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ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

What is ai-regression-testing?

ai-regression-testing is a Claude Code agent skill that regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

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Documentation

AI Regression Testing

Testing patterns specifically designed for AI-assisted development, where the same model writes code and reviews it — creating systematic blind spots that only automated tests can catch.

When to Activate

  • AI agent (Claude Code, Cursor, Codex) has modified API routes or backend logic
  • A bug was found and fixed — need to prevent re-introduction
  • Project has a sandbox/mock mode that can be leveraged for DB-free testing
  • Running /bug-check or similar review commands after code changes
  • Multiple code paths exist (sandbox vs production, feature flags, etc.)

The Core Problem

When an AI writes code and then reviews its own work, it carries the same assumptions into both steps. This creates a predictable failure pattern:

AI writes fix → AI reviews fix → AI says "looks correct" → Bug still exists

Real-world example (observed in production):

Fix 1: Added notification_settings to API response
  → Forgot to add it to the SELECT query
  → AI reviewed and missed it (same blind spot)

Fix 2: Added it to SELECT query
  → TypeScript build error (column not in generated types)
  → AI reviewed Fix 1 but didn't catch the SELECT issue

Fix 3: Changed to SELECT *
  → Fixed production path, forgot sandbox path
  → AI reviewed and missed it AGAIN (4th occurrence)

Fix 4: Test caught it instantly on first run PASS:

The pattern: sandbox/production path inconsistency is the #1 AI-introduced regression.

Sandbox-Mode API Testing

Most projects with AI-friendly architecture have a sandbox/mock mode. This is the key to fast, DB-free API testing.

Setup (Vitest + Next.js App Router)

// vitest.config.ts
import { defineConfig } from "vitest/config";
import path from "path";

export default defineConfig({
  test: {
    environment: "node",
    globals: true,
    include: ["__tests__/**/*.test.ts"],
    setupFiles: ["__tests__/setup.ts"],
  },
  resolve: {
    alias: {
      "@": path.resolve(__dirname, "."),
    },
  },
});
// __tests__/setup.ts
// Force sandbox mode — no database needed
process.env.SANDBOX_MODE = "true";
process.env.NEXT_PUBLIC_SUPABASE_URL = "";
process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY = "";

Test Helper for Next.js API Routes

// __tests__/helpers.ts
import { NextRequest } from "next/server";

export function createTestRequest(
  url: string,
  options?: {
    method?: string;
    body?: Record<string, unknown>;
    headers?: Record<string, string>;
    sandboxUserId?: string;
  },
): NextRequest {
  const { method = "GET", body, headers = {}, sandboxUserId } = options || {};
  const fullUrl = url.startsWith("http") ? url : `http://localhost:3000${url}`;
  const reqHeaders: Record<string, string> = { ...headers };

  if (sandboxUserId) {
    reqHeaders["x-sandbox-user-id"] = sandboxUserId;
  }

  const init: { method: string; headers: Record<string, string>; body?: string } = {
    method,
    headers: reqHeaders,
  };

  if (body) {
    init.body = JSON.stringify(body);
    reqHeaders["content-type"] = "application/json";
  }

  return new NextRequest(fullUrl, init);
}

export async function parseResponse(response: Response) {
  const json = await response.json();
  return { status: response.status, json };
}

Writing Regression Tests

The key principle: write tests for bugs that were found, not for code that works.

// __tests__/api/user/profile.test.ts
import { describe, it, expect } from "vitest";
import { createTestRequest, parseResponse } from "../../helpers";
import { GET, PATCH } from "@/app/api/user/profile/route";

// Define the contract — what fields MUST be in the response
const REQUIRED_FIELDS = [
  "id",
  "email",
  "full_name",
  "phone",
  "role",
  "created_at",
  "avatar_url",
  "notification_settings",  // ← Added after bug found it missing
];

describe("GET /api/user/profile", () => {
  it("returns all required fields", async () => {
    const req = createTestRequest("/api/user/profile");
    const res = await GET(req);
    const { status, json } = await parseResponse(res);

    expect(status).toBe(200);
    for (const field of REQUIRED_FIELDS) {
      expect(json.data).toHaveProperty(field);
    }
  });

  // Regression test — this exact bug was introduced by AI 4 times
  it("notification_settings is not undefined (BUG-R1 regression)", async () => {
    const req = createTestRequest("/api/user/profile");
    const res = await GET(req);
    const { json } = await parseResponse(res);

    expect("notification_settings" in json.data).toBe(true);
    const ns = json.data.notification_settings;
    expect(ns === null || typeof ns === "object").toBe(true);
  });
});

Testing Sandbox/Production Parity

The most common AI regression: fixing production path but forgetting sandbox path (or vice versa).

// Test that sandbox responses match the expected contract
describe("GET /api/user/messages (conversation list)", () => {
  it("includes partner_name in sandbox mode", async () => {
    const req = createTestRequest("/api/user/messages", {
      sandboxUserId: "user-001",
    });
    const res = await GET(req);
    const { json } = await parseResponse(res);

    // This caught a bug where partner_name was added
    // to production path but not sandbox path
    if (json.data.length > 0) {
      for (const conv of json.data) {
        expect("partner_name" in conv).toBe(true);
      }
    }
  });
});

Integrating Tests into Bug-Check Workflow

Custom Command Definition

<!-- .claude/commands/bug-check.md -->
# Bug Check

## Step 1: Automated Tests (mandatory, cannot skip)

Run these commands FIRST before any code review:

    npm run test       # Vitest test suite
    npm run build      # TypeScript type check + build

- If tests fail → report as highest priority bug
- If build fails → report type errors as highest priority
- Only proceed to Step 2 if both pass

## Step 2: Code Review (AI review)

1. Sandbox / production path consistency
2. API response shape matches frontend expectations
3. SELECT clause completeness
4. Error handling with rollback
5. Optimistic update race conditions

## Step 3: For each bug fixed, propose a regression test

The Workflow

User: "バグチェックして" (or "/bug-check")
  │
  ├─ Step 1: npm run test
  │   ├─ FAIL → Bug found mechanically (no AI judgment needed)
  │   └─ PASS → Continue
  │
  ├─ Step 2: npm run build
  │   ├─ FAIL → Type error found mechanically
  │   └─ PASS → Continue
  │
  ├─ Step 3: AI code review (with known blind spots in mind)
  │   └─ Findings reported
  │
  └─ Step 4: For each fix, write a regression test
      └─ Next bug-check catches if fix breaks

Common AI Regression Patterns

Pattern 1: Sandbox/Production Path Mismatch

Frequency: Most common (observed in 3 out of 4 regressions)

// FAIL: AI adds field to production path only
if (isSandboxMode()) {
  return { data: { id, email, name } };  // Missing new field
}
// Production path
return { data: { id, email, name, notification_settings } };

// PASS: Both paths must return the same shape
if (isSandboxMode()) {
  return { data: { id, email, name, notification_settings: null } };
}
return { data: { id, email, name, notification_settings } };

Test to catch it:

it("sandbox and production return same fields", async () => {
  // In test env, sandbox mode is forced ON
  const res = await GET(createTestRequest("/api/user/profile"));
  const { json } = await parseResponse(res);

  for (const field of REQUIRED_FIELDS) {
    expect(json.data).toHaveProperty(field);
  }
});

Pattern 2: SELECT Clause Omission

Frequency: Common with Supabase/Prisma when adding new columns

// FAIL: New column added to response but not to SELECT
const { data } = await supabase
  .from("users")
  .select("id, email, name")  // notification_settings not here
  .single();

return { data: { ...data, notification_settings: data.notification_settings } };
// → notification_settings is always undefined

// PASS: Use SELECT * or explicitly include new columns
const { data } = await supabase
  .from("users")
  .select("*")
  .single();

Pattern 3: Error State Leakage

Frequency: Moderate — when adding error handling to existing components

// FAIL: Error state set but old data not cleared
catch (err) {
  setError("Failed to load");
  // reservations still shows data from previous tab!
}

// PASS: Clear related state on error
catch (err) {
  setReservations([]);  // Clear stale data
  setError("Failed to load");
}

Pattern 4: Optimistic Update Without Proper Rollback

// FAIL: No rollback on failure
const handleRemove = async (id: string) => {
  setItems(prev => prev.filter(i => i.id !== id));
  await fetch(`/api/items/${id}`, { method: "DELETE" });
  // If API fails, item is gone from UI but still in DB
};

// PASS: Capture previous state and rollback on failure
const handleRemove = async (id: string) => {
  const prevItems = [...items];
  setItems(prev => prev.filter(i => i.id !== id));
  try {
    const res = await fetch(`/api/items/${id}`, { method: "DELETE" });
    if (!res.ok) throw new Error("API error");
  } catch {
    setItems(prevItems);  // Rollback
    alert("削除に失敗しました");
  }
};

Strategy: Test Where Bugs Were Found

Don't aim for 100% coverage. Instead:

Bug found in /api/user/profile     → Write test for profile API
Bug found in /api/user/messages    → Write test for messages API
Bug found in /api/user/favorites   → Write test for favorites API
No bug in /api/user/notifications  → Don't write test (yet)

Why this works with AI development:

  1. AI tends to make the same category of mistake repeatedly
  2. Bugs cluster in complex areas (auth, multi-path logic, state management)
  3. Once tested, that exact regression cannot happen again
  4. Test count grows organically with bug fixes — no wasted effort

Quick Reference

AI Regression PatternTest StrategyPriority
Sandbox/production mismatchAssert same response shape in sandbox modeHigh
SELECT clause omissionAssert all required fields in responseHigh
Error state leakageAssert state cleanup on errorMedium
Missing rollbackAssert state restored on API failureMedium
Type cast masking nullAssert field is not undefinedMedium

DO / DON'T

DO:

  • Write tests immediately after finding a bug (before fixing it if possible)
  • Test the API response shape, not the implementation
  • Run tests as the first step of every bug-check
  • Keep tests fast (< 1 second total with sandbox mode)
  • Name tests after the bug they prevent (e.g., "BUG-R1 regression")

DON'T:

  • Write tests for code that has never had a bug
  • Trust AI self-review as a substitute for automated tests
  • Skip sandbox path testing because "it's just mock data"
  • Write integration tests when unit tests suffice
  • Aim for coverage percentage — aim for regression prevention

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

api-design

REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs. Use when designing or reviewing REST endpoints, resource names, status codes, pagination, or versioning.

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