Research Skill (Codex) — Step 2: Forked Agent Research

Merges parallel Codex agent research into requirement option sets.

Updated May 27, 2026
One-click install
npx skills add https://github.com/ormastes/Spipe --skill research-skill-codex-step-2-forked-agent-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Research Skill (Codex) — Step 2: Forked Agent Research
Source: https://github.com/ormastes/Spipe/tree/main/doc/00_llm_process/skill_command/skills/pipe/research/research_codex
Command: npx skills add https://github.com/ormastes/Spipe --skill research-skill-codex-step-2-forked-agent-research

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

You need deeper, reliable research than a single agent can provide, and you must turn that research into clear requirement option sets without rework.

Core Features & Use Cases

  • Parallel Forked Research: Runs three focused Codex agent roles (alternative approaches, requirement validation, risk analysis) to cover both breadth and rigor.
  • Consolidated Output: Merges findings into a consolidated codex research artifact for each feature.
  • Requirement Option Sets: Produces 2–3 user-selectable requirement option sets derived from the research results.

Quick Start

Ask your AI pipeline to run the Research Skill (Codex) Step 2 for a given feature to generate consolidated codex research and requirement options from the Step 1 local and domain inputs.

Frequently Asked Questions about Research Skill (Codex) — Step 2: Forked Agent Research

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

FAQPage Schema
How do I validate feature requirements using parallel AI agents?▼

To validate feature requirements, you can fork parallel Codex agents to investigate alternative approaches, requirement validation, and risk analysis concurrently. This produces a consolidated codex research artifact for deeper reliability.

What is the best way to generate requirement option sets from feature research?▼

Generating requirement option sets involves running parallel agent roles to analyze feature research and output 2-3 distinct, user-selectable options. This eliminates rework by deriving choices directly from consolidated findings.

How does forked agent research work in a multi-LLM cooperative pipeline?▼

Forked agent research works by reading Step 1 local and domain research inputs, then running three focused Codex agent roles in parallel. Results merge into a consolidated codex research artifact and requirement option sets.

Can I use Codex agents for feature analysis if Step 1 outputs are missing?▼

You cannot use this pipeline step if Step 1 outputs are missing, because the forked agents require reading prior local and domain research inputs to generate consolidated codex research and requirement options.

Why use forked parallel agents instead of a single agent for requirement validation?▼

Using forked parallel agents provides deeper, reliable research by covering breadth and rigor across alternatives, validation, and risk simultaneously. A single agent cannot match this consolidated depth without rework.

What limitations exist when running forked agents for codex research?▼

Limitations include requiring existing Step 1 outputs in both local and domain research folders, and dependency on a multi-LLM cooperative pipeline. It cannot generate requirement options without these specific input paths.