imcodes

Name: imcodes
Author: im4codes

Warning

The IM for agents. Shared Agent Context & Memory, supervised execution, and cross-agent audit across AI providers.

960stars

122forks

TypeScript

Installation

# Add to your Claude Code skills
git clone https://github.com/im4codes/imcodes

Getting Started

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Security ReportWarning

Last scanned: 5/30/2026

{
  "issues": [
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      "type": "npm-audit",
      "message": "@anthropic-ai/claude-agent-sdk: Vulnerability found",
      "severity": "medium"
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      "type": "npm-audit",
      "message": "@anthropic-ai/sdk: Claude SDK for TypeScript has Insecure Default File Permissions in Local Filesystem Memory Tool",
      "severity": "medium"
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    {
      "type": "npm-audit",
      "message": "@github/copilot: GitHub Copilot CLI: Nested Bare Repository Can Execute Arbitrary Commands via core.fsmonitor",
      "severity": "high"
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      "message": "@vitest/coverage-v8: Vulnerability found",
      "severity": "medium"
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      "type": "npm-audit",
      "message": "@vitest/mocker: Vulnerability found",
      "severity": "medium"
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      "message": "brace-expansion: brace-expansion: Large numeric range defeats documented `max` DoS protection",
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      "message": "express-rate-limit: Vulnerability found",
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      "severity": "high"
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      "message": "vite: Vite Vulnerable to Path Traversal in Optimized Deps `.map` Handling",
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      "severity": "medium"
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      "message": "vitest: Vulnerability found",
      "severity": "medium"
    },
    {
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      "message": "ws: ws: Uninitialized memory disclosure",
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README.md

Frequently Asked Questions

What is imcodes?

imcodes is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by im4codes. The IM for agents. Shared Agent Context & Memory, supervised execution, and cross-agent audit across AI providers. It has 960 GitHub stars.

Is imcodes safe to use?

imcodes returned warnings in SkillsLLM's automated security scan. It has no critical vulnerabilities, but review the flagged issues in the Security Report section before adding it to your workflow.

How do I install imcodes?

Clone the repository with "git clone https://github.com/im4codes/imcodes" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is imcodes written in?

imcodes is primarily written in TypeScript. It is open-source under im4codes on GitHub, so you can review or fork the full source.

Are there alternatives to imcodes?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh imcodes against similar tools.

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IM.codes

The IM for agents. AI remote desktop, shared memory, OpenSpec Auto Deliver, managed MCP tools, supervised execution, and cross-agent audit across AI providers.

Two heads are better than one. But minds in concert don't answer fate, they author it. — IM.codes

IM.codes connects AI agents not only to coding sessions, but also to the computers where work happens. Enroll supported machines as restricted Controlled Nodes, then let an authorized agent directly operate one or many computers with scoped commands, file transfer, and typed Computer Use. When a person needs to see or take over, capable Windows nodes also provide browser-based remote desktop from desktop or mobile.

Alongside machine control, IM.codes gives coding agents one shared memory layer and one managed MCP tool surface across providers. It turns completed work into reusable context across Claude Code, Codex, Gemini CLI, GitHub Copilot, Cursor, OpenCode, OpenClaw, Qwen, and more. Terminal access, file and Git views, localhost preview, notifications, multi-agent workflows, OpenSpec Auto Deliver, Auto supervision, and cross-provider Team audit keep both human and agent work visible and connected.

Disclaimer: This is an actively developed personal open-source project. There are no warranties, no SLA, and no guarantees of stability, security, or backward compatibility. Use at your own risk.

Controlled Nodes: AI Remote Desktop

Turn supported computers into AI-operated nodes without making each machine a full IM.codes source server.

One AI, multiple computers. Enroll multiple machines and let one agent—or a team of agents—operate them directly to get work done.
Real computer actions. Run scoped commands, move individual files, and control desktop applications through typed Computer Use tools.
Remote desktop when humans need it. Open a capable Windows node from a browser or mobile device to view the screen or take control directly.
Scoped and revocable. Every node has independent credentials, remains separate from normal server/session lists, and can have execution access disabled or revoked by its owner.

Screenshots

Desktop

iPad / Tablet

Mobile

Apple Watch

Watch support covers quick session monitoring, unread counts, push notifications, and quick replies directly from the wrist.

Download

Supports iPhone, iPad, and Apple Watch. Also available as a web app.

Why

When you leave your desk, most coding-agent workflows fall apart. The agent is still running in a terminal, but continuing the work usually means SSH, tmux attach, remote desktop hacks, or waiting until you're back at your laptop.

That reach problem is only one half of it. Complex coding-agent work also needs steadier judgment: a single model can fall into familiar patterns, miss issues, or produce unstable answers on hard tasks. Switching providers can help, but without shared context it can also lose the thread.

IM.codes is built around both needs. It keeps sessions within reach from mobile or web: open the terminal, inspect files and git changes, preview localhost from another device, get notified when work finishes, invite another person into the same session or server, and keep multiple agents moving on your own infrastructure. It also pairs Shared Agent Context & Memory with Multi-Agent Discussions & Cross-Provider Audit: durable recall comes from summarized completed work, while Team discussion is structured cross-model review before code lands. It does not make output perfect, but it reduces single-model blind spots and helps complex work converge with more review.

It is not another AI IDE or a generic remote terminal. It is the messaging, memory, and review layer around terminal-based coding agents.

OpenSpec Auto Deliver

For OpenSpec-based changes, Auto Deliver turns a change folder into an end-to-end supervised delivery run: proposal/spec review, implementation, validation, Team audit, automatic module scoring, rework gates, and a visible final handoff.

One-click change pipeline. Launch from an OpenSpec change on a transport-backed coding session. IM.codes resolves the owning session, locks the Team lane to prevent conflicting runs, reads tasks.md, and keeps a live run projection in the UI.
Spec audit before implementation. Optional proposal/spec audit-repair uses the normal Team flow (default audit>review>plan) and reads an authoritative JSON result instead of trusting chat summaries.
Task-driven implementation loop. The daemon sends focused implementation prompts back into the same session, works only on that OpenSpec change, tracks checked/unchecked tasks, and surfaces safe validation command candidates discovered from project manifests.
Automatic module scoring. Every audit produces structured scores for spec, tasks, implementation, tests, and risk, with evidence and summaries visible in the run details instead of buried in chat text.
Implementation audit and rework gates. The final scored verdict — PASS, REWORK, or BLOCKED — decides whether the run can pass, should repair while limits allow, or needs a human decision.
Presets for delivery depth. fast, standard, strict, and deep tune spec-audit rounds, implementation-audit rounds, max implementation prompts, and elapsed-time limits.
Fail-closed and human-owned. Auto Deliver asks for human input on invalid audit output, time/prompt limits, manual interference, incompatible Team state, or unreadable tasks. It does not stage, commit, or push code; the user stays the final release owner.

Collaborative Coding

Share a tab, sub-session, or whole source server with another user. Use viewer for read-only review or participant when a teammate should send prompts into the covered sessions. Shared messages carry actor labels, and access can be downgraded or revoked from the UI.

Shared Agent Context & Memory

IM.codes continuously turns completed agent work into reusable memory and feeds that context back into future sessions.

Problem → solution memory, not log spam. Only final assistant.text outputs are materialized. Streaming deltas, tool calls, and intermediate noise are excluded.
Personal memory with optional cloud sync. Raw and processed memory always stay local; processed summaries can optionally sync to a user-scoped cloud pool shared across your devices.
Enterprise shared context. Teams can publish reusable memory into workspace/project