- name
- batch-file-processor
- description
- >
- Triggers
- batch process, file index, directory summary, bulk analyze, summarize files.
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" | sort2. 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": [...]}]