A rigorous inter-agent communication framework that defines how C-suite AI roles collaborate, invoke each other, and verify data without circular logic.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cs-agent-protocol
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install cs-agent-protocol using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Inter-Agent Protocol?
The Inter-Agent Protocol is a specialized framework within the Openclaw Skills ecosystem designed for high-stakes C-suite agent orchestration. It establishes formal rules for how executive AI agents—such as the CEO, CFO, and CTO—query one another and coordinate cross-functional analysis. By implementing strict invocation syntax and response structures, it transforms chaotic multi-agent interactions into a polished, professional board meeting environment.
This skill is critical for ensuring that AI-driven organizations maintain clarity and accountability. It enforces logical boundaries that prevent groupthink and anchoring, ensuring each agent provides independent, data-backed insights before cross-pollinating ideas. With this protocol, developers can build complex agentic workflows that deliver decision-ready outputs to human founders without the noise of raw processing.
Inter-Agent Protocol Use Cases
- Coordinating board meetings between multiple AI agent roles to reach a strategic consensus.
- Running cross-functional impact analysis, such as calculating the burn rate impact of hiring plans.
- Facilitating independent peer verification where a CFO agent validates the math of a CTO agent.
- Implementing proactive alerting systems where agents notify the founder of high-risk business anomalies.
- Resolving data conflicts between different specialized domains using established weighting or escalation rules.
How Inter-Agent Protocol Works
- An agent initiates an inquiry using the standard
[INVOKE:role|question]syntax to request specific domain data. - The system checks the invocation against loop prevention rules, ensuring the call depth is not greater than 2 and no circular calls exist.
- The invoked agent generates a response following a structured format that includes key findings, supporting data, confidence scores, and caveats.
- During high-stakes scenarios, the protocol enters isolation phases where agents must state assumptions instead of invoking others to prevent anchoring.
- The final output is routed through a multi-step verification loop (Self-Verify, Peer-Verify, and Critic Pre-Screen) before being presented to the user.
Inter-Agent Protocol Setup
To integrate this protocol into your multi-agent environment, include the following configuration in your Openclaw Skills directory:
# Clone the skill into your local agent library
git clone https://github.com/openclaw/skills/agent-protocol.git
# Reference the protocol in your agent definition files
cp agent-protocol/protocol-rules.md ./agents/system-prompts/
Ensure your company-context.md is updated to allow agents to reference the valid C-suite roles defined in the protocol.
Inter-Agent Protocol Data Schema & Taxonomy
The protocol organizes data through specific tags and tracking headers to maintain chain integrity:
| Tag | Purpose | Description |
|---|---|---|
[CHAIN] |
Traceability | Records the path of agent calls (e.g., cro -> cfo -> coo). |
[RESPONSE] |
Data Payload | Wraps the findings, confidence level, and caveats. |
[ASSUMPTION] |
Uncertainty | Flags explicit assumptions used when data is unavailable or blocked. |
[CONFLICT] |
Resolution | Identifies and surfaces discrepancies between two agent outputs. |
[VERIFIED] |
Truth Anchor | Indicates data checked against specific sources like pipeline reports or actuals. |
Inter-Agent Protocol Advanced Features
- Multi-step Verification Loop: Automated self-checks, cross-functional peer reviews, and executive mentor pre-screening.
- Loop Prevention Engine: Hardcoded rules to block self-invocation, circular calls, and excessive chain depth.
- Broadcast Pattern: Enables the CEO role to query all agents simultaneously for crisis management or rapid data aggregation.
- Context-Aware Isolation: Automatically restricts agent communication during Phase 2 of board meetings to ensure independent analysis.
- Standardized User Communication: Enforces a strict Bottom-Line-First format for all human-facing reports produced via Openclaw Skills.
SKILL.md
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