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 agentmods add commands/nisus74/humanise/fingerprintgit 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/fingerprint)<a href="https://agentmods.dev/commands/nisus74/humanise/fingerprint"><img src="https://agentmods.dev/badge/commands/nisus74/humanise/fingerprint.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 | $0.00000 | $0.00144 |
| Opus 5 | $0.00000 | $0.00072 |
| Sonnet 5 | $0.00000 | $0.00029 |
| Haiku 4.5 | $0.00000 | $0.00014 |
Grade A, and why
fingerprint 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 4d 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: fingerprint
(Re)generate the user's voice fingerprint from their corpus. Run after adding profile/sample-*.md samples.
Follow scripts/generate-fingerprint.md: read every raw sample, the negative examples and the user's
recorded edits. Extract decisions before surface habits. Label each pattern confirmed, supported or
provisional, cite its evidence, write profile/voice-fingerprint.md, and name the channel and
relationship gaps. The same step may rebuild the advisory voiceprint baseline.
Direct draft-to-final edit pairs are stronger than another polished sample. Regenerate after roughly five useful additions or when a channel first gains direct coverage. Never turn one occurrence into a personal rule.
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.
- 4d ago First seen · 13 lines · 0 tokens per session scan A f46852f7ab62
fingerprint is a command published in the GitHub repository Nisus74/humanise (1 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 144 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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