Borrowing it
Nothing to install: this file belongs to ThreeFish-AI/negentropy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ThreeFish-AI/negentropy/master/.agent/skills/batch-processor/SKILL.mdgit clone --depth 1 https://github.com/ThreeFish-AI/negentropyWrote 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/skills/threefish-ai/negentropy/batch-processor)<a href="https://agentmods.dev/skills/threefish-ai/negentropy/batch-processor"><img src="https://agentmods.dev/badge/skills/threefish-ai/negentropy/batch-processor/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/skills/threefish-ai/negentropy/batch-processor"><img src="https://agentmods.dev/badge/skills/threefish-ai/negentropy/batch-processor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.01853 |
| Opus 5.5 | $0.00018 | $0.00741 |
| Sonnet 5.5 | $0.00009 | $0.00371 |
| Haiku 4.5 | $0.00005 | $0.00185 |
Grade A, and why
batch-processor 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.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Batch Processor
专门管理大文档的批次处理，协调完整的翻译工作流程（提取、翻译、格式化），确保在处理大型文档时的效率和稳定性。
批次规则
批次大小限制
- 最大页数: 30 页/批次
- 最大段落数: 60 段落/批次
- 最大字数: 6000 字/批次
批次划分策略
- 优先级: 页数 > 段落数 > 字数
- 边界处理: 在章节、段落等自然边界处分割
- 内容完整性: 确保每个批次的内容相对完整
Workflow
1. 文档分析
输入: 文档路径或内容
输出: 文档分析报告
分析内容：
- 文档类型（PDF/Web/Markdown）
- 总页数、段落数、字数
- 建议的批次数量
- 预估处理时间
2. 批次规划
输入: 文档分析报告
输出: 批次处理计划
规划内容：
- 批次数量
- 每个批次的范围
- 处理顺序
- 临时文件命名
3. 批次执行
输入: 批次处理计划
输出: 批次处理结果
执行步骤：
- 创建工作目录
- 按顺序处理每个批次
- 跟踪处理进度
- 保存中间结果
- 错误处理和重试
批次内处理流程
FOR EACH batch:
1. 内容提取
- PDF: 调用 pdf-reader
- Web: 调用 web-translator
2. 内容翻译
- 调用 zh-translator
3. 格式优化
- 调用 markdown-formatter
4. 质量检查
- 验证输出质量
5. 保存批次结果
END FOR
4. 结果合并
输入: 所有批次结果
输出: 最终合并文档
合并操作：
- 按顺序合并内容
- 处理批次间的衔接
- 清理临时文件
- 生成处理报告
使用方法
自动批次处理
请处理这个大文档，自动分成合适的批次：/path/to/large_document.pdf
自定义批次大小
处理这个文档，每批最多 20 页：/path/to/document.pdf
恢复中断的处理
继续处理上次未完成的文档，从第 3 批开始：/path/to/document.pdf
批次命名规范
临时文件格式
- 批次文件：
{doc_name}_batch_{N}.md - 图片目录：
{doc_name}_batch_{N}_images/ - 进度文件：
{doc_name}_progress.json - 错误日志：
{doc_name}_errors.log
最终输出
- 合并文档：
{doc_name}_translated.md - 图片目录：
{doc_name}_images/ - 处理报告：
{doc_name}_report.md
进度跟踪
进度信息格式
{
"document": "document.pdf",
"total_batches": 5,
"current_batch": 3,
"status": "processing",
"start_time": "2025-12-10T20:00:00Z",
"estimated_completion": "2025-12-10T20:30:00Z",
"batches": [
{
"batch_id": 1,
"status": "completed",
"pages": "1-20",
"processed_at": "2025-12-10T20:05:00Z"
},
{
"batch_id": 2,
"status": "completed",
"pages": "21-40",
"processed_at": "2025-12-10T20:12:00Z"
},
{
"batch_id": 3,
"status": "processing",
"pages": "41-60",
"started_at": "2025-12-10T20:18:00Z"
}
]
}
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 · 275 lines · 46 tokens per session scan A ecc356bdd522
batch-processor is a skill published in the GitHub repository ThreeFish-AI/negentropy (10 stars, last pushed yesterday), licensed Apache-2.0. It adds 46 tokens to every session and 1,853 once invoked, about $0.0002 per session on Opus 5.5. 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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