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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

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Precio sin confirmar★ 287 Estrellas de GitHubRegistro actualizado · 6 sept 2026agent-skill

Resumen

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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.

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.
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.

Ver fuente ↗

Metadatos del archivo

name: agent-swarm-deployer
description: 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.
tools: Read, Write, Bash, Agent, Glob, Grep
user_invocable: true

Ver texto original

---
name: agent-swarm-deployer
description: 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.
tools: Read, Write, Bash, Agent, Glob, Grep
user_invocable: true
---

# Agent Swarm Deployer

## 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. See `references/agent-brief.md`.

6. 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`.

7. Track progress. As agents return, record status, processed counts, and cumulative coverage. See `references/agent-brief.md`.

8. 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`.

9. 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

1. Do not use a swarm for sequential tasks. If item N depends on item N-1, use a chain instead.
2. Do not deploy one agent per item. Batch items; one-per-agent wastes overhead.
3. Do not skip schema definition. Without a schema, merging results from many agents becomes unreliable.
4. Do not ignore failures. At 99% success, 1% of 10,000 items is still 100 failures. Always run retries.
5. 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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Precio y costes de ejecución

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Licencia: MIT
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Fuente del skill registrada

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Revisar antes de instalar: Evitar instalación automática

Licencia: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
No explicit security guidance on handling untrusted input data (e.g., prompt injection risks when processing documents).
Skill references 'agent-army' but does not provide a link or explanation; may confuse users.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 287 stars, 43 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Abrir auditoría completa

Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

Indexado

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: OneWave-AI/claude-skills
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 11 ago 2026
Registro actualizado: 6 sept 2026
Ruta de instrucciones: agent-swarm-deployer/SKILL.md @ 82859c0ebaff

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

68/100

Prometedor

Confianza

57/100

Do not auto-install

Auditoría

73/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

Dependency or permission surface needs review
Permission surface may require sandboxing
No explicit security guidance on handling untrusted input data (e.g., prompt injection risks when processing documents).
Skill references 'agent-army' but does not provide a link or explanation; may confuse users.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 287 stars, 43 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

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Más detalles

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Creador: OneWave-AI
Fuente: OneWave-AI/claude-skills
Indexado por: Índice comunitario de OpenAgentSkill

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