Agent skill · data analytics · onewave-ai
agent-swarm-deployer
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
Why this skill is useful
Adds a comprehensive workflow for deploying parallel sub-agents, including error recovery and output aggregation scripts that enhance data processing capabilities.
What it needs
Requires bash installed locally. About 2k tokens when loaded. Last updated 2026-07-15. 242 stars on the source repository.
What this skill does
Agent Swarm Deployer Deploy a swarm of parallel sub-agents to process massive, independent data tasks (documents, records, rows, items) and aggregate the results. Use this for data operations; use agent-army for code changes. Contents references/overview.md -- swarm vs army comparison, use cases, architecture diagram references/swarm-design.md -- input/output schemas, batch-size and swarm-size formulas, scaling guidelines references/agent-brief.md -- agent brief template, data distribution methods, progress tracking references/aggregation-recovery.md -- merge logic, completeness validation, retry strategy, error-handling table references/output-formats.md -- CSV/JSON/Markdown/individual-file outputs, final summary report references/task-configs.md -- ready-made configs for sentiment, lead scoring, content generation, summarization Workflow 1. Understand the task. Pin down five things before deploying anything: data source, operation per item, output format, output destination, and quality/validation requirements. If any is ambiguous, ask the user first -- a wrong spec wastes all agent compute. 2. Intake and inventory. Glob/Bash to locate and count items. Read 3-5 samples to learn structure. Estimate tokens per item and total. Report an intake summary (source, total count, item format, sample structure, token estimate). 3. Detect input schema and define output schema. Derive the input schema from samples; define the exact output schema the task requires. See references/swarm-design.md. 4. Design the swarm. Compute batch size from token budget (70% of ~200K usable context per agent) and swarm size from total items. Cap at 20 agents per wave; split into waves beyond that. Present the swarm plan and agent assignments, then get approval. See references/swarm-design.md. 5. Prepare agent briefs. Build a self-contained brief per agent: role, task, input data, output schema with example, quality rules, error protocol, and strict JSON output format. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills onewave-ai/agent-swarm-deployer