Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/majiang213/OpenClaw-MASnpx agentmods add skills/majiang213/openclaw-mas/ai-regression-testingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/majiang213/openclaw-mas/ai-regression-testing)<a href="https://agentmods.dev/skills/majiang213/openclaw-mas/ai-regression-testing"><img src="https://agentmods.dev/badge/skills/majiang213/openclaw-mas/ai-regression-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/majiang213/openclaw-mas/ai-regression-testing"><img src="https://agentmods.dev/badge/skills/majiang213/openclaw-mas/ai-regression-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.02749 |
| Opus 5.5 | $0.00017 | $0.01100 |
| Sonnet 5.5 | $0.00008 | $0.00550 |
| Haiku 4.5 | $0.00004 | $0.00275 |
Grade A, and why
ai-regression-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-regression-testing — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-checkor 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, "."),
},
},
});
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 386 lines · 42 tokens per session scan A e29260b01cad
ai-regression-testing is a skill published in the GitHub repository majiang213/OpenClaw-MAS (4 stars, last pushed 6mo ago), licensed MIT. It adds 42 tokens to every session and 2,749 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ai-regression-testing, differing in 0 lines, and is treated as a copy.
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