pairl-claude-skill
NewClaude Code skill for PAIRL v1.1 - Protocol for Agent Intermediate Representation (Lite)
Summary
1, enabling structured communication between AI agents.
- It standardizes how agents exchange intermediate reasoning, task delegation, and results, making multi-agent workflows more reliable and debuggable.
Install & Usage
mkdir -p .claude/agentsAdd the configuration to .claude/agents/pairl-claude-skill.md
@pairl-claude-skillUse Cases
Usage Examples
/pairl-claude-skill Create a PAIRL workflow that splits a research task into three sub-tasks: gather data, analyze trends, and summarize findings.
Use PAIRL to delegate the task 'Write a Python script to parse CSV files' to a code-generation agent and receive the result as a structured PAIRL message.
Set up a multi-agent pipeline where Agent A generates a list of keywords, Agent B searches the web for each keyword, and Agent C compiles the results into a report, all using PAIRL messages.
Security Audits
Frequently Asked Questions
What is pairl-claude-skill?
The PAIRL Claude Code skill implements the Protocol for Agent Intermediate Representation (Lite) v1.1, enabling structured communication between AI agents. It standardizes how agents exchange intermediate reasoning, task delegation, and results, making multi-agent workflows more reliable and debuggable.
How to install pairl-claude-skill?
To install pairl-claude-skill: create the agents directory (mkdir -p .claude/agents), then add the config to .claude/agents/pairl-claude-skill.md. Finally, @pairl-claude-skill in Claude Code.
What is pairl-claude-skill best for?
pairl-claude-skill is a agent categorized under General. It is designed for: agent, claude-code, claude-code-skill, pairl, agent-communication, agent-protocol, multi-agent, llm.
What can I use pairl-claude-skill for?
pairl-claude-skill is useful for: Decompose a complex task into sub-tasks and delegate them to specialized agents using PAIRL messages.; Log and inspect intermediate reasoning steps from multiple agents to debug coordination issues.; Chain multiple LLM calls where each agent's output is passed as structured input to the next agent.; Implement a supervisor agent that coordinates worker agents via PAIRL protocol for parallel task execution.; Integrate with existing agent frameworks that support PAIRL to enable cross-platform agent communication..