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OpenHarness Showcase

This page collects concrete ways to use OpenHarness without overselling the project. Each example is intended to be small, reproducible, and easy to extend.

1. Repository-aware coding assistant

Use OpenHarness as a lightweight local coding agent for reading code, making edits, and running validation commands.

uv run oh

Example prompt:

Review this repo, identify the highest-risk bug, patch it, and run the relevant tests.

2. Headless automation for scripts and CI

The print mode is useful when you want structured output in shell pipelines or automation jobs.

uv run oh -p "Summarize the purpose of this repository" --output-format json
uv run oh -p "List files that define the permission system" --output-format stream-json

3. Skill and plugin playground

OpenHarness can load Markdown skills and Claude-style plugin layouts, which makes it useful for experimentation with custom workflows.

Examples:

  • Put a custom skill in ~/.openharness/skills/.
  • Install a plugin into ~/.openharness/plugins/.
  • Use the same workflow conventions across multiple local projects.

4. Multi-agent and background task experiments

The repo includes team coordination primitives, background task management, and task inspection tools.

Example prompts:

Spawn a worker to audit the test suite while you inspect the CLI command registry.
Create a background task that runs the slow integration script and report back when it finishes.

5. Provider compatibility testbed

OpenHarness is useful when you need to compare Anthropic-compatible backends behind one harness.

Typical scenarios:

  • Default Anthropic setup.
  • Moonshot/Kimi through an Anthropic-compatible endpoint.
  • Vertex-compatible and Bedrock-compatible gateways.
  • Internal proxies that expose an Anthropic-style API surface.

See the provider compatibility table in README.md.

6. Documentation-first onboarding

If you are evaluating the project rather than contributing code, start here:

How to contribute a showcase entry

Good showcase additions are:

  • Based on a real workflow you ran.
  • Short enough to reproduce locally.
  • Honest about prerequisites and limitations.
  • Focused on what OpenHarness makes easier, not on generic LLM claims.