pi-cli

Name: pi-cli
Availability: InStock
Author: mkSteady

Run unified project analysis with DAG scheduling, LLM analysis, and dashboard UI.

9|1|Updated Jan 17, 2026

Search Tags:#documentation #cli #audit #analysis #llm #dependency-graph #project-index

Ready to run

npx skills add https://github.com/mkSteady/mkSkills --skill pi-cli

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pi-cli
Source: https://github.com/mkSteady/mkSkills/tree/main/pi-cli
Command: npx skills add https://github.com/mkSteady/mkSkills --skill pi-cli

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires codeagent-wrapper, kanban.

What problem does it solve?

pi-cli provides a single entry point to run project analysis tasks, streamline dependencies, testing, documentation, and audits, and present results in a Web Dashboard.

Core Features & Use Cases

DAG-based scheduling for parallel analysis across multiple modules
LLM-driven module analysis and automated document/audit generation
Web dashboard for real-time status, metrics, and task management
Safe, configurable workflows with .pi-config.json and .stale-config.json

Quick Start

Initialize the configuration with pi init, build the dependency graph with pi deps build, analyze modules with pi module analyze --llm, and start the dashboard with pi ui.

Frequently Asked Questions about pi-cli

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

FAQPage Schema

How do I automate project analysis and dependency graph generation for a multi-module codebase?▼

Automate project analysis by initializing with pi init, building dependency graphs, and scheduling parallel LLM-driven module analysis. This unified CLI workflow scales from small repos to large multi-module codebases of any language.

What is LLM-driven module analysis and how does it generate project documentation?▼

LLM-driven module analysis uses large language models to evaluate project modules and automatically generate documentation or audit reports. It integrates with DAG scheduling to process modules in parallel and persists results in a configurable cache.

Does this project analysis CLI support any programming language and large scale repositories?▼

Yes, this project analysis CLI targets software projects of any programming language. It scales from small repositories to large multi-module codebases by utilizing DAG scheduling and configurable workflows to enforce safe operations.

How do I configure safe workflows and manage task status for codebase audits?▼

Configure safe workflows using .pi-config.json and .stale-config.json files. You can manage audit task status through a web dashboard UI that provides real-time metrics, background updates, and Kanban integration.

Can I visualize real-time analysis metrics and manage tasks through a web dashboard?▼

Yes, you can start the web dashboard using the ui command to visualize real-time analysis metrics, manage tasks, and monitor module analysis status. The dashboard orchestrates background updates and integrates with Kanban boards.

What are the limitations of using DAG scheduling for parallel module analysis?▼

DAG scheduling for parallel module analysis requires building dependency graphs and relies on configurable caches to enforce safe operations. Complex dependency structures may require manual configuration tuning to avoid stale analysis results.