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deep-research

Automate iterative deep research loops and generate cited reports with visualizations.

12|2|Updated Apr 21, 2026
Search Tags:#research#reporting#visualization#evidence#recursion#iterative-search#ipython
One-click install
npx skills add https://github.com/haomingz/kimi-skills --skill deep-research-haomingz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/haomingz/kimi-skills/tree/main/skills/deep-research
Command: npx skills add https://github.com/haomingz/kimi-skills --skill deep-research-haomingz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Exhaustive, evidence-based deep research is often manual, time-consuming, and hard to audit. This skill automates a disciplined research protocol that enforces iterative exploration, rigorous citation, and structured long-form reporting.

Core Features & Use Cases

  • Iterative search loops with at least 10 rounds to maximize coverage and reduce blind spots.
  • Recursive reflection after each round with concise thinking and summary outputs to guide the next steps.
  • IPython-powered visualizations embedded in final reports for quantitative support, trend analysis, and reproducibility.
  • Use Case: Researchers compiling a comprehensive literature review with traceable sources and reproducible figures.

Quick Start

Initiate the deep research protocol and perform at least 10 search iterations to generate the final, cited report.

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I automate evidence-based research with iterative search and citation tracking?▼

Automated evidence-based research enforces iterative search loops with at least 10 rounds and recursive reflection after each iteration, ensuring comprehensive coverage and citation-ready sourcing for the final report.

How do I generate long-form research reports with IPython visualizations?▼

Generate long-form research reports with IPython-powered visualizations embedded directly for quantitative support and trend analysis, outputting a final structured report.md file with frontmatter and reproducible figures.

What is recursive reflection in deep research workflows?▼

Recursive reflection in deep research workflows generates concise thinking and summary outputs after each iterative search round, guiding subsequent exploration steps to reduce blind spots and maximize source coverage.

Can I use automated research workflows for compiling a comprehensive literature review?▼

Yes, automated research workflows support compiling comprehensive literature reviews by enforcing rigorous citation, iterative exploration, and reproducible figures to ensure traceable sources throughout the final output.

What are the limitations of iterative search loops for exhaustive research?▼

Iterative search loops for exhaustive research require at least 10 rounds to maximize coverage, meaning the process demands significant time and outputs are strictly constrained to a deterministic report.md file with enforced frontmatter.

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