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.