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 skills add yeaight7/agent-powerups --skill ai-regression-testinggit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/ai-regression-testing)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/ai-regression-testing"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/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/yeaight7/agent-powerups/ai-regression-testing"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/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 | $0.00048 | $0.00917 |
| Opus 5.5 | $0.00019 | $0.00367 |
| Sonnet 5.5 | $0.00010 | $0.00183 |
| Haiku 4.5 | $0.00005 | $0.00092 |
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.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Regression Testing
When an agent writes code and then reviews it, it carries the same assumptions into both steps. Automated tests break this cycle.
When to Use
- An agent has modified logic, API routes, or data transformation code
- A bug was found — need to prevent re-introduction
- Running
/bug-checkafter a change session - Multiple execution paths exist (feature flags, sandbox vs production, env variants)
The Core Problem
Agent writes fix → Agent reviews fix → Agent says "looks correct" → Bug still present
The most common blind spot: an agent fixes the production path but leaves the sandbox/mock path unchanged, or vice versa.
Workflow
Run in order. Do not skip to agent review if automated steps fail.
Step 1 — Run Tests (mandatory)
npm test # or: pytest, cargo test, go test ./...
npm run build # TypeScript build / type check
- Test fail → highest priority; fix before anything else
- Build fail → report type errors as highest priority
- Both pass → continue to Step 2
Step 2 — Agent Code Review
With tests passing, do a focused review for patterns agents commonly miss:
- Execution path parity: Do all code paths (sandbox, production, feature-flag on/off) return the same response shape?
- Query completeness: Are all fields used in the response present in the query or selection?
- Error state cleanup: On error, is stale state cleared before the error is surfaced?
- Optimistic update rollback: If an API call fails, is the optimistic UI change reverted?
Step 3 — Write a Regression Test for Each Bug Fixed
For every bug found and fixed, add a test immediately:
Bug: <description>
File: <path>
Regression test: <test name and what it asserts>
If you cannot write a test, document why:
Bug: <description>
Regression test: DEFERRED — <reason> (e.g., requires E2E harness not yet in place)
Do not silently skip. Every real bug should either have a test or an explicit deferral note.
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 · 112 lines · 48 tokens per session scan A 23e058d9c59d
ai-regression-testing is a skill published in the GitHub repository yeaight7/agent-powerups (8 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 917 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-14.
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