How I Built a Full-Stack Quality Skill for AI Coding Agents
AI coding agents are getting very good at writing code.
But I kept running into the same problem:
They can move fast, but without strong project rules they can also create messy architecture, duplicate utilities, inconsistent APIs, weak security checks, and frontend components that slowly drift away from the design system.
So I built Full-Stack Quality Skill.
It is a reusable AI coding skill for full-stack audits, architecture guidance, long-term project memory, and CI quality gates.
Repo: https://github.com/lablnet/full-stack-quality-skill
Website: https://skills.lablnet.com
Why I Built It
When I use AI agents like Cursor, Codex, Claude Code, Antigravity, or similar tools, I do not only want them to "write code".
I want them to think like a careful senior engineer:
- Is the database normalized correctly?
- Are backend layers clean?
- Is business logic leaking into controllers?
- Are frontend components consistent?
- Are Vue components using composables?
- Are React components using hooks correctly?
- Are HTTP methods and status codes right?
- Is GraphQL safe from N+1 problems?
- Are security and privacy risks checked?
- Are tests missing for critical paths?
- Is documentation still matching the code?
That is a lot to remember every time.
So instead of repeating the same instructions in prompts, I turned them into a reusable skill.
What It Covers
The skill includes audit areas for:
- Database
- Backend
- Frontend
- Mobile
- HTTP APIs
- GraphQL
- Security
- Privacy
- Accessibility
- i18n
- Analytics
- Background jobs
- Infrastructure
- Testing
- Performance
- Observability
- Delivery / CI
- Multi-tenancy
- Payments
- Notifications
- Data import/export
- API compatibility
- Developer experience
- AI/LLM safety
It also includes examples for common stacks:
- Node.js / TypeScript
- Python
- Django
- Laravel
- Java / Spring
- C# / ASP.NET Core
- Go
- Ruby on Rails
- React
- Next.js
- Vue
- Angular
- SvelteKit
- Flutter
- React Native
- Kotlin / Android
- Swift / iOS
- SQL
- GraphQL
Read-Only Audits by Default
One important rule:
Audit mode is read-only.
That means the agent should not edit source code, schemas, configs, docs, generated files, or lockfiles unless I explicitly ask it to.
This matters because sometimes I want an honest review before touching anything.
For a small audit, it can produce:
review.md
findings.json
For a broader audit, it can produce per-area files:
review.md
findings/
backend.findings.json
frontend.findings.json
security.findings.json
database.findings.json
Markdown is for humans.
JSON is for structured audit output.
Parallel Auditors
One thing I really wanted was parallel review.
For a whole-project audit, the skill tells the agent to run separate read-only auditors in parallel:
- database auditor
- backend auditor
- frontend auditor
- security auditor
- testing auditor
- performance auditor
- delivery auditor
If the project does not use GraphQL, the GraphQL auditor is skipped.
This makes the review much cleaner than one huge generic pass.
Each auditor focuses on one area and returns evidence-backed findings.
Project Memory
The skill also has an optional project context mode.
That means it can help generate long-term project docs such as:
docs/
agents.md
architecture.md
decisions.md
security.md
testing.md
migration-backlog.md
audit/
inventory.json
drift-report.md
findings/
This is useful because AI agents need memory.
Not memory like "remember my favorite color".
Project memory like:
- What architecture did we choose?
- Where should new code go?
- What legacy code should not be copied?
- Which security rules matter?
- Which tests are required?
- What decisions have already been made?
Without this, every new AI session starts from zero.
Drift Detection
Another useful idea is drift detection.
Drift means the docs and the code no longer agree.
For example, the docs say:
All database access must go through repositories.
But later someone adds a controller that calls the database directly.
That is drift.
The skill can create a drift report so future agents do not blindly trust stale documentation.
CI Quality Gate
The skill can also be used as a CI-style review gate.
For example, on a pull request it can check:
- Did this change violate architecture boundaries?
- Did it introduce a security problem?
- Did it change an API without updating docs?
- Did it touch auth without tests?
- Did it add a new dependency without a decision?
This can be advisory or blocking depending on the project.
Installing in Cursor
For Cursor, I use it as a project tool:
git submodule add https://github.com/lablnet/full-stack-quality-skill.git .cursor/tools/full-stack-quality
Then create:
.cursor/rules/full-stack-quality.mdc
With:
---
description: "Full-stack quality review for database, backend, frontend, APIs, GraphQL, security, testing, performance, observability, delivery, and utilities."
---
Read .cursor/tools/full-stack-quality/SKILL.md and follow it.
Then I can ask:
Use full-stack-quality to audit the whole project read-only.
Run all relevant auditors in parallel.
Create review.md and findings/<area>.findings.json files.
Installing in Codex
For Codex-style agents:
git submodule add https://github.com/lablnet/full-stack-quality-skill.git tools/full-stack-quality
Then add this to AGENTS.md:
For full-stack quality reviews, read tools/full-stack-quality/SKILL.md
and follow it. Supporting files are in the same folder.
What I Learned
The biggest lesson is that AI coding agents need structure.
A better prompt helps.
But a reusable skill is much better.
It gives the agent:
- standards
- examples
- review rules
- output formats
- safety boundaries
- project memory
- CI review behavior
And once it is in the repo, the whole team can use the same rules.
Final Thought
AI coding tools are powerful, but power without taste and boundaries can make a codebase worse.
This skill is my attempt to give AI agents better engineering judgment.
Not by making them slower.
By making them more consistent.
If you want to try it:
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