Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/harnessworks/harness-starter-kitnpx agentmods add commands/harnessworks/harness-starter-kit/harness-doctorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/harnessworks/harness-starter-kit/harness-doctor)<a href="https://agentmods.dev/commands/harnessworks/harness-starter-kit/harness-doctor"><img src="https://agentmods.dev/badge/commands/harnessworks/harness-starter-kit/harness-doctor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/harnessworks/harness-starter-kit/harness-doctor"><img src="https://agentmods.dev/badge/commands/harnessworks/harness-starter-kit/harness-doctor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01324 |
| Opus 5.5 | $0.00000 | $0.00530 |
| Sonnet 5.5 | $0.00000 | $0.00265 |
| Haiku 4.5 | $0.00000 | $0.00132 |
Grade A, and why
harness-doctor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/harness doctor
Run Harness Doctor to evaluate how ready the current repository is for reliable AI coding agent collaboration.
Harness Doctor is diagnostic only. Do not create, edit, delete, move, format, or stage files while running this command.
Do not remove a nested ./harness-starter-kit directory from the target
repository while running this command. If you created a temporary clone outside
the target repository, such as under /tmp, you may remove only that temporary
copy after the report is complete.
Goal
Inspect the repository and produce a Harness Score out of 100. The score should identify weak points where AI coding agents are likely to repeat mistakes across sessions. It is a harness health diagnostic, not proof of agent effectiveness or mistake reduction.
Scope
Harness Doctor scores the six-part repository harness model in
docs/theory/harness-engineering.md:
Harness = Instructions + Constraints + Feedback + Memory + Evaluation + Governance
The score is a harness health and coupling diagnostic. It checks durable repository evidence for the six elements and reports broken links between rules, checks, memory, evaluation, and governance. It does not prove agent effectiveness or mistake reduction.
Doctor may report task outcome records and effectiveness reports as evidence that the Evaluation element exists. It must not count those records as proof that agents improved unless the report explicitly includes comparable outcome evidence. Runtime-only execution sandboxes and tool protocols are outside the score unless their assumptions are visible in repository files, commands, CI, or approval rules.
Use this principle when judging the repository:
Prompting is temporary. Context is session-scoped. A harness is project-scoped.
Procedure
- Treat the current working directory as the target repository root.
- Inspect durable repository files, not chat context.
- If
./harness-starter-kitexists, treat it as read-only reference material. Do not delete it, ignore it, move it, or clean it up as part of the doctor run. - Read likely harness files:
AGENTS.md,CLAUDE.md,.cursor/rules/*,.github/copilot-instructions.mdREADME.md,CONTRIBUTING.md, project docs, and CI configsdocs/decisions,docs/failures,docs/conventions, anddocs/domainscripts/, package manager config, test config, lint config, and pre-commit config
- If available, run a baseline scan:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 166 lines · 0 tokens per session scan A 342a5276ce94
harness-doctor is a command published in the GitHub repository harnessworks/harness-starter-kit (114 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,324 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
coder-eval-implement-plan
Implement an approved codereval plan phase by phase with risk-scaled per-phase review, then a final code review.
contract
Advance a parallel-change plan from migrate to contract phase, verifying zero callers reference the old signature. Provided by the Parallel Change methodology.
CHANGELOG
All notable changes to the @mstar-harness/commands package are documented in this file.
milestone-list
PitWay: List every milestone with a concise one-line status.
review
Review proposed ADRs for lifecycle progression — accept implemented decisions, deprecate stale ones, archive trivial ones.
fix
Universal debugging and fix application with semantic code analysis.