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jitenkr2030
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deep-research

Generate source-grounded research reports with multi-source verification and citation tracking.

Updated Feb 7, 2026
Search Tags:#automation#validation#multi-source#GLM#deep-research#citation-management#source-credibility
One-click install
npx skills add https://github.com/jitenkr2030/AutoReel-AI --skill deep-research-jitenkr2030
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/jitenkr2030/AutoReel-AI/tree/main/skills/deep-research
Command: npx skills add https://github.com/jitenkr2030/AutoReel-AI --skill deep-research-jitenkr2030

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires , and includes scripts (resource) components.

What problem does it solve?

This Skill enables autonomous, source-grounded deep research reports with multi-source verification. It reduces manual effort by automating source collection, triangulation of claims across diverse sources, and validation against credibility criteria. The workflow supports progressive context loading, automated validation, and a structured, citation-backed output suitable for executive reports.

Core Features & Use Cases

  • 8-phase pipeline: Scope → Plan → Retrieve → Triangulate → Synthesize → Critique → Refine → Package for rigorous analysis.
  • Multi-depth modes: Quick, Standard, Deep, and UltraDeep to balance speed and depth for a wide range of tasks.
  • Credible outputs: Built-in source credibility scoring, automated citation tracking, and validation.
  • Autonomous execution: Self-contained, local execution with progressive context management and no required human prompting.
  • Use cases: Produce enterprise-grade research reports, competitive analyses, or technology reviews with full bibliographies.

Quick Start

  • Clone or install the skill into your environment, then run the CLI or API to trigger deep research.
  • Example CLI: python {skill_path}/scripts/research_engine.py --query "state of GLM deep research in 2025" --mode standard
  • The tool saves outputs to {skill_path}/research_output/ and generates a Markdown report along with optional HTML/PDF formats.

Frequently Asked Questions about deep-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate multi-source research with citation management and validation?▼

The deep research pipeline works by executing an 8-phase workflow: scope, plan, retrieve, triangulate, synthesize, critique, refine, and package, applying automated validation to produce citation-backed reports.

Can I run autonomous deep research locally without manual prompting?▼

Yes, you can run autonomous deep research locally without manual prompting using the self-contained Python execution environment, which manages progressive context loading and retrieval autonomously.

How do I balance research speed and depth for different analysis tasks?▼

You balance research speed and depth by selecting from multiple modes including Quick, Standard, Deep, and UltraDeep, allowing you to adjust the rigor of retrieval and synthesis based on task requirements.

Does this research automation tool produce reports suitable for executive review?▼

Yes, this tool generates executive-grade reports by synthesizing triangulated information into structured Markdown with optional HTML and PDF formats, including full bibliographies.

What is source triangulation and how does it validate research credibility?▼

Source triangulation validates research credibility by cross-referencing claims across diverse retrieved sources, applying automated credibility scoring to ensure synthesized information meets verification criteria.

What are the limitations of using autonomous research pipelines for literature reviews?▼

Limitations of autonomous research pipelines include reliance on local execution environments and Python dependencies, meaning complex literature reviews require proper environment setup and cannot run without script dependencies.

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