zenithth/anibon-stream-synthesis

batching-subagents-concurrency

Use when spawning multiple parallel subagents to process large datasets, transcript chunks, or batch workloads without hitting API rate limits or falling back to heuristics

First seen Aug 20, 2026

Installation

$ npx skills add zenithth/anibon-stream-synthesis --skill batching-subagents-concurrency

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Agent compatibility

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Repository health

Default branch main
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Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,429 B
  • docs SUMMARY.md 210 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Batching Subagents Concurrency

Overview

When processing long-form tasks (e.g., analyzing 20+ transcript chunks across an 8-hour livestream), spawning all subagents simultaneously exhausts model API rate limits (429 RESOURCE_EXHAUSTED). Batching subagents into controlled groups of maximum 10 (default 8–10 with Model: "flash") per invocation turn guarantees continuous execution without rate limits or fallback hallucinations.

When to Use

  • Launching 8 or more parallel subagents for chunk analysis, web scraping, or file processing.
  • Experiencing RESOURCE_EXHAUSTED (429) rate limit errors during subagent invocation.
  • Processing long livestreams or large multi-file repositories using anibon-chunk-timestamper.

When NOT to use:

  • Running 1–7 independent subagents (can be launched directly in a single turn).

Core Recipe (Batching Pattern)

1. Max 10 Subagents Per Batch Turn (Model: "flash")

Never exceed 10 subagents in a single invoke_subagent call (standard default: 8–10 subagents).

# ✅ GOOD: Controlled batch of up to 10 subagents with Flash tier
invoke_subagent(
    Subagents=[
        {"TypeName": "anibon-chunk-timestamper", "Role": "Group 00", "Model": "flash", "Prompt": "..."},
        {"TypeName": "anibon-chunk-timestamper", "Role": "Group 01", "Model": "flash", "Prompt": "..."},
        # ... up to 10 subagents per turn
        {"TypeName": "anibon-chunk-timestamper", "Role": "Group 09", "Model": "flash", "Prompt": "..."}
    ]
)
# Stop calling tools. Wait for Batch 1 messages before launching Batch 2.
# ❌ BAD: Spawning 20+ subagents in a single turn
invoke_subagent(Subagents=[... 23 subagent specs ...]) # Triggers API 429 RESOURCE_EXHAUSTED

2. Explicit Model Tier Selection (Model: "flash")

For high-volume data-reading and chunk-stamping subagents, explicitly pass Model: "flash" to utilize lighter model quotas and achieve 3x faster processing times.


Anti-Hallucination Guardrail (No Heuristic Fallbacks)

NEVER SUBSTITUTE FAILING SUBAGENTS WITH HEURISTIC GENERATION SCRIPTS.

If subagents fail or hit rate limits:

  • Do NOT run regex or heuristic fallback scripts that synthesize fake or repetitive placeholder timestamps.
  • Do kill lingering subagents (managesubagents(Action="killall")), reduce batch size to 6-8, switch model tier to "flash", and re-run subagents until 100% authentic outputs are produced from source data.

Rationalization Table

Excuse Reality
"Spawning all 23 subagents at once is faster." Triggers 429 rate limits, kills subagents, and wastes time retrying.
"If subagents fail, a heuristic Python script is a safe fallback." Heuristics generate hallucinated, generic timestamps that break accuracy contracts.
"I don't need to specify Model: 'flash'." Inheriting default heavy models burns API quota 5x faster.

Red Flags - STOP and Start Over

  • Launching >10 subagents in a single tool call.
  • Generating output using regex/heuristic text scripts when subagents fail.
  • Continuing to call tools in a loop without waiting for active batch subagents to complete.