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MCP verified Apache-2.0 Self-run

Harness Ai Kit

mcp-seed-forge-harness-ai-kit · by seed-forge

Package manager for AI agent assets (skills / CLIs / MCPs / loops) across Codex, Claude Code, Cursor and Kiro.

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Install

$ agentstack add mcp-seed-forge-harness-ai-kit

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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yesterday

Declared compatibility

Claude CodeClaude DesktopCursorWindsurf

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

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)

Package manager for AI agent assets — install, lock and govern skills / CLIs / MCPs / loops across Codex, Claude Code, Cursor and Kiro. Includes an enterprise-grade AI-ops skill library.

[Quickstart](docs/quickstart.md) · [Skill Catalog](CATALOG.md) · [Usage Scenarios](docs/usage-scenarios.md) · [Docs](docs/) · [Roadmap](ROADMAP.md) · [Changelog](CHANGELOG.md) · [中文文档](README.zh-CN.md)

Why

As AI agents proliferate, teams accumulate reusable prompts, skills, CLIs and MCP servers — but there's no npm for these assets. harness-ai-kit fills that gap: one CLI to install, resolve, lock, validate and govern AI agent assets across multiple runtimes.

Why not just copy SKILL.md files?

| | Copy-paste | harness-ai-kit | |---|-----------|--------| | Install a skill | git clone → find the right dir → copy files | harness-ai-kit add skill | | Pin versions | Manual tracking | harness-ai-kit.lock with SHA-256 | | Team consistency | "Works on my machine" | harness-ai-kit.yml + harness-ai-kit sync = identical state | | Multiple AI runtimes | Repeat for each tool | --runtime codex/claude-code/cursor/kiro | | Dependency conflicts | Silent breakage | SAT solver detects conflicts upfront | | Offline / air-gapped | Re-download everything | harness-ai-kit sync --offline from cache |

No lock-in — you don't have to use harness-ai-kit

Every skill in the [catalog](CATALOG.md) is a plain folder of Markdown + JSON. If you don't want another CLI, you don't need one:

  • Let your agent fetch it — paste the skill's GitHub URL to Codex / Claude Code / Cursor and ask it to install the skill into your runtime's skills directory.
  • npx / one-off scripts — pull a single SKILL.md straight from the repo, no install step.
  • Copy the folder yourself — drop it into .agents/skills/ (Codex) or .claude/skills/ (Claude Code) by hand.

harness-ai-kit is not a gatekeeper for the content. What it adds on top is the asset manifest: a curated, versioned, checksummed inventory (harness-ai-kit.yml + harness-ai-kit.lock) of what your project uses. If that inventory is useful to you, the CLI is the fastest way to manage it. If not, the skills work fine without it.

Team collaboration — commit the manifest, not the assets

The manifest is where harness-ai-kit pays off most. The intended team flow:

Member A (sets up)
  harness-ai-kit add skill devlab-spec-driven-dev
  harness-ai-kit add skill diag-mysql-deadlock
  git add harness-ai-kit.yml harness-ai-kit.lock        # commit ONLY the manifest
  git commit -m "chore: pin team AI skills"

Member B (joins / updates)
  harness-ai-kit sync                            # done — exact same assets, SHA-256 verified

Two concrete benefits:

  1. Nothing sensitive leaves the repo. Member A commits only two small YAML/JSON files. The raw skill folders — which may carry local paths, personal runtime config, or credentials from a member's own machine — are never committed. Member B re-materializes them locally from the manifest.
  2. Updates are cheap and non-destructive. When the team bumps a skill version, members just harness-ai-kit sync (or harness-ai-kit update) to pull the latest. Because the lockfile records exactly what's managed by harness-ai-kit, a member's own hand-added / custom skills are left untouched — sync reconciles the manifest, it doesn't wipe your local additions.

In short: the manifest is the team's shared source of truth for "which AI assets we run", and sync is how everyone stays identical without sharing anything sensitive.

Quick Start

pip install harness-ai-kit
harness-ai-kit init
cd your-project
harness-ai-kit add skill https://github.com/anthropics/skills/tree/main/skills/skill-creator
harness-ai-kit sync

> Short alias: every command is also available as ai-kit (e.g. ai-kit sync). Use whichever you prefer — both invoke the same CLI.

Verify it worked:

harness-ai-kit doctor              # health check — should be all green
ls .agents/skills/          # skill-creator should be here

The skill is now available to your AI agent. See [examples/](examples/README.md) for real-world usage patterns (team sync, multi-runtime, offline mode).

Command Cheatsheet

| Command | What it does | |---------|-------------| | harness-ai-kit init | First-time machine setup | | harness-ai-kit add skill | Add a skill to your project | | harness-ai-kit sync | Install declared assets to runtime | | harness-ai-kit list | Browse available skills | | harness-ai-kit show | Show skill metadata | | harness-ai-kit lock | Pin exact versions to harness-ai-kit.lock | | harness-ai-kit doctor | Health check your environment | | harness-ai-kit remove skill | Remove a skill | | harness-ai-kit outdated | Check for updates | | harness-ai-kit cache clean | Clear local cache |

Full reference: [docs/cli-reference.md](docs/cli-reference.md)

Features

  • Unified asset schema — skills, CLIs, MCPs, plugins, hooks, subagents and loops share one typed dependency model with pinned versions
  • Dependency resolutionresolvelib-based solver with lockfile (harness-ai-kit.lock) and checksum verification
  • Multi-runtime support — install to Codex, Claude Code, Cursor, Kiro (project or global scope)
  • GitHub direct install — install skills from any GitHub repo, no private registry required
  • Staging + rollback — atomic installs with automatic rollback on failure
  • Offline mode — cache-driven install without network access
  • Enterprise skill library — curated ops skills (MySQL deadlock diagnosis, K8s CrashLoopBackOff, container OOM, etc.) included

Installation

pip install harness-ai-kit

Requires Python >= 3.10 and git.

Built-in Skill Library

34 production-tested skills included. Install any with harness-ai-kit add skill . Full categorized index: [CATALOG.md](CATALOG.md).

> New here? Read [Usage Scenarios](docs/usage-scenarios.md) first — it explains how skills get pulled into real work via an SDD framework (e.g. Trellis), and how loops bind to a runtime.

Database Expert Bases (9 skills — schema design, indexing, query tuning, replication)

| Skill | Domain | |-------|--------| | public-mysql-expert-base | MySQL/InnoDB — schema, indexes, locks, tuning | | public-postgres-expert-base | PostgreSQL — B-Tree/GIN/GiST, JSONB, partitioning | | public-redis-expert-base | Redis — data structures, connection pool, TTL | | public-mongodb-expert-base | MongoDB — aggregation, indexes, replica sets | | public-kafka-expert-base | Kafka — topics, consumer groups, exactly-once | | public-rabbitmq-expert-base | RabbitMQ — exchanges, durability, dead letter | | public-oracle-expert-base | Oracle — JDBC, LOB, character set | | public-nl2sql-expert-base | NL2SQL — natural language to SQL | | public-git-workflow-expert-base | Git — commit, branch, PR conventions |

Diagnostic Playbooks (7 skills — enterprise troubleshooting chains)

| Skill | Scenario | |-------|----------| | diag-mysql-deadlock | InnoDB deadlock capture + lock chain analysis | | diag-mysql-slow-query | Slow query log + EXPLAIN + index analysis | | diag-mysql-replication | Master-slave delay root cause | | diag-container-oom | dmesg OOM killer → cgroup → Docker memory | | diag-k8s-pod-crashloop | CrashLoopBackOff full-chain diagnosis | | diag-k8s-node-pressure | CPU/Memory/Disk/PID pressure | | diag-network-port-unreach | DNS → TCP → iptables → service → route |

AI Engineering Methodology (5 skills — spec-driven dev, agent architecture, eval, tech debt)

| Skill | Purpose | |-------|---------| | devlab-spec-driven-dev | Spec-driven AI collaboration (requirements → design → tasks) | | devlab-ai-agent-engineering | AI agent app architecture methodology | | devlab-eval-driven-agent | Eval-driven agent quality system | | devlab-ai-kit-miner | Post-session retrospective → asset extraction | | devlab-tech-debt-ops | Tech debt lifecycle (audit → refactor → verify) |

Patent & Document Authoring (6 skills)

| Skill | Purpose | |-------|---------| | patent-specification-writer | Patent specification drafting | | patent-review | Patent quality review with dimensions checklist | | patent-disclosure-workflow | Patent disclosure end-to-end workflow | | work-sc-patent-specification-writer | Patent spec (work-sc namespace) | | work-sc-software-copyright-writer | Software copyright application materials | | document-reference-sop-builder | Turn an exemplar document into a reusable SOP |

General & Infra (7 skills)

| Skill | Purpose | |-------|---------| | base-cn-registry-mirror-strategy | China mirror acceleration (Docker/Debian/Python/Maven) | | base-goal-execution | Goal-driven execution with checkpoints | | markitdown | Document-to-Markdown conversion | | work-convert / work-export | Document conversion/export | | post-task-skill-miner | Post-task retrospective → skill extraction | | infra-system-env-ops | Monit watchdog / service self-healing |

Full catalog with install commands: [CATALOG.md](CATALOG.md) · usage patterns: [docs/usage-scenarios.md](docs/usage-scenarios.md)

Architecture

┌─────────────────────────────────────────────────────┐
│                    harness-ai-kit CLI                        │
│  init · add · install · sync · lock · resolve ·     │
│  graph · why · validate · doctor · upgrade · cache  │
├─────────────────────────────────────────────────────┤
│              Package Manager Core                    │
│  ┌──────────┐  ┌──────────┐  ┌──────────────────┐   │
│  │ Resolver │  │ Lockfile │  │ Runtime Adapters  │   │
│  │ (resolve)│  │ (lock)   │  │ codex·claude·kiro│   │
│  └────┬─────┘  └────┬─────┘  └────────┬─────────┘   │
│       │              │                  │             │
│  ┌────▼──────────────▼──────────────────▼─────────┐  │
│  │          Source Abstraction Layer               │  │
│  │  GitHub repos · PyPI · raw registries · cache   │  │
│  └─────────────────────────────────────────────────┘  │
├─────────────────────────────────────────────────────┤
│              Enterprise Skill Library                │
│  public-*-expert-base · diag-* · infra-* · loops     │
└─────────────────────────────────────────────────────┘

Roadmap

See [ROADMAP.md](ROADMAP.md) for the full plan with milestone criteria.

| Phase | Content | Status | |-------|---------|--------| | v0.1 (current) | CLI + schema + validate + GitHub direct install + curated skill library | ✅ Released | | v0.2 (planned) | Loop automation framework + hooks mechanism + skill authoring toolkit | 🔜 Planned | | v0.3 (future) | Expanded infra/devlab skill library + MCP assets + RBAC governance | 📋 Backlog | | Phase B (long-term) | Public registry backend + browser UI + admin/publisher system | 🔬 Research |

Project Layout

harness-ai-kit/
├── harness_ai_kit/      # CLI source code
│   ├── commands/        # Command handlers (install, resolve, lock, ...)
│   ├── domain/          # Domain models (manifest, lockfile, resolver, ...)
│   ├── infrastructure/   # Infrastructure (git ops, registry client, ...)
│   └── data/             # Default config seed
├── skills/              # Curated enterprise skill library
├── examples/            # Real-world usage examples
├── docs/                # Documentation
│   ├── quickstart.md    # Step-by-step getting started
│   ├── cli-reference.md # Complete CLI command reference
│   ├── concepts.md      # Core concepts explained
│   ├── skill-authoring.md # Write your own skills
│   ├── asset-map.md     # Skill library catalog
│   └── troubleshooting.md # Common issues and fixes
├── .github/             # CI, issue templates, community files
├── pyproject.toml       # Package metadata
└── LICENSE              # Apache-2.0

Community & Support

  • Questions: GitHub Discussions or [SUPPORT.md](SUPPORT.md)
  • Bugs & features: Issue tracker
  • Security: See [SECURITY.md](SECURITY.md)
  • Changes: See [CHANGELOG.md](CHANGELOG.md)

Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md). We use the Developer Certificate of Origin (DCO) — all commits must be signed off.

License

[Apache-2.0](LICENSE) © 2026 SeedForge

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.