paseo-committee

Coordinate two AI agents to perform root-cause analysis and produce a unified plan.

Updated Apr 25, 2026

Search Tags:#workflow #planning #multi-agent #decision-making #root-cause-analysis #committee #paseo

Ready to run

npx skills add https://github.com/KQDtianxiaK/AtHand --skill paseo-committee-kqdtianxiak

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: paseo-committee
Source: https://github.com/KQDtianxiaK/AtHand/tree/main/paseo-main/skills/paseo-committee
Command: npx skills add https://github.com/KQDtianxiaK/AtHand --skill paseo-committee-kqdtianxiak

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinates two high-reasoning AI agents to step back from a problem and produce a robust plan.

Core Features & Use Cases

Parallel planning: two agents (Opus 4.6 and GPT 5.4) are launched in parallel to generate a plan.
Root-cause analysis and drift checking: they perform in-depth reasoning and maintain a shared plan for Phase 3 review to catch drift.
Multi-phase workflow: plan → implement → review with consolidated synthesis and user confirmation.

Quick Start

Launch both committee members with your prompt and wait for convergence.

Frequently Asked Questions about paseo-committee

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

FAQPage Schema

How do I use multi-agent planning to break out of a stalled workflow loop?▼

Multi-agent planning breaks stalled workflow loops by coordinating two high-reasoning AI agents to perform root-cause analysis and synthesize a unified plan. This approach enforces phase gating across planning, implementation, and review to catch drift and resolve looping cycles.

What is root-cause analysis in multi-agent decision-making?▼

Root-cause analysis in multi-agent decision-making is a process where parallel AI agents perform in-depth reasoning to step back from a problem. They maintain a shared plan to generate a robust, consolidated strategy for hard planning problems.

How do I coordinate parallel AI agents for complex planning tasks?▼

You coordinate parallel AI agents for complex planning tasks by launching multiple high-reasoning models simultaneously with your prompt. The system enforces phase gating and synthesizes their distinct outputs into a merged plan requiring user confirmation before implementation.

When should I use a committee approach instead of single-agent planning?▼

You should use a committee approach instead of single-agent planning for hard planning problems, stuck work, or looping cycles where single-agent approaches stall. It provides parallel planning and consolidated review to catch drift and resolve complex issues.

Can I apply multi-agent workflow planning to resolve stuck work?▼

Yes, you can apply multi-agent workflow planning to resolve stuck work by launching two committee members in parallel. They perform in-depth reasoning and drift checking to produce a robust, synthesized plan with user confirmation for hard planning problems.

What are the limitations of using a multi-agent committee for workflow planning?▼

A limitation of using a multi-agent committee for workflow planning is the requirement to wait for convergence between parallel agents. The enforced phase gating across plan, implement, and review stages demands user confirmation, which may add overhead to straightforward tasks.