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/tianyidatascience/humanize/claude-mdgit clone --depth 1 https://github.com/TianyiDataScience/humanizeWrote 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/tianyidatascience/humanize/claude-md)<a href="https://agentmods.dev/instructions/tianyidatascience/humanize/claude-md"><img src="https://agentmods.dev/badge/instructions/tianyidatascience/humanize/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 | $0.00337 | $0.00337 |
| Opus 5 | $0.00169 | $0.00169 |
| Sonnet 5 | $0.00067 | $0.00067 |
| Haiku 4.5 | $0.00034 | $0.00034 |
Grade A, and why
humanize 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 5d 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
Claude Code 使用说明
这个仓库可以直接在 Claude Code 中使用。
当用户要求使用 humanize 优化中文文案时，执行：
python3 humanize.py --text "{完整用户请求}" --output-root ./runs
如果是第一次使用，可以先运行：
python3 scripts/bootstrap_runtime.py
注意：
- 保留用户完整输入，尤其是
原文、原稿、正文后面的长文本。 - 不要传
--mode rewrite。 - 不要手写 challenger。
- 不要自己总结替代官方输出。
- 如果输出中有
=== HUMANIZE_FINAL_RESPONSE_BEGIN ===和=== HUMANIZE_FINAL_RESPONSE_END ===，最终只返回 marker 中间的 markdown。
如果当前环境没有可检测的宿主 active model，可以配置本地 OpenAI-compatible endpoint：
export HUMANIZE_GENERATION_BACKEND=local
export HUMANIZE_LLM_BASE_URL=http://127.0.0.1:54841/v1
export HUMANIZE_LLM_MODEL=<your-local-model-id>
如果你用的是 Ollama 上的 thinking 模型，建议再加：
export HUMANIZE_LLM_REASONING_EFFORT=none
如果在 Apple 芯片机器上想避免 scorer 首次走 MPS 带来的等待，也可以固定：
export HUMANIZE_SCORER_DEVICE=cpu
没有生成模型时，系统会降级到 heuristic-only，常见模板化文案仍可跑完整流程。
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.
- 5d ago First seen · 46 lines · 337 tokens per session scan A 59e1b0304f23
humanize CLAUDE.md is an instructions file published in the GitHub repository TianyiDataScience/humanize (91 stars, last pushed 4mo ago), licensed MIT. It adds 337 tokens to every session, about $0.0017 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 2026-08-30.
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