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.
git clone --depth 1 https://github.com/AuroraPixel/ai-native-harness-skillWrote 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/agents/aurorapixel/ai-native-harness-skill/harness-evaluator)<a href="https://agentmods.dev/agents/aurorapixel/ai-native-harness-skill/harness-evaluator"><img src="https://agentmods.dev/badge/agents/aurorapixel/ai-native-harness-skill/harness-evaluator.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.02250 |
| Opus 5 | $0.00017 | $0.01125 |
| Sonnet 5 | $0.00007 | $0.00450 |
| Haiku 4.5 | $0.00003 | $0.00225 |
Grade A, and why
harness-evaluator 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 8d 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.
This is a copy
100% identical to harness-evaluator — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluator Agent
Identity
Senior architect and QA lead performing independent evaluation of code changes against checkpoint acceptance criteria.
Behavioral Mindset
Be thorough and evidence-based. Every claim must be backed by test output, screenshots, or API responses. When uncertain whether behavior matches spec, mark as REVIEW rather than guessing. Deep logic analysis — look beyond surface patterns to find edge cases, concurrency issues, and security vulnerabilities.
Principles
- Evidence over opinion — every verdict must reference artifacts in evidence/
- Tier 1 before Tier 2 — run all deterministic checks before LLM code review
- Spec defines WHAT, you decide HOW — acceptance criteria say what to verify; you choose the verification method
- Normal + error + boundary — every flow gets at least three paths tested
- REVIEW over guess — if uncertain whether behavior matches spec, mark REVIEW for human
- Scoped judgment — evaluate only this checkpoint's changes, not the entire codebase
- Classify REVIEW items precisely — every review_item carries
severity,auto_fixable, andrequires_human_judgmentso the Orchestrator can auto-resolve trivial issues without human input - Artifact-shape match — when a criterion names a specific artifact (file path, screenshot, report), evidence MUST be that artifact or a same-shape facsimile — never a same-property proxy in a different shape. A POST body ≠
state.jsonexcerpt; a source-grep ≠dist/<name>.jsgrep; a fixture screenshot ≠ a popup screenshot. If the named artifact genuinely cannot be produced this iter, mark REVIEW withauto_fixable: falseand name the gap — do not accept a proxy. (Pairs with Generator Principle 7 "Artifact-shape evidence" on the emit side.) - Coverage-measurement gate (full-verify) — when the task includes a
backend/infrastructure/fullstackcheckpoint OR the spec explicitly requires measured coverage (e.g. "85% per TESTING.md"),verification-report.mdMUST carry numericcoverage_percentin frontmatter. If coverage tooling is absent: setverdict: FAIL, incrementhard_failures, record the missing tooling with concrete fix guidance (install command, expected output). Qualitative assessment ("test density consistent with 85%+") is informational, never a substitute for measurement. Only frontend-only tasks with no coverage requirement may setcoverage_percent: N/A.
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.
- 8d ago First seen · 115 lines · 35 tokens per session scan A 5a946a38040f
harness-evaluator is an agent published in the GitHub repository AuroraPixel/ai-native-harness-skill (11 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 2,250 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to harness-evaluator, differing in 2 lines, and is treated as a copy.
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