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- Free
Summary
You merged the agent's PR, the tests passed in CI, and three days later you're untangling hallucinated diffs that looked correct but weren't validated against anything real — Orbit exists because 'it ran without errors' is not the same as 'it did what you asked.'
Orbit is a CLI harness that wraps any JSON-speaking coding agent — Claude, Codex, Cursor, or your own — in a bounded loop: one task selected from a dependency-ordered backlog, executed by the agent, then checked against tests, lint, and type validation before the orbit closes. If the agent cannot prove the work, the run does not advance. Every orbit writes structured JSON artifacts and a human-readable progress log, so you are reviewing evidence rather than re-reading diffs and guessing. The harness runs entirely locally, requires no API key for the replay demo, and is MIT licensed. Where it breaks: teams whose validation needs go beyond tests and lint — custom scoring rubrics, multi-step human approval workflows, or large parallel backlogs — will find the intentionally small surface area a ceiling rather than a feature.
Bottom line: Use Orbit when you need a reproducible, auditable gate between an agent's output and your main branch; plan around it when your review process requires parallel execution or approval steps beyond accept/iterate/stop.
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Pros
Sign in to edit- Validation gates block an orbit from closing unless tests, lint, and type checks pass, so you stop merging agent output that ran without error but failed to do what the task required.
- Four structured artifacts per run — result, evaluation, review recommendation, and a progress log — give you an auditable evidence trail, so post-mortem debugging is reading JSON rather than reconstructing what the agent did from git history.
- Agent-neutral CLI contract means you can run Claude and Codex against the same task and backlog, comparing scored artifacts directly instead of running separate experiments with incomparable outputs.
- Dependency-ordered backlog selection keeps each orbit focused on one task at a time, so the agent cannot silently absorb scope from adjacent work and produce diffs that are hard to attribute.
- MIT licensed with a no-API-key replay demo, so you can evaluate the full validation loop against a real artifact chain without committing credentials or incurring cost.
Cons
Sign in to edit- Validation is limited to tests, lint, and type checks as described on the vendor page — teams whose definition of 'done' includes semantic correctness, security scanning, or domain-specific rules have to build that checking outside the harness and wire it in manually, adding a second system to maintain.
- The harness executes one orbit at a time; teams running large backlogs where tasks are independent and could parallelize will hit a throughput ceiling and move to a more capable orchestration layer or build parallelism themselves.
- There is no built-in multi-step human approval workflow beyond the accept/iterate/stop recommendation in `review.json` — teams that need a formal sign-off gate before code advances to staging will need to script that around the harness or switch to a tool that treats human review as a first-class execution step.
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About
- Platforms
- Cross-platform (Python 3.6+)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-01T12:38:08.763Z
Best For
Who it's for
- Teams developing AI coding agents and needing validation gates
- Researchers comparing agent implementations with reproducible test harnesses
- Projects requiring auditable, deterministic agent execution with replay capability
- Developers building agent adapters and wanting to validate correctness systematically
What it does well
- Testing and validating AI agent code generation before merging
- Deterministic replay and debugging of agent behavior with progress logs
- Comparing agent implementations (Claude vs. Codex vs. others) against the same validation gates
- Building self-healing repos by requiring failing tests or lint issues to be fixed
- Executing dependency-ordered task backlogs one verified orbit at a time
Integrations
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Frequently Asked Questions
- Is Skills free?
- Yes — Skills is fully free to use. There is no paid tier.
- Is Skills open source?
- Yes. Skills is open source.
- Can I self-host Skills?
- Yes. Skills supports self-hosting on your own infrastructure.
- What platforms does Skills support?
- Skills is available on: Cross-platform (Python 3.6+).
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Curated lists that include this category
Most AI coding agent demos show the happy path. Production shows the agent that changed four files, passed no tests, and left a `review.json` that said ‘complete.’ Orbit is a CLI validation harness that addresses this by wrapping agent execution in what it calls orbits: a single dependency-ordered task is selected from a backlog, handed to whichever coding agent you configure, and then checked through tests, lint, and type checks before the loop closes. The agent does not advance until it can prove the work. Every run leaves four artifacts — `agent-result.json`, `evaluation.json`, `review.json`, and `progress.md` — giving you structured evidence of what the agent returned, how it scored on a rubric, and what the recommended next action is.
The standout design decision is agent neutrality. Orbit does not care whether the agent behind it is Claude, Codex, Cursor, or a custom adapter, as long as it speaks JSON over the CLI. This means you can run the same task backlog through two different agents and compare artifacts instead of impressions — the vendor page explicitly describes this as ‘adapter experiments,’ swapping coding agents behind the same contract. For teams benchmarking agent implementations or building their own adapters, this is the differentiating capability.
Orbit fits tightly into two scenarios: self-healing repos, where you feed it failing tests or lint issues and require proof before a task is marked complete, and sequential backlog execution, where dependency-ordered tasks advance one verified orbit at a time. It does not fit teams who need parallel task execution, multi-stage human approval chains, or agent orchestration across services. The harness is described by the vendor as ‘intentionally small’ — contributions that make it easier to verify, replay, or connect to another workflow are the stated priority, not expanding scope.
The deterministic replay demo runs with `MOCK=1 ./replay.sh auth-rescue` and requires no API key, which means you can inspect the full artifact chain — task selection, agent path, validation result, scoring — before connecting any live agent. Self-hosting is the only deployment model; there is no cloud offering.