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statsclaw-protocol

Orchestrate specialized AI agents for statistical package development workflows.

89|17|Updated Mar 27, 2026
Search Tags:#workflow automation#agent orchestration#code validation#monte carlo simulation#ai agent teams#statistical package development#research software
One-click install
npx skills add https://github.com/statsclaw/statsclaw --skill statsclaw-protocol
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsclaw-protocol
Source: https://github.com/statsclaw/statsclaw/tree/main/skills/statsclaw-protocol
Command: npx skills add https://github.com/statsclaw/statsclaw --skill statsclaw-protocol

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the complex manual work of coordinating multiple specialized AI agents for end-to-end statistical package development, handling workflow orchestration, strict pipeline isolation, state management, and validation gates so you can focus on domain expertise instead of process management.

Core Features & Use Cases

  • Multi-Agent Orchestration Protocol: Defines the full workflow for coordinating 9 specialized AI agents (leader, planner, builder, tester, simulator, scriber, distiller, reviewer, shipper) to handle every stage of statistical package work from planning to shipping.
  • Isolated Multi-Pipeline Architecture: Enforces strict separation between code, test, and simulation pipelines to enable adversarial verification, drastically reducing the risk of overlooked errors in statistical software.
  • Use Case: A statistical researcher can trigger a full bug fix, feature implementation, or Monte Carlo simulation study for their R or Python statistical package with a single natural language prompt, with all validation, documentation, and PR creation handled automatically.

Quick Start

Use the statsclaw-protocol skill to fix issue #42 in your statistical package by typing "fix fect issue #42" in your Claude Code session.

Frequently Asked Questions about statsclaw-protocol

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

FAQPage Schema
How do I automate statistical package development workflows with AI agents?▼

Automating statistical package development is handled by orchestrating specialized AI agent teams that manage the entire workflow, from planning and bug fixes to validation and shipping, across R and Python environments. This eliminates complex manual coordination so you can focus on domain expertise.

What is the best way to run Monte Carlo simulations for an R or Python statistical package?▼

Running Monte Carlo simulation studies is achieved by deploying a specialized simulator agent within an isolated pipeline, ensuring strict separation from code and test workflows. This architecture enforces mandatory validation gates to guarantee correctness and reproducibility of statistical software changes.

Can I use AI agent teams to fix bugs and implement features across different statistical programming languages?▼

Yes, AI agent teams can handle bug fixes, feature implementation, and cross-language translation for statistical software packages across R, Python, and other languages. A leader agent orchestrates nine specialized agents to complete the workflow from planning to automatic PR creation.

Do I need to manually coordinate validation and documentation updates when changing statistical software?▼

No, manual coordination is not needed because the workflow enforces mandatory validation gates and automatic documentation updates through dedicated scriber and reviewer agents. This strict pipeline isolation drastically reduces the risk of overlooked errors in statistical packages.

How does pipeline isolation work during statistical package testing and code validation?▼

Pipeline isolation enforces strict separation between code, test, and simulation workflows to enable adversarial verification. This architecture ensures that validation gates are mandatory before any statistical software changes are shipped, drastically reducing the risk of overlooked errors.

What are the limitations of using automated agent orchestration for research software development?▼

The primary limitation of this automated agent orchestration is its strict dependency on pipeline isolation and mandatory validation gates, which may introduce overhead for rapid prototyping. It is specifically tailored for research software requiring high correctness and reproducibility rather than quick, unvalidated code changes.

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