AI SummarySpawn N parallel API workers for any long-running or multi-turn batch task. Auto-calculates optimal worker count and stagger delay. Supports tiered model delegation: orchestrator plans with a frontier model (V4 Pro), workers execute with a cheaper model (V4 Flash). DeepSwarm 2.0 generalizes the prov
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "deepswarm" skill in my project. Please run this command in my terminal: # Install skill into your project mkdir -p .claude/skills/deepswarm && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/deepswarm/SKILL.md "https://raw.githubusercontent.com/amanning3390/deepswarm/main/SKILL.md" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Use when running parallel AI workers for any long-running or multi-turn batch API task. Auto-calculates optimal workers + stagger. Supports tiered delegation (V4 Pro orchestrator → V4 Flash workers). 99.95% API success rate at scale.
Overview
DeepSwarm 2.0 generalizes the proven orchestration pattern from the 19,331-trace generation project to any batch API task. You define a task — translations, reasoning traces, code reviews, summarization — and DeepSwarm parallelizes it across optimal workers with the right stagger for your API. The core insight: API rate limits are a function of simultaneous connections, not total volume. Auto-calculated stagger + worker count = 99.95% success.
DeepSwarm — Task-Agnostic Parallel Worker Orchestration
Spawn N parallel API workers for any long-running or multi-turn batch task. Auto-calculates optimal worker count and stagger delay. Supports tiered model delegation: orchestrator plans with a frontier model (V4 Pro), workers execute with a cheaper model (V4 Flash).
When to Use
• Any batch API task: generation, translation, summarization, extraction, classification • Long-running individual calls (30s+) that benefit from parallelization • Multi-turn tasks where each worker loops through conversation turns • Cost optimization via tiered delegation (orchestrator ≠ worker model) • Crash-resilient batch processing (checkpointed, idempotent) Don't use for: • Quick calls under 10s (overhead not worth it — just loop) • Tasks requiring inter-worker coordination (use delegate_task) • Real-time interactive sessions (use tmux-agent-orchestrator)
Install
hermes skills tap add amanning3390/deepswarm
Discussion
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