harness-ai-kit

# harness-ai-kit [](https://pypi.org/project/harness-ai-kit/) [](https://pypi.org/project/harness-ai-kit/) [](LICENSE) [](https://github.com/seed-forge/harness-ai-kit/actions/workflows/ci.yml) **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](docs/quickstart.md) · [Asset Catalog](CATALOG.md) · [Usage Scenarios](docs/usage-scenarios.md) · [Concepts](docs/concepts.md) · [Roadmap](ROADMAP.md) · [Changelog](CHANGELOG.md) · [中文文档](README.zh-CN.md) ## 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: ```text 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. - **R**euse proven assets from the community or your own repositories. - **E**xtend an asset when a small, explicit adaptation is enough. - **M**ix Skills, CLIs, MCPs, and runtime assets into one workflow. - **I**ntegrate the selected assets through a typed manifest and lockfile. - e**X**ecute 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. ```bash 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](docs/quickstart.md) for runtime-specific installation details. ## Team Workflow Commit the declaration and lockfile, not copied runtime directories: ```text 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](CATALOG.md) | ## Architecture ```text 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](docs/dsh-integration.md). The [usage scenarios](docs/usage-scenarios.md) 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](ROADMAP.md) for scope, milestones, and non-goals. ## Documentation - [Quickstart](docs/quickstart.md) - [Core concepts](docs/concepts.md) - [CLI reference](docs/cli-reference.md) - [Asset catalog](CATALOG.md) - [Asset authoring contract](docs/asset-authoring-contract.md) - [Troubleshooting](docs/troubleshooting.md) - [OSS release process](docs/oss-release.md) ## Contributing And Security Use [CONTRIBUTING.md](CONTRIBUTING.md) for contribution expectations and [SECURITY.md](SECURITY.md) for responsible disclosure. Issues and feature requests belong in the GitHub issue tracker; open-ended design discussion can use GitHub Discussions. ## License [Apache-2.0](LICENSE) © 2026 SeedForge.