Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/23ag1/completely/ai-regression-testingnpx skills add 23ag1/completely --skill ai-regression-testinggit clone --depth 1 https://github.com/23ag1/completelyWrote 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/23ag1/completely/ai-regression-testing)<a href="https://agentmods.dev/skills/23ag1/completely/ai-regression-testing"><img src="https://agentmods.dev/badge/skills/23ag1/completely/ai-regression-testing.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.02749 |
| Opus 5 | $0.00021 | $0.01375 |
| Sonnet 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 4d 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
91% identical to ai-regression-testing — 27 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.
- 4d ago First seen · 386 lines · 42 tokens per session scan A e29260b01cad
ai-regression-testing is a skill published in the GitHub repository 23ag1/completely (5 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 2,749 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to ai-regression-testing, differing in 27 lines, and is treated as a copy.
Other skills, from other repositories
write-documentation
Use when writing or substantially rewriting human-facing prose: documentation, README, guides, blog posts, emails, Slack messages, PR descriptions, release notes, or any text a human will read. Not for code comments, commit messages, or agent-to-agent communication.
stress-test
Use when a design, plan, or decision needs adversarial scrutiny before proceeding. Interrogates every branch of the decision tree, providing recommended answers and forcing explicit agreement or pushback. Triggers on "grill me", "stress test this", "poke holes", "challenge this design", or when…
writing-plans
Use when you have a spec or requirements for a multi-step task, before touching code.
getting-up-to-speed
Orients on an unfamiliar or stale codebase at the start of a session, after compaction, or whenever the project state is unclear. Loads beads context, deep-dives the codebase, and produces a structured 'current state' summary. Triggers on phrases like "catch me up", "where are we", "orient me", "what's the state of…
using-git-worktrees
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification.
extracting-requirements
Use when starting an iterative-development run on human spec collateral — reads the spec, produces per-epic requirement files with proof obligations and behavior scenario cards with stable IDs.