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batch-file-processor

面向目录内的大量文件,先列出文件并按每批 2-4 个分组,逐批派发子代理读取完整内容,完成摘要、字段抽取、主题分类或代码分析,再由主代理汇总为索引或报告,以避免主上下文溢出。

ddpie
ddpie
热度 14.49
最近更新 2026-07-27 04:04:28
分类:效率依赖:读写文件 / 代码执行 / 内部调用大模型应用场景:工作流 / 摘要总结 / 数据处理

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add batch-file-processor
Metadata
name
batch-file-processor
description
Parallel batch processing of large file sets using sub-agents (summarize, analyze, extract, transform). Use when performing the same operation across many files in a directory, such as generating file indexes/summaries, batch content analysis, bulk information extraction, or format conversion. Triggers: batch process, file index, directory summary, bulk analyze, summarize files. NOT for: single file operations (just read it directly), fewer than 5 files (manual is faster).

Batch File Processor

Process large numbers of files in parallel using sub-agents, avoiding main agent context overflow.

Workflow

1. List files

find <directory> -type f -name "*.md" | sort

2. Group

Split into batches of 2-4 files each (3 is optimal).

3. Dispatch sub-agents

One sub-agent per batch. Task template:

Read the following files completely and generate a brief summary (under 50 words) for each.
1. /path/to/file1.md
2. /path/to/file2.md
3. /path/to/file3.md
Return ONLY a JSON array:
[{"file": "relative/path/file1.md", "summary": "..."},...]

Key parameters:

  • mode: "run" (one-shot task)
  • runTimeoutSeconds: 120 (increase to 180 for large files)
  • label: descriptive label, e.g. idx-project-batch1

4. Collect results

Sub-agents push results on completion. Use sessions_yield to wait and collect incrementally.

5. Compile output

Once all results are in, the main agent compiles the final deliverable (index file, report, etc.).

Rules

  • 2-4 files per sub-agent — never let one sub-agent process an entire directory sequentially
  • Read full file content — no head/tail truncation; partial reads produce incomplete summaries
  • Standardize output format — JSON makes it easy for the main agent to parse and merge
  • One spawn per turn — system limitation; use multiple spawn + yield cycles

Anti-patterns

Mistake Consequence
head -20 to skim file headers Poor summary quality, key information missed
One sub-agent processes entire directory Context overflow, timeout failure
Main agent reads all files sequentially Context window exhausted, later files unreadable
One sub-agent per large directory Large directories timeout, small ones waste capacity

Benchmarks

70 files → 25 sub-agents (3 files each) → parallel execution → completed in 5 minutes → high accuracy summaries

Task Template Variants

File summarization (default)

Generate a brief summary (under 50 words) for each file.

Information extraction

Extract the following fields from each file: project name, budget, key contacts, risks.
Return JSON: [{"file": "...", "project": "...", "budget": "...", "contacts": [...], "risks": [...]}]

Content classification

Classify each file by checking for these topics: security, compliance, migration.
Return JSON: [{"file": "...", "has_security": true/false, "has_compliance": true/false, "has_migration": true/false}]

Code analysis

Analyze each source file: count lines, list imports/dependencies, identify main functions.
Return JSON: [{"file": "...", "lines": N, "imports": [...], "main_functions": [...]}]