swarm-advanced_skill

This skill helps orchestrate advanced distributed workflows using swarm topologies and agent strategies to accelerate research, development, and testing.

Python

0

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

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Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill chrislemke/stoffy --skill swarm-advanced

SKILL.md23.3 KB

Overview

This skill provides advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows. It bundles topology choices, agent strategies, and ready-made patterns to accelerate experiments, CI/CD, QA, and knowledge synthesis. The focus is practical: spawn agents, orchestrate tasks, monitor runs, and persist results for reuse.

How this skill works

The skill initializes swarms with chosen topologies (mesh, hierarchical, star, ring), spawns specialized agents, and assigns strategies (adaptive, balanced, specialized, parallel). It exposes orchestration primitives for parallel execution, sequential workflows, memory storage, pattern recognition, quality assessment, and monitoring. CLI fallbacks let you run quick swarm jobs when you need a fast, reproducible run.

When to use it

Parallel research and literature synthesis across many sources
Coordinated full-stack development requiring role separation and CI/CD
Comprehensive distributed testing, performance and security validation
Pipe-lined data processing or multi-stage analysis workflows
Prototyping complex distributed agent behaviors and failure modes

Best practices

Choose topology to match workflow: mesh for exploratory research, hierarchical for development, star for centralized testing, ring for pipelines
Limit maxAgents to what you can monitor and debug; scale gradually and observe bottlenecks
Use memory namespaces and TTLs to persist intermediate results for reproducibility and later analysis
Assign clear roles and capabilities to agents to avoid duplicated work and improve traceability
Combine parallel collection with sequential synthesis steps to ensure quality before report generation

Example use cases

Research swarm: parallel web, academic, and data collection then synthesize into a comprehensive report
Development swarm: architect-led hierarchy that splits backend, frontend, database, testing, and docs work with CI/CD deployment
Testing swarm: star topology runs unit, integration, e2e, performance and security tests in parallel with centralized reporting
Analysis swarm: sequential ring pipeline for large-scale data transformation and pattern detection across stages
Ad-hoc CLI jobs: run a quick distributed task from shell to produce a report or test run

FAQ

You can reconfigure swarms between workflows. For in-flight topology changes, orchestrate a controlled migration: spawn new swarm, shift responsibilities, and retire old agents to avoid state loss.

How do I persist and reuse research results?

Use memory storage primitives with namespaces and TTLs to store findings, knowledge graphs, and artifacts. Persist key outputs after validation step for later search and reuse.