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/Nisus74/humaniseWrote 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/nisus74/humanise/check)<a href="https://agentmods.dev/commands/nisus74/humanise/check"><img src="https://agentmods.dev/badge/commands/nisus74/humanise/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/commands/nisus74/humanise/check"><img src="https://agentmods.dev/badge/commands/nisus74/humanise/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 | $0.00000 | $0.00172 |
| Opus 5.5 | $0.00000 | $0.00069 |
| Sonnet 5.5 | $0.00000 | $0.00034 |
| Haiku 4.5 | $0.00000 | $0.00017 |
Grade A, and why
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 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
humanise: check
Run the deterministic checker on a draft (no LLM). Wraps evals/assertions/writing_checks.py.
- Terminal:
npx humanise detect <file> [dialect] [medium] - In-tool: write the draft to a temp file, run
python3 evals/assertions/writing_checks.py <file> <dialect> [medium], then read the_summary(thefailedlist and thestructural_densityblock).
Use to catch mechanical and structural risks. The checker never sees the meaning contract, the reader or the user's voice, so a clean result is not proof that a draft is good. Treat advisory counts as prompts for review rather than targets. Use a separate adversarial reviewer only when the stakes or length justify it.
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 · 12 lines · 0 tokens per session scan A e8a5274fe455
check is a command published in the GitHub repository Nisus74/humanise (1 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 172 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 2026-08-31.
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code-review
Review code quality applying DRY, SOLID, naming, and formatting standards.
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Investigate the feature flag $ARGUMENTS and produce a detailed investigation for removal.