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feature-execution

Orchestrates agent-based feature delivery by wave, tracking progress and committing changes.

12|2|Updated Feb 2, 2020
Search Tags:#automation#workflow#code-review#wave-based#team-lead#feature-execution
One-click install
npx skills add https://github.com/servitola/dotfiles --skill feature-execution-servitola
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: feature-execution
Source: https://github.com/servitola/dotfiles/tree/main/claude-code/skills/feature-execution
Command: npx skills add https://github.com/servitola/dotfiles --skill feature-execution-servitola

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates complex feature delivery by coordinating multiple agents across waves, reducing manual overhead and ensuring structured reviews.

Core Features & Use Cases

  • Spawn agents by wave to execute tasks and monitor progress.
  • Manage review cycles (max 3 rounds) and commit per wave to maintain a clean history.
  • Support audit readiness with decisions.md records and escalation paths.

Quick Start

Describe the feature intent to initiate a wave-based agent orchestration.

Frequently Asked Questions about feature-execution

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

FAQPage Schema
How do I orchestrate multi-agent feature delivery with structured reviews?▼

You can orchestrate multi-agent feature delivery by spawning agents in waves to execute tasks, tracking progress, and committing changes per wave to maintain a clean history while ensuring structured reviews.

What is wave-based agent execution for feature tasks?▼

Wave-based agent execution is a phase-driven orchestration method where agents are spawned in sequential waves to complete feature tasks. It loads task frontmatter, monitors progress per wave, and commits changes incrementally.

How do I handle escalation paths when code review cycles exceed limits?▼

Escalation handling is built into the feature execution workflow, which manages code review cycles up to a maximum of three rounds. If issues remain, it triggers escalation paths and records decisions in decisions.md for audit readiness.

Can I use wave-based feature execution for team lead workflow automation?▼

Yes, wave-based feature execution is designed for team lead workflow automation. It reduces manual overhead by coordinating multiple agents, managing phase-driven execution, and committing changes per wave to maintain project momentum.

How do I track feature delivery progress and maintain audit readiness?▼

Feature delivery progress is tracked through wave-based agent monitoring and recorded in a decisions.md file. This file logs phase-driven execution decisions and review outcomes, ensuring full traceability and audit readiness.

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