sr-implement

Automate the OpenSpec lifecycle from GitHub issues to code artifacts.

9|2|Updated Mar 12, 2026

Search Tags:#architecture #documentation #pipeline #review #ai-agent #open-spec #github-issues

Ready to run

npx skills add https://github.com/fjpulidop/specrails-core --skill sr-implement

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sr-implement
Source: https://github.com/fjpulidop/specrails-core/tree/main/templates/skills/sr-implement
Command: npx skills add https://github.com/fjpulidop/specrails-core --skill sr-implement

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

sr-implement orchestrates a complete OpenSpec lifecycle using specialized AI agents (architect, developer, reviewer, and doc-sync) to transform GitHub issues or feature descriptions into ready-to-ship specifications and code artifacts.

Core Features & Use Cases

End-to-end OpenSpec pipeline with dedicated agents for architecture, implementation, validation, testing, and documentation.
Supports GitHub issues or textual features, coordinating multiple phases while ensuring traceability and auditability.
Handles conflict detection, multi-feature merges, and reviewer feedback with a deterministic workflow.

Quick Start

Install and run the sr-implement pipeline to process a backlog item or issue, providing either an issue number or a natural-language feature description to begin.

Frequently Asked Questions about sr-implement

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

FAQPage Schema

How do I automate feature implementation from GitHub issues using AI agents?▼

You can automate feature implementation from GitHub issues by running a deterministic OpenSpec pipeline that assigns specialized AI agents to architecture, development, review, and documentation phases. It transforms issue descriptions into ready-to-ship code and specifications.

What is the OpenSpec lifecycle for software development?▼

The OpenSpec lifecycle is a deterministic workflow that uses AI agents to design, implement, test, and document features. It enforces mandatory phases like architecture, development, review, and doc-sync to ensure traceability and auditability from issue to deployment.

Can I process a multi-issue backlog with AI agents in a single pipeline run?▼

Yes, AI agents can process multi-issue backlogs by coordinating the architect, developer, reviewer, and doc-sync phases across multiple issues. The pipeline handles multi-feature merges and conflict detection within its deterministic workflow.

Do I need git and compatible AI agents to run the OpenSpec pipeline?▼

Yes, you need git and compatible AI agents to run the OpenSpec pipeline. The deterministic workflow orchestrates these agents through mandatory architecture, implementation, testing, and documentation phases to generate code artifacts from your features.

How does the pipeline handle reviewer feedback and code conflicts?▼

The pipeline handles reviewer feedback and code conflicts through a deterministic workflow that includes conflict detection and reviewer phases. AI agents coordinate to resolve issues during the implementation and validation stages before finalizing documentation.

Is there a dry-run mode for testing AI agent feature implementation before committing?▼

Yes, the AI agent feature implementation pipeline supports an optional dry-run mode. This allows you to test the deterministic OpenSpec workflow and validate the architect, developer, and reviewer phases before applying actual code changes.