Implementing Agentic Workflows via skill.md: Orchestrating macOS iMessage through Local LLM Agents

The paradigm of Large Language Model (LLM) interaction is shifting from passive text generation to active, agentic execution. While traditional interfaces like ChatGPT or Claude.ai are confined to a browser sandbox, the emergence of local-first coding agents—such as Claude Code, OpenAI’s Codex, and Cursor—has opened a new frontier: system-level orchestration. By leveraging a structured skill.md implementation, developers can extend these agents' capabilities beyond code manipulation into the realm of OS-level automation, specifically targeting the macOS iMessage ecosystem.

The Architecture of Local Agentic Tool-Use

The core of this technical workflow lies in the deployment of a skill.md file within the agent's local execution environment. Unlike standard system prompts, which define persona and constraints, a "skill" file acts as a functional interface definition (similar to an OpenAPI specification) that instructs the agent on how to interact with specific local APIs or CLI tools.

When running an agent like Claude Code or Hermes locally on macOS, the agent is granted permission to execute shell commands and access system-level data. The skill.md file provides the necessary logic for the agent to bridge the gap between natural language intent (e.g., "Message Emily about the meeting") and the programmatic execution of iMessage protocols.

Contact Resolution and Identity Mapping

One of the primary technical challenges in automating messaging is identity resolution. An LLM cannot inherently know which "Emily" a user intends to contact without access to a structured database. The implementation described utilizes the macOS Contacts framework to perform real-time lookup operations.

The workflow follows a specific computational logic:

1. Intent Parsing: The agent parses the user's natural language input to extract an entity (e.g., "Emily") and a payload (the message content).
2. Entity Resolution: The agent queries the local contacts database to resolve the string "Emily" against existing contact records.
3. Ambiguity Handling: In cases of high entropy—where multiple contacts match the search query—the agent is programmed to trigger a follow-up request, presenting the identified candidates to the user for verification.
4. Payload Construction: Once the identifier (phone number or Apple ID) is resolved, the agent constructs the final message string, often appending metadata such as a signature (e.g., CC for Claude Code) to maintain transparency in automated communications.

The Safety Protocol: The Send-and-Confirm Loop

In autonomous agent design, the "Human-in-the-Loop" (HITL) pattern is critical for preventing unintended side effects—especially when an agent has write-access to communication channels.

The skill.md implementation utilizes a Send-and-Confirm Loop. This prevents the agent from executing a send command without explicit user validation of the resolved contact and the composed message body. The loop functions as follows:

• Pre-computation: The agent generates the proposed message.
• Interruption/Verification: The agent pauses execution to display the draft to the terminal or UI.
• Boolean Validation: The process only proceeds to the send state upon receiving a positive confirmation (e.g., "Yes" or "Proceed") from the user.

As an agent's reliability increases through iterative testing, this loop can be programmatically bypassed for low-risk tasks, though maintaining it for initial deployment is a best practice in agentic safety engineering.

Temporal Automation and Scheduled Orchestration

Beyond reactive messaging, these agents can be leveraged to create scheduled automation pipelines. By instructing the agent to execute specific skill sets at predefined intervals (e.g., 9:00 AM and 8:00 PM), users can transform a coding assistant into a personal operations manager.

This involves the agent monitoring system time and triggering the skill.md logic to scrape recent message threads, summarize group chat activity, and push updates back to the user. This effectively turns iMessage into a real-time telemetry feed for an AI-driven productivity workflow.

Scaling via Cloud-Based Agentic Interfaces: Chorus.com

While local agents offer deep system integration, they are limited by the hardware on which they run. Chorus.com extends this capability to the cloud, providing a bridge that allows users to interact with advanced models directly through iMessage without requiring a persistent local Mac connection.

The technical ecosystem of Chorus.com integrates specialized skills via third-party APIs:

• Supadata Integration: Enables agents to ingest and process unstructured data from YouTube transcripts, Instagram Reels, and TikTok videos.
• Advanced Reasoning Engines: Utilizing high-parameter models like Opus 4.8, the platform can perform complex research tasks, such as analyzing multi-video datasets to generate comprehensive market intelligence reports or landing page structures.

By decoupling the agent's "brain" (the LLM) from the "interface" (iMessage), developers can create a ubiquitous, highly accessible automation layer that functions across any device capable of receiving an iMessage.