ai-pcli

Name: ai-pcli
Availability: InStock
Author: enduser123

Orchestrate parallel LLM CLI queries across qwen, gemini, codex, and opencode backends.

Updated Apr 26, 2026

Search Tags:#routing #cli #llm #orchestration #aggregation #parallel #prompt-toolkit

Ready to run

npx skills add https://github.com/EndUser123/cc-marketplace --skill ai-pcli

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-pcli
Source: https://github.com/EndUser123/cc-marketplace/tree/main/plugins/cc-skills-ai-cli/skills/ai-pcli
Command: npx skills add https://github.com/EndUser123/cc-marketplace --skill ai-pcli

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, python-dotenv, and includes scripts (resource) components.

What problem does it solve?

Parallel multi-LLM CLI orchestration speeds up decision making by running multiple CLI-backed model providers in parallel and surfacing a unified view.

Core Features & Use Cases

Parallel execution of multiple LLM CLIs (qwen, gemini, codex, opencode) to reduce latency and provide diverse perspectives.
Automatic aggregation of outputs with optional critique pass (ai-cli-critic) to validate claims and consistency.
Context-aware prompting and routing to select appropriate CLIs for given queries.

Quick Start

Provide a query to /ai-pcli to run all configured CLIs in parallel and aggregate their outputs.

Frequently Asked Questions about ai-pcli

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

FAQPage Schema

How do I run multiple LLM CLIs in parallel to compare outputs?▼

Parallel LLM CLI orchestration runs queries across multiple CLI-backed model providers simultaneously, aggregating their outputs into a unified view to speed up decision making. You provide a query and the system executes it across configured backends like qwen, gemini, and codex.

What is an optional critic pass for aggregated LLM outputs?▼

An optional critic pass validates the aggregated outputs from parallel LLM execution. It applies an ai-cli-critic to check claims and consistency, ensuring the quality of the unified response generated by the multiple backend models.

Can I route specific queries to certain LLM CLIs based on context?▼

Yes, context-aware prompting and routing rules allow you to select appropriate CLIs for given queries. This ensures specific queries are directed to the most suitable backend among the configured providers for optimal results.

Does parallel LLM orchestration support JSON or structured outputs?▼

Yes, parallel LLM CLI orchestration supports JSON and structured outputs when requested. This allows you to receive aggregated responses from multiple model backends in a structured format for programmatic use.

What dependencies do I need to set up LLM CLI orchestration?▼

You need pyyaml and python-dotenv installed in your environment to configure and run the parallel LLM CLI orchestration. These dependencies handle configuration loading and environment variable management for the backend routing.