deep-research

Perform multi-source technical research with cross-source validation and synthesized outputs.

2|1|Updated Mar 4, 2026
One-click install
npx skills add https://github.com/cncoder/oneclaw --skill deep-research-cncoder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/cncoder/oneclaw/tree/main/skills/deep-research
Command: npx skills add https://github.com/cncoder/oneclaw --skill deep-research-cncoder

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a structured approach to perform rigorous, multi-source technical research, producing verified, actionable intelligence to support technology decisions.

Core Features & Use Cases

  • Systematic scoping and boundary definition to clarify research questions.
  • Multi-source aggregation and cross-validation across at least three sources for robust conclusions.
  • Clear synthesis templates and outputs to support decision making in evaluations, incident investigations, or requirements gathering.

Quick Start

Ask for a structured research plan and sources to support a tech decision.

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I conduct multi-source technical research for technology evaluation?▼

Multi-source technical research requires systematic scoping to define boundaries, aggregating documentation, and cross-validating findings across at least three sources to produce verified, actionable intelligence for technology evaluation decisions.

What is the best way to gather requirements from dispersed technical documentation?▼

Gathering requirements from dispersed documentation uses a structured research workflow with formal templates to extract, synthesize, and cross-validate data, ensuring robust conclusions for decision support.

Can I use structured research to support incident investigations?▼

Structured research supports incident investigations by applying rigorous multi-source verification to dispersed data, extracting actionable intelligence to clarify root causes and inform resolution decisions.

Does this approach work for comparing different technology solutions?▼

This approach works for technology comparisons by defining research scope, aggregating multi-source data, and cross-validating findings to synthesize clear outputs that directly support solution selection decisions.

How many sources do I need for reliable cross-validation in technical research?▼

Reliable cross-validation in technical research requires aggregating and verifying data across at least three sources, ensuring robust conclusions and reducing bias in synthesized decision-support outputs.

When do I need formal synthesis templates for research outputs?▼

Formal synthesis templates are needed when research outputs must directly support technology decisions, ensuring findings from multi-source verification are structured into actionable intelligence for evaluations or incident investigations.