AI Coding Context Pack Builder
Create a compact, reliable context pack that helps an AI coding assistant understand an existing app before making changes.
Vibe coders working in an existing repository who need to give an AI assistant enough context to make a targeted change safely.
You are a senior software maintainer who specializes in preparing codebase context for AI-assisted development.
Inputs:
1. App purpose and target users: {{app_context}}
2. Technology stack and repository structure: {{stack_and_structure}}
3. Requested change or current objective: {{requested_change}}
4. Relevant files, schemas, interfaces, and conventions: {{technical_evidence}}
5. Constraints, prohibited changes, and validation commands: {{constraints}}
Do the following:
1. Identify the minimum files, interfaces, data flows, configuration, and conventions the coding assistant must understand before changing anything.
2. Summarize the current behavior and trace the requested change through UI, state, API, database, authentication, and deployment layers where relevant.
3. Separate confirmed codebase facts from assumptions and unresolved questions, and flag missing evidence that could cause the assistant to edit the wrong layer.
4. Define invariants, scope boundaries, reusable patterns, files that must not change, and commands for tests, linting, type checks, builds, or migrations.
5. Produce a paste-ready context pack with repository map, change objective, relevant excerpts list, dependency notes, acceptance criteria, and a pre-edit checklist. Keep it concise enough to fit the model context without omitting safety-critical details.How to use
- Describe one concrete change at a time.
- Include relevant file paths and interfaces.
- State invariants and prohibited edits explicitly.
- Provide the exact validation commands used by the project.
Example input
App: Volunteer shift scheduler for neighborhood food banks. Stack: Next.js 15, TypeScript, Supabase, Tailwind, and Playwright. Change: let coordinators duplicate a weekly shift while excluding volunteer assignments. Evidence: shifts table, assignments table, server action patterns, coordinator role check, and existing duplicate-event utility. Constraints: do not change the database schema, preserve row-level security, use existing toast patterns, and run npm test, npm run lint, and the scheduling Playwright suite.
Example output
The context pack identifies the schedule page, shift server actions, role guard, duplicate-event utility, and assignment relationship as the minimum relevant surface. It states that only shift metadata should copy, while IDs, assignments, attendance, and audit fields must not. It flags recurrence handling as an unresolved question, defines permission and rollback invariants, lists validation commands, and instructs the assistant to propose a file plan before editing.
Customization tips
- — Include representative code excerpts rather than the whole repository.
- — Name an existing implementation pattern to copy.
- — Mark assumptions that require inspection.
- — Add a token budget when the repository is large.
Tags
FAQ
What is this prompt for?
How should I customize it?
Are there any limitations?
How is it different from a basic prompt?
Related Prompts
All Vibe Coding prompts →AI Coding Task Sequence Generator
Convert a feature brief into small, dependency-aware AI coding tasks with checkpoints that prevent one oversized generation from destabilizing the app.
AI-Generated Code Change Reviewer
Review an AI-generated patch against the requested behavior, repository conventions, security boundaries, and regression risks before accepting it.
App Idea to Build Brief Converter
Turns a rough app idea into a structured build brief with core features, data model, and page-by-page scope, ready to paste into a vibe-coding tool like Lovable or Bolt.
Related Articles
How do I use AI coding agents safely?
AI coding agents should work on controlled branches, avoid secrets, explain changes, run tests, and require human review before merging or deploying.
How do I use AI tools like Lovable or Cursor to build an app?
AI app builders work best when users provide a scoped feature request, data model, user flow, UI constraints, acceptance criteria, and test cases.
How do I test code generated by AI?
AI-generated code should be tested with unit tests, integration tests, manual checks, security review, and regression cases before production use.