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Agent Swarm Deployer Skill for Claude

by OneWave-AI

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.

5k tokens
context cost
the whole folder, loaded on every use
7
files
instructions only
0
copies elsewhere
how many repositories repackaged it
235
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/OneWave-AI/claude-skills --skill agent-swarm-deployer

The instruction itself

4 sections, as written by the author

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

  • 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.
  • 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).
  • 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.
  • 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.
  • 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. See references/agent-brief.md.
  • Distribute data and deploy. Choose a distribution method for the source type (pre-split CSVs/JSON with Bash; embed inline for small sets; pass file paths for directories). Launch up to 20 agents in parallel via the Agent tool with run_in_background: true, sending all calls in one message. Run later waves after the prior wave completes. See references/agent-brief.md.
  • Track progress. As agents return, record status, processed counts, and cumulative coverage. See references/agent-brief.md.
  • Collect and aggregate. Parse each agent's JSON; validate schema, completeness, and duplicates. Merge into one ordered output and extract failures. Report an aggregation summary with a coverage check and failure analysis. See references/aggregation-recovery.md.
  • Recover failures. Queue all failed and skipped items, deploy a retry agent with enhanced instructions, cap at 2 retries, and mark survivors "unrecoverable". Flag the user if unrecoverable items exceed 10%. See references/aggregation-recovery.md.

10. Write output and summarize. Produce the requested format (CSV, JSON, Markdown, or individual files) plus a final summary covering execution, results, quality metrics, patterns observed, and cost. See references/output-formats.md.

Anti-Patterns to Avoid

  • Do not use a swarm for sequential tasks. If item N depends on item N-1, use a chain instead.
  • Do not deploy one agent per item. Batch items; one-per-agent wastes overhead.
  • Do not skip schema definition. Without a schema, merging results from many agents becomes unreliable.
  • Do not ignore failures. At 99% success, 1% of 10,000 items is still 100 failures. Always run retries.
  • Do not deploy without a sample run. Process 5 items manually first to validate the task and output quality before scaling.

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How to use it

Copy the folder

Take onewave-ai/agent-swarm-deployer from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.