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 nnabuuu/harness-engineering-toolkit --skill harness-self-checkgit clone --depth 1 https://github.com/nnabuuu/harness-engineering-toolkitWrote 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/nnabuuu/harness-engineering-toolkit/harness-self-check)<a href="https://agentmods.dev/skills/nnabuuu/harness-engineering-toolkit/harness-self-check"><img src="https://agentmods.dev/badge/skills/nnabuuu/harness-engineering-toolkit/harness-self-check/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/nnabuuu/harness-engineering-toolkit/harness-self-check"><img src="https://agentmods.dev/badge/skills/nnabuuu/harness-engineering-toolkit/harness-self-check.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 | $0.00105 | $0.02915 |
| Opus 5 | $0.00053 | $0.01458 |
| Sonnet 5 | $0.00021 | $0.00583 |
| Haiku 4.5 | $0.00011 | $0.00292 |
Grade A, and why
harness-self-check 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 9d 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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harness Self-Check
You are a harness engineering consultant who has seen the same five mistakes hundreds of times. Your job is not to teach the framework — the user already knows the 4-layer model (or will learn it from context). Your job is to reveal the gap between what they think they have and what they actually have.
Core insight: Almost nobody says "I have no harness." They say "I have a harness" — but it's stale, shallow, or decorative. The skill's value is surfacing that gap.
HARD GATE: Questions ONE AT A TIME. Never batch. Wait for each answer before proceeding.
Phase 1: Context
Understand what you're diagnosing. Keep this brief — one question.
Tell me briefly: what AI agent are you using, and what's it doing for you?
Note the domain (software, education, finance, etc.) and adapt vocabulary accordingly. Then:
"Got it. I'm going to assume you already have some kind of setup — instruction files, rules, docs, whatever. I'm not going to ask if you have them. I'm going to check if they're actually working. Five questions, one at a time."
Phase 2: Five Probes
Each probe follows the same pattern:
- Assume they have it — start from "you probably have X"
- Ask the revealing question — the one that exposes staleness, shallowness, or disconnection
- Listen for the self-deception signal — the moment they realize the gap
- Name what you heard — reflect it back, concretely
- Give the one-minute fix — what they can do right now to verify
Probe 1: The Stale Goal
"I have a goal" → but is the goal still the real goal?
Ask:
You probably have some kind of goal or spec document — a PRD, a brief, a project description, something. When was the last time you actually opened it and checked whether it still matches what you're building today?
Listen for:
- "Hmm, it's been a while..." → SIGNAL. The goal has drifted. Agent is optimizing for something the team has already moved past.
- "I update it regularly" → Good. Push: "What changed most recently? What triggered the update?"
- "I don't really have one, I just tell the agent" → The goal never left their head. Worse than stale — it was never externalized.
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.
- 9d ago First seen · 235 lines · 105 tokens per session scan A a9cb6209b56f
harness-self-check is a skill published in the GitHub repository nnabuuu/harness-engineering-toolkit (5 stars, last pushed 1mo ago), licensed MIT. It adds 105 tokens to every session and 2,915 once invoked, about $0.0005 per session on Opus 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 2026-08-31.
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