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TECH & AI
Six Claude Code Skills That Close the AI Agent Feedback Loop
jackminion 0



AI agents write code that compiles, runs locally, and breaks the first time it touches your Kubernetes cluster. The cluster is full of state the model never sees: the env vars on the running pod, the schema in your real Postgres, the headers your upstream auth-service sends, the topics your consumer subscribes to. Without that context, the code an agent writes for your live infrastructure is informed guessing, whether you’re shipping a new feature or fixing a regression.

mirrord closes that gap. It runs a local process as if it were a real pod inside your cluster: real env vars, real DNS, real network, optionally real inbound traffic. A real example: Daylight Security pairs Cursor with mirrord for daily development. Their team cut their typical edit-test cycle from 5–8 minutes to about 5 seconds. The reason isn’t faster CPUs; it’s that the agent now operates against the real cluster the way a senior engineer would, instead of guessing from logs.

We recently shipped six Agent Skills that teach AI agents how and when to use mirrord. The whole bundle installs in one command.

# Claude Code
/plugin marketplace add metalbear-co/skills

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# Any Agent Skills consumer
npx skills add metalbear-co/skills

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Here’s what each skill does, with a concrete prompt that triggers it.

1. mirrord-quickstart

Zero-to-first-session for engineers (and agents) who have never used mirrord. Detects your OS, walks through CLI install or VS Code / IntelliJ setup, finds your target pod in the cluster, runs your first session. Your local process can now reach every service, database, and queue in the cluster.

Try: “I’m new to mirrord, help me run my Node app against my staging cluster.”

The agent installs mirrord, lists targets in your namespace, picks a likely match, and runs mirrord exec –target … — node server.js. No copy-paste from docs.

2. mirrord-config

Generates and validates mirrord.json, which tells mirrord what to do and where to do it. mirrord’s config surface is wide: traffic stealing vs mirroring, filesystem modes, env injection, target selection, database and queue behavior. The skill turns “I want X behavior” into valid config without you opening the docs.

Try: “Steal traffic from pod/api-server, but only requests carrying my baggage header so I don’t break anyone else’s session.”

The agent writes the right config, validates it against the schema, and explains what it does. The interesting part: the skill covers the full mirrord.json surface (target selection, traffic modes, env injection, file system hooks), not just filters. Filtered steal is one of the things that lets multiple developers share one cluster without colliding, but it’s only one of the patterns mirrord-config knows how to set up.

3. mirrord-operator

Sets up the mirrord Operator for teams. Mirroring traffic from a pod is concurrency-safe out of the box; you only need the operator when multiple developers want to steal the same pod’s traffic with different filters, share branched databases, or split a Kafka topic. The operator brokers session boundaries, RBAC, and the routing rules that make those interactions work without collisions.

Try: “Install the operator on our EKS cluster and configure RBAC so only the dev group can use it.”

monday.com runs 350+ engineers on a single shared staging cluster this way. The operator is what makes that scale work: concurrent filtered steal so multiple devs share one pod, queue splitting so they share one SQS topic, DB branching so they share one database, RBAC so they don’t touch workloads they shouldn’t, and the rest of the routing rules that let 350 developers work on the same cluster at the same time.

4. mirrord-ci

Run integration tests in CI in isolation against your staging cluster, instead of spinning up an ephemeral test environment for each PR. The service under test runs in the CI runner with mirrord; mirrord steals the cluster traffic destined for it and routes it to your build, so test traffic follows the same path it would in production, with only that one service swapped. That catches the integration bugs mocks miss, with one shared staging cluster instead of one ephemeral cluster per PR.

Try: “Set up GitHub Actions to run our integration tests against the staging cluster.”

The agent writes the workflow, injects your kubeconfig from a secret, sets MIRRORD_CI_API_KEY, and wires mirrord ci start around your service and mirrord ci stop in the cleanup hook.

5. mirrord-db-branching

Per-developer database branches. Copy-on-write Postgres (or any supported DB), so two engineers can develop against “the same” database without stepping on each other’s writes.

Try: “Give me an isolated DB branch off the staging Postgres for this feature.”

The agent provisions the branch via the operator, points your local process at the branch, and tears down when the session ends. No more “who deleted the test users?” Slack threads.

6. mirrord-kafka

Kafka queue splitting. Each developer gets a slice of the topic that only they consume, while the original consumer keeps running in the cluster. Lets you run a real Kafka workload locally without intercepting messages other people care about.

Try: “Set up queue splitting on the orders.created topic for my local consumer.”

The agent configures the operator’s Kafka splitter, gives your local process a per-developer consumer group, and confirms message routing.

Install

# Claude Code
/plugin marketplace add metalbear-co/skills

# Any Agent Skills consumer
npx skills add metalbear-co/skills

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Repo: github.com/metalbear-co/skills. Issues and PRs welcome; we ship updates fast.



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TECH & AI
How I Use Claude to Build Full-Stack Apps in Under 4 Hours — The Complete Workflow
jackminion 0



Three months ago, I spent 3 weeks building a SaaS dashboard. Last week, I built a more complex one in 3 hours and 42 minutes — using Claude as my co-pilot.

The difference wasn’t just “using AI.” It was a specific, repeatable workflow that eliminates the bottlenecks most developers hit when coding with AI.

Here’s exactly how I do it — step by step, with real prompts.

The Problem: Most People Use AI Wrong

I see developers making the same mistakes:

❌ Pasting entire codebases into Claude and hoping for the best
❌ Using vague prompts like “build me a dashboard”
❌ Not breaking down the problem before asking AI
❌ Copy-pasting AI output without understanding it
❌ Not using AI for the things it’s actually best at

The secret? AI is a junior developer that never sleeps, never gets bored, and has read every Stack Overflow answer ever written. But like any junior dev, it needs clear direction.

My 4-Hour Framework

I divide every project into 4 phases of ~1 hour each:

Phase
Time
What AI Does
What I Do

1. Blueprint
60 min
Generates architecture, tech choices
Define requirements, review plan

2. Scaffold
60 min
Generates boilerplate, database schema
Set up repos, configure env

3. Build
60 min
Writes core feature code
Review, test, iterate

4. Polish
45 min
CSS, error handling, edge cases
Final review, deploy

Let me walk through each phase.

Phase 1: Blueprint (60 Minutes)

Before writing a single line of code, I spend an hour planning with Claude. This is the most important phase and the one most people skip.

Step 1: Define the Problem

I start with a clear, structured prompt:

I’m building a SaaS product. Here’s what I need:

Product: A subscription analytics dashboard
Users: SaaS founders who want to track MRR, churn, and LTV
Data Source: Stripe API
Tech Stack: Next.js 14 (App Router), TypeScript, Prisma, PostgreSQL, TailwindCSS
Timeline: Need a working prototype today

Give me:
1. A complete database schema with all relationships
2. API route structure (REST endpoints)
3. Component hierarchy (what pages/components I need)
4. The order I should build things in (dependency graph)
5. Potential gotchas I might hit

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Why this works: Claude generates a concrete plan. No more “I’ll figure it out as I go.” You get a roadmap.

Step 2: Generate the Database Schema

Then I drill into each part:

Based on the schema you generated, write:
1. Complete Prisma schema with all models, relations, and indexes
2. Seed data (at least 20 records per model) that looks realistic
3. Migration SQL if needed

Format as a single `schema.prisma` file I can copy directly.

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Step 3: API Contract

For each API route, give me:
1. The endpoint path and HTTP method
2. Request body/params type (TypeScript interface)
3. Response type (TypeScript interface)
4. Authentication requirement
5. Brief description of what it does

Format as a TypeScript file with all types exported.

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Phase 1 output: You now have a complete spec — database schema, API types, component list, and build order. This would take 2-3 days to produce manually.

Phase 2: Scaffold (60 Minutes)

Now let AI generate all the boring stuff.

Generate Project Structure

Set up a Next.js 14 project with:
– App Router (not Pages Router)
– TypeScript strict mode
– TailwindCSS with these custom colors: (your palette)
– Prisma with PostgreSQL
– NextAuth.js for authentication (GitHub + email)
– shadcn/ui component library

Give me the exact commands to run and the folder structure.

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Generate Type Definitions

Create a complete `types/index.ts` file that includes:
– All database model types (from our schema)
– All API request/response types
– All component prop types
– Utility types (pagination, API response wrapper, etc.)

Make it fully typed. No `any` allowed.

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Generate Utility Functions

Write these utility functions:
1. `apiResponse(data, status, message)` — standardized API response
2. `validateRequest(schema, body)` — Zod validation wrapper
3. `paginate(query, page, limit)` — cursor-based pagination
4. `formatCurrency(amount, currency)` — i18n currency formatting
5. `calculateMRR(subscriptions)` — Monthly Recurring Revenue calc
6. `calculateChurn(subscriptions, period)` — Churn rate calc

Each function should be production-ready with proper error handling.

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Phase 2 output: A complete project skeleton with types, utils, auth, and database — ready to build features on top of.

Phase 3: Build (60 Minutes)

This is where the magic happens. I build features one at a time, using a specific prompt pattern.

The Feature Prompt Pattern

For every feature, I use this template:

Build me the (FEATURE NAME) feature.

Context:
– Tech stack: Next.js 14, TypeScript, Prisma, TailwindCSS, shadcn/ui
– Database schema: (paste relevant models)
– API types: (paste relevant types)

Requirements:
1. (Specific requirement 1)
2. (Specific requirement 2)
3. (Specific requirement 3)

Give me:
1. The API route code (app/api/…)
2. The React component code
3. Any Prisma queries needed
4. Test cases for edge cases

Important rules:
– Use Server Components by default, Client Components only when needed
– Handle loading states and errors
– Use optimistic updates where appropriate

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Example: Building the Dashboard Page

Build me the main dashboard page.

It should show:
1. Revenue chart (line chart, last 12 months) — use Recharts
2. Current MRR card with % change from last month
3. Active subscribers count
4. Churn rate card
5. Top 5 plans by revenue (horizontal bar chart)
6. Recent transactions table (last 10, with pagination)

Layout:
– Top row: 3 stat cards
– Middle row: Revenue chart (span 2/3), top plans chart (span 1/3)
– Bottom row: Recent transactions table (full width)

Use shadcn/ui Card, Table, and Badge components.

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The key here is specificity. I tell Claude:

Exactly which UI components to use
The exact layout I want
The exact data sources

Vague prompts = vague output. Specific prompts = production-ready code.

Phase 4: Polish (45 Minutes)

The last phase is where good apps become great apps.

Error Handling

Go through all API routes and add:
1. Input validation with Zod
2. Proper error responses (400, 401, 403, 404, 500)
3. Error logging
4. Rate limiting considerations

Also add a global error handler for unhandled exceptions.

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Edge Cases

For the dashboard, handle these edge cases:
1. No data yet (empty state with helpful message)
2. Very large numbers (format as K/M/B)
3. Negative growth (red indicators)
4. Stale data (show “last updated” timestamp)
5. Loading states for every async component
6. Mobile responsiveness (stack cards vertically on small screens)

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CSS Polish

Polish the dashboard UI:
1. Add subtle animations (fade-in for cards, chart animations)
2. Consistent spacing and border radius
3. Hover effects on interactive elements
4. Loading skeletons for all data components
5. Dark mode support (use CSS variables or Tailwind dark: prefix)

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Phase 4 output: A polished, production-ready app that handles errors gracefully and looks professional.

The Results

Using this workflow, here’s what I’ve shipped:

Project
Time
Features
Would’ve Taken (Manual)

SaaS Analytics Dashboard
3h 42m
Charts, tables, auth, CRUD
2-3 weeks

Blog Platform
4h 15m
CMS, auth, comments, SEO
1-2 weeks

E-commerce Admin
5h 10m
Inventory, orders, analytics
3-4 weeks

Task Management App
3h 55m
Kanban, real-time, teams
2 weeks

The key insight: I’m not asking Claude to build the entire app at once. I’m using it as a force multiplier in each phase, giving it clear, specific tasks.

5 Tips That Made the Biggest Difference

1. Never Ask AI to “Build an App”

Instead, ask it to build one feature at a time. “Build me a login page” works. “Build me a SaaS” doesn’t.

2. Always Generate Types First

Types are the contract between you and AI. Generate them in Phase 1, reference them in every prompt. This dramatically reduces hallucinations.

3. Use Claude Projects

Claude Projects let you attach files (schema, types, utils) that persist across conversations. This means you never have to re-paste context.

4. Review, Don’t Just Accept

AI will write code that works but might not be ideal. Always review:

Security (auth, input validation)
Performance (N+1 queries, unnecessary re-renders)
Accessibility (keyboard nav, screen readers)

5. Iterate with Specific Feedback

Instead of “this doesn’t look right,” say:

“The cards should be 1/3 width on desktop, full width on mobile”
“Add a subtle blue left border to the stat cards”
“The chart tooltip should show the exact date and amount”

Common Mistakes & How to Avoid Them

Mistake
Fix

Pasting 2000 lines of code
Share files via Claude Projects instead

“Fix this bug” with no context
Include error message, expected behavior, relevant code

Building everything at once
One feature, one prompt, one PR at a time

Ignoring AI warnings
Read every warning, investigate red flags

Not testing
Run code after every major generation, test edge cases

The Bottom Line

Claude (and AI in general) isn’t a magic wand. It’s a force multiplier that works best when you:

Plan first — Spend time on the blueprint before coding

Be specific — Detailed prompts = detailed output

Iterate fast — Small, focused tasks over big, vague ones

Review carefully — You’re the senior dev, AI is the junior

Use the right tools — Claude Projects, shadcn/ui, Prisma, etc.

With this workflow, I’ve gone from multi-week projects to multi-hour projects — without sacrificing quality.

What’s your AI coding workflow? I’d love to hear what’s working for you in the comments.

If you found this helpful, follow me for more AI developer content. I write about practical AI workflows, not hype.



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