The Tool Desk
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What Harness Score measures
A coding-agent harness is the surrounding system that supplies a model with project context, tools, feedback and controls. Harness Score focuses on a measurable subset of that setup: artifacts present in a repository. According to the Harness Score project README, its scanner runs 36 checks using filesystem facts rather than LLM judgments or network lookups, then reports a maturity level, dimension scores and prioritized guidance.
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The README documents npm-based use, machine-readable and Markdown output, badges, a GitHub Action and a minimum-level CI gate. These features make the score useful for identifying and tracking setup gaps; they do not establish that a repository is safe or that its agent behaves well.
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The labels below are Harness Score’s own maturity model, not an industry-wide standard. The project describes levels as gated by coverage of new dimensions, not as simple score bands: earning a higher level means adding kinds of guidance, feedback or guardrails, rather than merely accumulating points.
#1 Best Overall
| Level | Project description | What the level indicates |
|---|---|---|
| L0 · Unharnessed | Little structured repository guidance. | A starting point is adding an AGENTS.md file. |
| L1 · Documented | A substantive AGENTS.md orients the agent to the project, build and test process, and constraints. |
Core project guidance is written down. |
| L2 · Guided | Scoped rules, at least one skill or command, and basic hygiene; guidance is versioned with the code. | The agent receives more targeted, maintainable instructions. |
| L3 · Sensing | Tests, linting, type checking and CI provide repeatable feedback on pushes. | Changes can be checked through recurring feedback mechanisms. |
| L4 · Self-correcting | Runtime gates and feedback hooks block risky actions and apply checks such as linting or formatting inline. | Some controls act during the agent’s workflow, not only after a change. |
Use the level as a structural milestone. A higher level signals that additional categories of repository support exist; it does not tell you whether the instructions are accurate or the checks are effective.
What the 108-point score covers
The Harness Score README, accessed in 2026, lists 108 possible points across six dimensions and 36 checks:
| Dimension | Points |
|---|---|
| Context & Guides | 20 |
| Skills & Commands | 17 |
| Hooks & Guardrails | 14 |
| Sensors & Feedback | 20 |
| CI Feedback | 14 |
| Hygiene & Safety | 23 |
These totals describe the project’s scoring scheme, not a validated reliability statistic. The project says maturity-model details may change in minor releases, so record the version or access date and compare like versions when tracking a repository or comparing projects.
How to use the score to improve a repository
- Run the scanner on the repository you intend to assess. Follow the current npm usage documented in the project README, and keep the repository scope consistent across later runs.
- Read the dimension breakdown and the next-level blocker. Identify the specific missing artifact or check rather than treating the overall score as a to-do list.
- Prioritize a concrete gap. Add or improve the guidance, feedback or guardrail the scanner identifies, choosing changes that fit how the project is built and reviewed.
- Run the scan again. Confirm that the intended check is now satisfied and review the updated results. In CI, the documented GitHub Action and minimum-level gate can help enforce a chosen structural threshold.
A gate can ensure that a repository meets a configured Harness Score level; it cannot guarantee the quality of the underlying tests, instructions or agent output. Set the threshold as a maintenance policy, not as a proxy for correctness.
What the score cannot tell you
The project explicitly says the scanner does not assess test quality, whether rules are true or current, functional correctness, or team practices such as review culture and branch protection. A high score means the measured infrastructure exists; the project describes that infrastructure as necessary but not sufficient.
That makes a static maturity scan different from evaluating an agent on tasks. For task behavior, Google’s September 9, 2026 engineering guidance recommends observable behavioral checks as an iteration aid, complementary to end-to-end benchmarks. Behavioral checks can observe intermediate actions; end-to-end benchmarks focus on final task outcomes and may not explain why a result changed. Google also recommends batch evaluation and aggregate trends for noisy model behavior rather than relying on one run. Its examples—such as checking whether an agent asks for clarification when a request is ambiguous, runs a validator after changing a build file, or uses an allowed tool—are possible checks, not a universal required suite. See Google Developers Blog.
Rank #4
For a fair comparison between repositories, hold the Harness Score version and repository scope constant. Compare the overall level alongside each dimension, failed checks and remediation, then evaluate behavior on a fixed set of agent tasks. A raw maturity score alone does not account for differences in repository context.
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Harness Score is not the general meaning of an agent harness
The word “harness” also refers more broadly to runtime scaffolding that drives model and tool calls, manages state and context, applies approvals and supports multistep work. Microsoft Learn describes runtime components such as chat pipelines, context providers, middleware, observability and optional bounded loops. That general architecture is broader than Harness Score’s repository-artifact scanner. See Microsoft Learn.
Best Value
Two other names are easy to confuse with the L0–L4 scale:
Quick Recap
- Harness Protocol is a separate portability proposal for a vendor-neutral
harness.yamldescribing plugins, MCP servers, environment requirements, instructions and permissions. Its documentation describes schema v1 as current, with exchange and registry layers planned. It is not the Harness Score maturity model. See Harness Protocol documentation. - A separate 2026 arXiv preprint proposes an H0–H3 controlled-visibility ladder and trace-based evaluation. It is neither Harness Score’s L0–L4 ladder nor an adopted standard. See the arXiv preprint.
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