Agentic Coding

Coordinate contract-first AI coding with PACT execution and acceptance checks.

Updated Apr 8, 2026
One-click install
npx skills add https://github.com/LudwigAJ/swell --skill agentic-coding-ludwigaj
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Agentic Coding
Source: https://github.com/LudwigAJ/swell/tree/main/.factory/skills/agentic-coding
Command: npx skills add https://github.com/LudwigAJ/swell --skill agentic-coding-ludwigaj

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It eliminates vague, open-ended AI coding by forcing a clear contract, measurable acceptance checks, and evidence-based completion before code is considered done.

Core Features & Use Cases

  • Contract-First Delivery: Define objective, acceptance, non-goals, and constraints before editing code.
  • PACT Workflow: Use a repeatable problem-framing, acceptance-design, change-set, and trace-and-test loop for reliable execution.
  • Proof-Driven Handoffs: Capture validation results, risks, and rollback steps so reviewers can trust the change.
  • Use Case: When you need a bug fix, feature, or refactor completed with minimal drift, this Skill keeps the work focused, testable, and ready for review.

Quick Start

Ask the AI to create a compact implementation contract for your coding task with objective, acceptance checks, non-goals, and constraints before making any changes.

Frequently Asked Questions about Agentic Coding

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

FAQPage Schema
How do I enforce a contract-first AI coding workflow to prevent scope drift?

Contract-first AI coding prevents drift by forcing a clear contract with objective, acceptance checks, non-goals, and constraints before editing code. This ensures measurable acceptance checks and evidence-based completion before code is considered done.

What is the best way to prepare AI-generated code changes for a reviewer handoff?

Preparing AI code changes for handoff requires capturing proof-driven evidence, including validation results, risks, and rollback steps. This ensures reviewers can trust the change based on objective, acceptance, and before-and-after verification.

How do I structure measurable acceptance checks for risky refactors using AI?

Measurable acceptance checks for risky refactors are structured using a PACT workflow. This repeatable problem-framing, acceptance-design, change-set, and trace-and-test loop ensures work remains focused, testable, and ready for production delivery.

Can I use this contract-first delivery workflow for Xcode-centered Swift development?

Yes, contract-first delivery applies to Xcode-centered Swift delivery workflows. It coordinates AI coding work by requiring objective, acceptance, non-goals, constraints, and PACT execution to complete production features and bug fixes with measurable acceptance checks.

Why does my AI coding task fail code review without before-and-after verification?

AI coding tasks fail review without before-and-after verification because they lack evidence-based completion. Defining a compact implementation contract with objective, acceptance checks, non-goals, and constraints ensures validation results and rollback steps are ready for reviewers.