Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add harnessworks/harness-starter-kit --skill harness-doctorgit clone --depth 1 https://github.com/harnessworks/harness-starter-kitWrote 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/skills/harnessworks/harness-starter-kit/harness-doctor)<a href="https://agentmods.dev/skills/harnessworks/harness-starter-kit/harness-doctor"><img src="https://agentmods.dev/badge/skills/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/skills/harnessworks/harness-starter-kit/harness-doctor"><img src="https://agentmods.dev/badge/skills/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 | $0.00038 | $0.00199 |
| Opus 5.5 | $0.00015 | $0.00080 |
| Sonnet 5.5 | $0.00008 | $0.00040 |
| Haiku 4.5 | $0.00004 | $0.00020 |
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.
What it actually says
Harness Doctor
Evaluate repository harness health without modifying files.
Steps
- Read
../../references/package-contract.md. - Read
../../references/doctor-workflow.md. - If
./harness-starter-kit/commands/harness-doctor.mdexists in the target, prefer that canonical workflow. - Inspect durable repository evidence.
- Run
python scripts/harness_doctor.py --target .when available. - Produce the Harness Doctor Report.
Boundaries
- Do not create, edit, delete, move, format, or stage files.
- Do not remove a nested
./harness-starter-kitdirectory. - Score repository-visible harness health, not claimed agent effectiveness.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 26 lines · 38 tokens per session scan A fb2ea8f00a76
harness-doctor is a skill published in the GitHub repository harnessworks/harness-starter-kit (115 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 199 once invoked, about $0.0002 per session on Opus 5.5. 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 skills, from other repositories
qa-to-engineering-brief
Converts bug reports, screenshots, user panic, QA notes, and vague complaints into durable engineering briefs. Use when turning observed failures into reproducible issues with severity, expected behaviour, evidence, scope, owner, and acceptance tests.
k-issue
A workflow for reproducing and diagnosing an existing software failure. After receiving permission to fix it, it proves the same symptom before and after the fix.
concurrency-and-shared-state
Design, debug, or review concurrent and asynchronous code for race freedom, state invariants, ordering, cancellation, resource ownership, and failure recovery. Use when an AI coding agent changes threads, tasks, queues, locks, caches, events, retries, or shared mutable state.
epistemic-coding
Apply an evidence-first philosophy while investigating, changing, or explaining a software repository. Use when correctness, uncertainty, scope, or the evidence behind a coding decision matters.
evidence-driven-debugging
Diagnose software failures by reproducing the symptom, separating observations from hypotheses, testing competing explanations, and making the smallest verified fix. Use for bugs, regressions, flaky behavior, failing builds, and unexplained runtime errors in AI coding-agent work.
performance-regression-analysis
Investigate and validate software performance changes using representative workloads, baselines, variance-aware measurements, and resource tradeoffs. Use when an AI coding agent is asked to make code faster, reduce latency or cost, increase throughput, or diagnose a performance regression.