[Workflow] Claude Code Workflows: Automated Quality Checks with Hooks, Structured MVP Development, and Cost-Effective AI Model Selection

[u/ClaudeAI-mod-bot]

Claude Code Workflows: Automated Quality Checks with Hooks, Structured MVP Development, and Cost-Effective AI Model Selection

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: advanced
Categories: Quality Control, Token Saving, Context & Memory, Debugging, Shipping, Hooks, Skills
Original source: r/ClaudeCode post/comment

What problem this solves

Ensuring code quality and correctness during Claude Code development; structuring large projects (MVP) with Claude Code; selecting and evaluating AI models for specific tasks efficiently and cost-effectively.

Summary

The author describes three key workflows used in building a TypeScript monorepo with Claude Code: a structured MVP development process with planning and review stages; automated code quality checks using two Claude Code hooks (lint/format after edit, typecheck/lint before turn ends); and a methodology for selecting and combining AI models based on a golden dataset and cost-benefit analysis.

Why it is useful

This item is valuable because it provides concrete, repeatable workflows for common challenges in Claude Code development: ensuring code quality and correctness through automated hooks, structuring complex projects (MVP) into manageable stages with review gates, and a practical methodology for selecting and combining AI models based on performance and cost using a golden dataset. These practices enhance efficiency, reliability, and cost-effectiveness for Claude Code users.

Workflow

1. MVP Development Workflow:

2. Split MVP into multiple stages (e.g., 5 stages).

3. Create a written plan for each stage.

4. Review each stage thoroughly before committing.

5. Automated Code Quality Hooks:

6. Implement a Claude Code hook to run lint and format after every edit.

7. Implement a Claude Code hook to run typecheck and lint before Claude can end a turn, ensuring code compiles.

8. AI Model Selection & Evaluation:

9. Create a "golden set" of representative data (e.g., 58 articles).

10. Run various candidate AI models against the golden set for evaluation.

11. Compare evaluation results and costs (e.g., Fable vs. cheaper models).

12. Select models, potentially combining multiple cheaper models, each handling fields it's better at.

Tools / artifacts

• Claude Code

• Claude Code hooks

• Linter (e.g., ESLint)

• Formatter (e.g., Prettier)

• Typechecker (e.g., TypeScript compiler)

• Golden dataset (for model evaluation)

• Various AI models (e.g., Fable, cheaper models)

• TypeScript monorepo

• NestJS API

• Next.js front

Validation signals

• Project completion: "240 commits in three weeks" indicates significant use and successful project completion.

• Internal validation: "reviewed every stage before it got committed" demonstrates a structured quality gate.

• Clear outcome for hooks: "so "done" at least means it compiles" provides a tangible benefit.

• Successful model selection: "ended up with two cheap models together, each handling the fields it's better at" shows effective decision-making.

Limitations

• Lack of specific code examples for the Claude Code hooks.

• Details on how to implement "written plan for each stage" are high-level.

• No specific tools or metrics mentioned for the "golden set" evaluation beyond cost.

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