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ai-assisted-development skill

/_skills/ai-assisted-development

This skill helps you accelerate coding with AI-assisted development while ensuring quality through review and manual refinement.

npx playbooks add skill baz-scm/awesome-reviewers --skill ai-assisted-development

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: ai-assisted-development
description: Leveraging AI coding assistants and tools to boost development productivity, while maintaining oversight to ensure quality results.
version: '1.0'
---
# AI-Assisted Development

AI is transforming how developers work. A large majority of developers are using or planning to use AI tools in their development process. Tools like GitHub Copilot and ChatGPT can generate code, write tests, and even help debug. The ability to effectively use these assistants is a new superpower – it can dramatically speed up routine tasks. However, developers must also critically review AI output because many still do not fully trust AI accuracy and see it struggle with complex tasks.

## Examples
- Using an AI pair programmer to generate a boilerplate module, then refining and optimizing that code manually.
- Asking an AI tool to explain a piece of code or suggest fixes for a bug, speeding up the debugging process while double-checking its suggestions.

## Guidelines
- **Embrace AI Tools:** Integrate AI assistants into your workflow for tasks like code generation, documentation drafting, and test case suggestions. These tools can significantly boost productivity for routine coding tasks.
- **Validate AI Output:** Always review and test code produced by AI. Treat AI suggestions as helpful drafts, not final solutions. It’s a critical skill to critique and improve AI-generated code – add missing error handling, fix inefficient logic, and ensure it fits your project’s style and requirements.
- **Stay in Control:** Use AI to augment your work, not replace your thinking. Be aware of known limitations (AI might produce insecure or incorrect code). Maintain rigorous quality practices (code reviews, tests) for AI-written code just as you would for human-written code to ensure reliability.

Overview

This skill helps developers use AI coding assistants to accelerate development while keeping human oversight to ensure quality. It provides practical prompts, workflows, and checks for agentic code review and everyday coding tasks. The focus is on safe, reproducible use of tools like Copilot, ChatGPT, and similar coding agents.

How this skill works

The skill supplies ready-to-use system prompts and review patterns that guide AI agents to produce code, tests, and explanations. It defines concrete inspection points for reviewing AI output, such as security, edge cases, and style compliance. The approach combines AI-generated drafts with automated and manual validation steps to produce trustworthy results.

When to use it

  • Generating boilerplate modules or repetitive code that benefits from fast drafts.
  • Writing unit tests, test scaffolding, or initial integration tests for new features.
  • Explaining unfamiliar code, suggesting bug fixes, or proposing optimization ideas.
  • Performing pull-request reviews where AI suggests improvements but humans verify changes.
  • Accelerating documentation, comments, and changelogs while preserving accuracy.

Best practices

  • Treat AI output as a draft: always review, run tests, and validate assumptions before merging.
  • Add missing error handling, input validation, and security checks that AI may omit.
  • Keep prompts specific: include language, framework, style rules, and desired edge cases.
  • Use small, focused prompts for review tasks to reduce hallucination and improve accuracy.
  • Pair AI suggestions with CI checks and peer code review to maintain quality and compliance.

Example use cases

  • Ask an agent to scaffold a REST endpoint, then manually integrate authentication and rate limiting.
  • Generate unit tests for a legacy function, run them, and refine failing assertions discovered by humans.
  • Use AI to propose refactors, then measure performance and confirm behavior with benchmarks.
  • Draft a pull-request description and checklist with the AI, and then validate each item during review.

FAQ

Can AI replace human reviewers?

No. AI speeds up review but cannot fully replace human judgment. Humans must verify correctness, security, and architecture decisions.

How do I reduce incorrect or insecure suggestions?

Give precise prompts, require tests, run static analysis, and add explicit security checks in your review checklist.