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 instructions/sandeep-alluru/humanproof/claude-mdgit clone --depth 1 https://github.com/sandeep-alluru/humanproofWrote 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/instructions/sandeep-alluru/humanproof/claude-md)<a href="https://agentmods.dev/instructions/sandeep-alluru/humanproof/claude-md"><img src="https://agentmods.dev/badge/instructions/sandeep-alluru/humanproof/claude-md.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 | $0.00266 | $0.00266 |
| Opus 5 | $0.00133 | $0.00133 |
| Sonnet 5 | $0.00053 | $0.00053 |
| Haiku 4.5 | $0.00027 | $0.00027 |
Grade A, and why
humanproof CLAUDE.md 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 3d 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
humanproof — Session Anchor
Research spec: ../tech-research/14-Gaming/generative-motor-noise-fingerprinting-detecting-ai-by-mo/README.md
One-liner: Detect AI input from human neuromuscular SDE signatures — generalizes to bot/fraud detection
Phase: backlog
Stack: Python, numpy, scipy, scikit-learn
Key decisions
- README should read like a short paper — this project earns academic credibility
- Generalizes beyond gaming: bot detection, fraud, CAPTCHA replacement
Next step
Read the research spec carefully (SDE model of human motor control), then implement the hidden-state SDE fitting.
MVP definition
pip install humanproofworks- Fits hidden-state SDE model of human motor control to input event stream
- Computes likelihood ratio under human-plant model vs smoothed-AI model
- API:
humanproof.score(events) → (human_probability, confidence_interval) - Demo dataset: recorded human mouse input vs programmatic movement
- Demo: human input scores > 0.8, programmatic input < 0.3 consistently
- README reads like a mini-paper: problem, mathematical model, results, API
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.
- 3d ago First seen · 24 lines · 266 tokens per session scan A 039f60298240
humanproof CLAUDE.md is an instructions file published in the GitHub repository sandeep-alluru/humanproof (0 stars, last pushed 17d ago), licensed MIT. It adds 266 tokens to every session, about $0.0013 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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