major topic area
    • Prioritize authoritative sources (official docs, academic, established media)
  2. Source classification

    • Categorize by source type: News, Academic Papers, Whitepapers, Technical Documentation, Forums, Blogs
    • Assess domain authority and reliability
    • Flag sources requiring deeper analysis
  3. Iterative deep-diving

    • Extract key terms and concepts from initial results
    • Generate follow-up queries using discovered terminology
    • Expand search to related topics and subtopics
    • Loop until saturation (no new significant information)
  4. Diverse source coverage

    • Ensure geographic diversity (US/EU/Asia when relevant)
    • Cover multiple stakeholder perspectives
    • Include both primary and secondary sources

Target: Minimum 100 unique, verified sources

Phase 3: Content Reading & Reasoning

Tools Used: extract_content_from_websites, extract_pdfs_key_info

Process:

  1. Content extraction

    • Access each promising URL
    • Extract structured information: facts, statistics, quotes, dates, claims
    • Parse PDF documents for detailed data
  2. Relevance assessment

    • Score content against research objectives (1-5 scale)
    • Filter out low-relevance or duplicate content
    • Prioritize high-value sources for deep analysis
  3. Information extraction matrix

    For each source:
    - Source metadata (title, author, date, URL)
    - Key findings (bullet points)
    - Supporting evidence (quotes, statistics)
    - Contradicting information (if any)
    - Confidence level (high/medium/low)
  4. Pattern recognition

    • Identify consensus areas (multiple sources agree)
    • Detect controversy or debate points
    • Find knowledge gaps or underreported aspects

Phase 4: Verification & Gap Filling

Process:

  1. Cross-verification protocol

    • Check consistency across independent sources
    • Verify statistics with multiple citations
    • Confirm quotes with original context
  2. Contradiction resolution

    • Document conflicting information
    • Assess source credibility differences
    • Note the nature of disagreement (factual vs interpretive)
    • Present multiple perspectives when resolution impossible
  3. Gap identification

    • Compare gathered information against research plan
    • Identify missing perspectives or outdated information
    • Flag areas needing additional primary source verification
  4. Iteration loop (if gaps identified)

    • Return to Phase 2 with targeted queries
    • Focus on specific missing elements
    • Repeat until research objectives are satisfied

Phase 5: Structured Report Synthesis

Output Format: Comprehensive research report

Structure:

# [Research Title]

## Executive Summary
[2-3 paragraph overview of key findings]

## 1. Background and Purpose
[Context and research motivation]

## 2. Key Findings

### 2.1 [Topic Area 1]
#### Facts and Data
#### Analysis and Interpretation
#### Sources

### 2.2 [Topic Area 2]
... (repeat for all sub-topics)

## 3. Market Trends and Future Outlook
[Aggregated trends and predictions]

## 4. Challenges and Risks
[Identified challenges with evidence]

## 5. Opportunities and Recommendations
[Actionable insights]

## 6. List of Sources
[All 100+ sources in academic citation format]

## Appendix
[Supplementary data, tables, charts]

Quality Standards:

  • Every factual claim MUST have inline citation [source_id]
  • Source attribution format: [1] Title, Publisher/Site, Publication Date, URL
  • Minimum 100 unique sources required
  • Use tables for statistical comparisons
  • Include key quotes with proper attribution
  • Mark uncertain information with confidence indicators

Execution Guidelines

Parallel Execution Strategy

  • Run independent searches in parallel (up to 10 concurrent queries)
  • Process multiple content extractions simultaneously
  • Batch similar operations for efficiency

Quality Thresholds

  • Source minimum: 100 unique URLs successfully extracted
  • Citation minimum: 100 inline references in final report
  • Content relevance: Average score >= 3.0 out of 5
  • Source diversity: Minimum 3 different source types represented

Error Handling

  • Failed URLs: Log and skip, continue with alternative sources
  • Contradictory info: Document and present both perspectives
  • Insufficient coverage: Extend search phase until threshold met
  • Verification failures: Flag claims as unverified in final report

Progress Tracking

Maintain research log with:

  • Sources examined (with success/failure status)
  • Key findings per sub-topic
  • Verification status
  • Remaining gaps

Example Research Queries

This skill excels at:

  • "Investigate the latest trends in AI technology using 100+ sources"
  • "Electric vehicle market trends 2024 comprehensive analysis"
  • "Research on industrial applications of quantum computing"
  • "Sustainable energy transition analysis with 100+ sources"
  • "Create a comprehensive market research report on [any specialized field]"

Constraints

  • Time budget: Allow sufficient iteration time for 100+ source verification
  • Source validation: All statistics must have minimum 3 source verification
  • Bias awareness: Include diverse perspectives, not just mainstream views
  • Currency: Prioritize recent sources (within 2 years) for current topics
  • Language: Support English and other major languages as needed

Frequently asked questions

What does the Deep Research Agent AI skill do?

Comprehensive research agent for in-depth investigation. Use when users ask for deep research, comprehensive analysis, market research, academic surveys, competitive analysis, technology trends, or any topic requiring 100+ source verification. Triggers on requests like "investigate", "research", "analyze", "create a report", "comprehensive report", "deep dive", "thorough analysis".

Why use Deep Research Agent on TypingMind?

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

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

Which AI models can use Deep Research Agent?

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 Agent?

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

Is the Deep Research Agent AI skill free?

Yes. It is published on GitHub by shobcoder 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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