harness-ai-kit

August 27, 2026 · View on GitHub

A package manager and composition layer for AI agent assets.

harness-ai-kit installs, resolves, locks, validates, and synchronizes Skills, CLIs, MCPs, plugins, hooks, subagents, and loops across Codex, Claude Code, Cursor, Kiro, and DeepSeek Harness (dsh).

Quickstart · Asset Catalog · Usage Scenarios · Concepts · Roadmap · Changelog · 中文文档

Why

AI teams collect useful prompts, Skills, CLIs, and MCP servers quickly. The hard part is making that collection reproducible: knowing what is installed, which versions work together, where it is materialized, and how a teammate can get the same working state without copying runtime directories.

harness-ai-kit makes the project manifest the source of truth:

harness-ai-kit.yml  ->  resolve  ->  harness-ai-kit.lock  ->  runtime materialization
       intent             plan          integrity snapshot       Codex / Claude / Cursor / Kiro / dsh

The lock records resolved sources and checksums. Installation uses a staging directory and only replaces the runtime target after the selected assets are ready. The result is useful for one developer, and essential when a team needs the same AI engineering environment across projects and runtimes.

The REMIX Method

This project is a composition layer, not another vertical AI toolkit. When a focused community Skill, CLI, or MCP already solves a problem, the preferred path is to compose it, pin it, and make it reproducible instead of rebuilding it.

Reuse proven assets from the community or your own repositories.
Extend an asset when a small, explicit adaptation is enough.
Mix Skills, CLIs, MCPs, and runtime assets into one workflow.
Integrate the selected assets through a typed manifest and lockfile.
eXecute the same declared environment across supported runtimes.

No Lock-In

An installed Skill is still a normal directory of Markdown and metadata. You can read it, copy it, or install it directly from its Git repository without using this tool. harness-ai-kit is not a gatekeeper for content; it adds the reproducible inventory, dependency resolution, checksums, and runtime sync around that content.

Quick Start

Requirements: Python 3.10+ and Git.

python -m pip install --upgrade harness-ai-kit==0.18.18
harness-ai-kit init

mkdir my-agent-project
cd my-agent-project
harness-ai-kit init-project
harness-ai-kit add skill https://github.com/OWNER/REPO/tree/main/path/to/skill
harness-ai-kit sync
harness-ai-kit doctor

init creates or updates the shared configuration at ~/.harness-ai-kit/config.yaml. init-project creates the project manifest; sync resolves it, writes the lockfile, and materializes the selected assets for the configured runtime. See the quickstart for runtime-specific installation details.

Team Workflow

Commit the declaration and lockfile, not copied runtime directories:

Maintainer                         Teammate
----------                         --------
add selected assets                clone or pull the project
review the lockfile                harness-ai-kit sync
commit manifest + lockfile         receive the same resolved asset set

This keeps local customizations out of version control while allowing a shared, auditable AI asset baseline. sync reconciles managed assets; it is not a blind wipe of unrelated local files.

What It Provides

Capability	Outcome
Typed asset model	One dependency contract for Skills, CLIs, MCPs, plugins, hooks, subagents, and loops
Resolution and lockfiles	A reproducible selection of versions, sources, features, and SHA-256 checksums
Multiple runtime adapters	Project or global installation for supported AI coding runtimes
Git-based sources	Install a reviewed Skill directly from a public Git repository
Safe materialization	Staging, verification, replacement, and rollback-aware installation flow
Configuration boundary	User-specific endpoints and credentials live in `~/.harness-ai-kit/config.yaml`, not in assets
Curated public assets	Reusable engineering, diagnostic, and AI-development assets listed in the catalog

Architecture

                     harness-ai-kit CLI
 init | add | install | sync | lock | doctor | validate | upgrade
                              |
                 manifest + dependency resolver
                              |
                     harness-ai-kit.lock
                              |
         source adapters + cache + checksum verification
                              |
      runtime adapters and asset bundles for AI coding environments

The public project deliberately separates portable product behavior from private operating context. Public packages must work with a user's own configuration and public dependencies; private endpoints, credentials, and deployment topology do not belong in the published tree.

Usage Paths

Adopt a public Skill: install from a Git repository, then sync it into a project runtime.
Share an engineering baseline: commit the manifest and lockfile so the team resolves the same assets.
Author an internal or public asset: use the metadata contract, validate it locally, and publish only through an explicit reviewed release path.
Run dsh: install Skills or the bundled plugin through the dsh runtime adapter. See dsh integration.

The usage scenarios explain when to use a Skill, when a loop is appropriate, and how a spec-driven workflow can route both.

Roadmap

The current public product focuses on portable asset management, reproducible installation, and a reviewed public catalog. Future work expands authoring and automation first; registry, browser, and organization administration remain separate platform milestones rather than hidden dependencies of the core CLI.

See ROADMAP.md for scope, milestones, and non-goals.