deep-research

Orchestrate an 8-phase Python research pipeline with credibility scoring.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/junkijin/my-opencode --skill deep-research-junkijin
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/junkijin/my-opencode/tree/main/skills/deep-research
Command: npx skills add https://github.com/junkijin/my-opencode --skill deep-research-junkijin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill automates deep, citation-backed research by orchestrating multi-source collection, validation, and synthesis to deliver credible reports, reducing manual hours and cognitive load.

Core Features & Use Cases

  • 8-phase pipeline: Scope → Plan → Retrieve → Triangulate → Synthesize → Critique → Refine → Package with built-in validation and credibility scoring.
  • Source credibility scoring: Quantifies each citation from 0-100 and flags potential biases.
  • Autonomous operation: Default to plan and execute with minimal user prompts; optional graceful escalation when needed.
  • Progressive context management: Caches static context and loads dynamic content on demand to optimize latency.
  • Multiple depth modes: Quick, Standard, Deep, and UltraDeep to balance speed and thoroughness.
  • Self-contained, offline-friendly: Pure Python stdlib usage with deterministic outputs and local storage for reports.
  • Structured outputs: Markdown reports with Bibliography, Methodology Appendix, and validation status; HTML/PDF variants available.
  • Extensive validation: Automated report validation with 8 checks and a target of 10+ sources per report.

Quick Start

  • Provide a concise instruction to run the quick-start: Use the quick start or CLI to run the module with a sample query.

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I generate a citation-backed research report autonomously?

To generate a citation-backed research report autonomously, provide a complex topic query and the skill runs an 8-phase pipeline to gather, validate, and synthesize sources into a Markdown report. It defaults to planning and executing with minimal user prompts.

What is source credibility scoring in automated research?

Source credibility scoring in automated research quantifies each citation from 0-100 to evaluate reliability and flags potential biases. This validation ensures synthesized reports maintain rigorous verification standards across all gathered sources.

Can I run deep research workflows offline using only Python?

You can run deep research workflows offline using pure Python standard library modules. The self-contained script operates deterministically without external dependencies and stores generated reports locally.

What is the best way to handle complex topics requiring 10+ sources?

The best way to handle complex topics requiring 10+ sources is using the Deep or UltraDeep depth modes. These modes balance speed and thoroughness while orchestrating multi-source triangulation and synthesis for rigorous verification.

Does deep research output include a methodology appendix and bibliography?

Deep research output includes structured Markdown reports with a bibliography, methodology appendix, and validation status. HTML and PDF variants are also available for export.

Why does autonomous research use progressive context loading?

Autonomous research uses progressive context loading to optimize latency by caching static context and loading dynamic content on demand. This ensures efficient processing during the retrieval and synthesis phases.