spawn-implementation-agents

Coordinate specialized sub-agents across five implementation phases with token budgeting.

10|2|Updated Dec 23, 2025

Search Tags:#testing #codebase #implementation #verification #agents #orchestration #token-management

Ready to run

npx skills add https://github.com/eveld/claude --skill spawn-implementation-agents

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: spawn-implementation-agents
Source: https://github.com/eveld/claude/tree/main/skills/spawn-implementation-agents
Command: npx skills add https://github.com/eveld/claude --skill spawn-implementation-agents

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrate specialized sub-agents during implementation to keep the main agent context under 40k tokens per phase.

Core Features & Use Cases

Five-phase orchestration pattern: Analysis, Implementation, Testing, Verification, and Documentation.
Parallel analysis with dedicated sub-agents to reduce main-context load.
Structured output: summaries instead of raw files, enabling efficient decision-making.
Guidelines for parallel vs sequential execution and safe token budgeting.

Quick Start

Spawn parallel sub-agents to analyze the codebase, implement, test, verify, and document in the five-phase orchestration pattern.

Frequently Asked Questions about spawn-implementation-agents

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I conserve agent context window when implementing features in a large codebase?▼

To conserve agent context during large codebase implementation, orchestrate specialized sub-agents in a five-phase model that keeps the main agent under 40k tokens per phase by offloading analysis, coding, testing, verification, and documentation tasks to sub-agents.

What is the five-phase orchestration pattern for AI agents?▼

The five-phase orchestration pattern for AI agents is a structured workflow model consisting of Analysis, Implementation, Testing, Verification, and Documentation phases, using parallel analysis and sequential verification to manage token budgets and enable safe data handoffs between agents.

How do I orchestrate parallel sub-agents for codebase analysis without exceeding token limits?▼

You can orchestrate parallel sub-agents for codebase analysis by spawning dedicated agents to analyze different parts simultaneously, requiring them to return structured summaries instead of raw files to reduce main-context load and keep token usage within the 40k limit per phase.

When do I need sub-agent orchestration for implementation workflows?▼

You need sub-agent orchestration for implementation workflows when working across large codebases where end-to-end tasks involving analysis, coding, testing, verification, and documentation threaten to exceed the main agent's context window capacity and degrade performance.

Does sub-agent orchestration work for end-to-end testing and verification workflows?▼

Yes, sub-agent orchestration works for end-to-end testing and verification workflows by applying sequential verification phases after parallel analysis and implementation, ensuring safe data handoffs between agents while maintaining a strict 40k token budget per phase.

What are the limitations of using a five-phase agent orchestration model?▼

A limitation of the five-phase agent orchestration model is the overhead of managing safe data handoffs between sub-agents, requiring strict adherence to sequential verification and parallel execution guidelines to prevent context overflow beyond the 40k token budget.