What output does the skill provide after finishing a task?

It generates a structured .task/impl-result.json file containing the status, a summary of changes, lists of modified files, and any tests added.

What is the primary purpose of the implement-sonnet skill?

The skill is designed to act as the implementation engine in a multi-AI pipeline, taking a refined plan and writing the actual code and tests required to complete a task.

Does this skill write its own tests?

Yes, a core requirement of the skill is to write tests for all new functionality and verify the implementation before completion.

How does this skill handle existing code standards?

It reads dedicated reference documents, such as standards.md, to ensure all generated code complies with the project's specific style and quality guidelines.

Can it incorporate feedback from other AI agents?

Yes, it specifically looks for review files from other models (like Codex) in the .task directory to ensure previous feedback is addressed during implementation.

Sonnet Code Implementation

Name: Sonnet Code Implementation
Author: cskwork

bycskwork

생산성 및 워크플로

Executes technical plans and implements production-grade code using the Claude Sonnet model within automated multi-AI workflows.

소개

The Implement with Sonnet skill is a specialized capability designed for the execution phase of a multi-AI development pipeline. It acts as the primary 'coder' agent, taking refined task plans and transforming them into clean, tested, and standards-compliant source code. By leveraging the Claude 3.5 Sonnet model, this skill balances high-speed implementation with deep context awareness, ensuring that every modification is aligned with existing project architectures and previous reviewer feedback. It is particularly useful for teams looking to automate the transition from architectural planning to tangible code generation while maintaining strict quality control.

주요 기능

Plan-driven code implementation from structured JSON tasks
Detailed implementation reporting with structured JSON output
Strict adherence to project-specific coding standards and styles
0 GitHub stars
Automated unit and integration test generation for new features
Deep integration with multi-agent review and feedback cycles

사용 사례

Executing complex feature requests derived from multi-stage AI planning sessions
Automating the implementation of routine technical debt fixes and refactoring tasks
Generating production-ready code that incorporates feedback from AI-driven review agents

소개

주요 기능

Plan-driven code implementation from structured JSON tasks
Detailed implementation reporting with structured JSON output
Strict adherence to project-specific coding standards and styles
0 GitHub stars
Automated unit and integration test generation for new features
Deep integration with multi-agent review and feedback cycles

사용 사례

Executing complex feature requests derived from multi-stage AI planning sessions
Automating the implementation of routine technical debt fixes and refactoring tasks
Generating production-ready code that incorporates feedback from AI-driven review agents