Batch Worker — 100 AI Agents in Parallel, Zero-Token Cleanup

Batch Worker: 100 AI Agents Running in Parallel

The Problem

Auditing a codebase takes hours when you go file by file. Content creation, search, fixes — every task is bottlenecked by sequential execution.

The Solution

Batch Worker is an OpenClaw skill that dispatches up to 100 AI agents in parallel with staggered launch to avoid rate limits.

Three-Step Pipeline

1. ai_planner -> Analyze project, generate audit plan with 100 soldier prompts
2. core_taskPipeline -> Dispatch 100 agents in staggered batches (10/batch x 20ms)
3. ai_collector -> Collect reports, deduplicate, rank by severity — zero LLM tokens

Enter fullscreen mode Exit fullscreen mode

104 Audit Dimensions

Domain Dimensions Security 42 (injection, XSS, CSRF, auth, encryption…) Architecture 12 (boundaries, cycles, idempotency, resilience) Performance 12 (N+1, caching, memory leaks, lock contention) Code Quality 10 (complexity, error handling, dead code) Language-specific 10 (Promise, async, EventEmitter, Stream) DevOps 8 (observability, CI/CD, config, containers) Compliance 4 (data privacy, audit trail, accessibility)

83 Task Types

From code audit to content creation, search to fix, translation to analysis — batch-worker handles them all.

Zero-Token Cleanup

After 100 agents finish, ai_collector automatically extracts JSON findings, deduplicates, and merges — using zero LLM tokens. Pure script, no hallucination.

GitHub: https://github.com/haoyun18881-beep/batch-worker
Docs: https://haoyun18881-beep.github.io/batch-worker/