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Regression Testing

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nahid-sparktales
regression-testing

Turn a fixed bug into a test that fails on the old code and passes on the new one. Assert behavior at the cheapest useful level and compare old behavior without reverting unrelated work or leaving scratch files. Use after a fix or when recurring defects lack coverage; not for a new feature's test strategy or an environmental/cosmetic failure.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill nameregression-testing
Stars
58
Forks
5
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Regression Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/quality/regression-testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/quality/regression-testing .claude/skills/regression-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Regression Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Regression Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Regression Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Regression testing

A test written after the fix, run once, green, proves only that the code currently passes it. It becomes a regression test at the moment you watch it fail against the old code — until then it may be asserting nothing at all.

When this fires

  • A bug has been fixed and the fix is about to be reported as done.
  • A defect has recurred, so the previous fix had no test or the test did not cover the real case.
  • A test exists for a fix but was never observed failing without it.

It does not fire for new-feature coverage, for test strategy, or before the cause is understood — a test pinning a symptom you cannot explain pins the wrong thing.

Procedure

  1. Restate the defect as a behavioural assertion. Given this input and this state, the system should do this. If that sentence cannot be written, the diagnosis is not finished (systematic-debugging), and the test will end up asserting the implementation instead.
  2. Decide whether a test is the right tool at all. See When not to write one below. Write nothing rather than something that will be deleted as noise in a month.
  3. Pick the cheapest level that can actually observe the defect. Unit when the cause lives in one function; integration when it lives in the wiring between two; end-to-end only when the bug is invisible below the full stack. A slow test at the wrong level catches the bug once and taxes every run afterwards.
  4. Assert the observable contract, not the patch. Assert the returned value, the persisted row, the rendered output, the raised error. A test that asserts a mock was called, or pokes a private helper the fix introduced, will pass forever and catch nothing — including the same bug arriving by another route.
  5. Make the assertion specific to this defect. "Does not raise" passes for a function that returns nothing useful. Assert the value, the message, the count, the order — whatever the bug got wrong.
  6. Run it against the old code and watch it fail. Prefer an isolated in-memory old implementation or inline python3 -B -c 'CODE' check. Do not stash/revert the user's working tree merely to demonstrate failure. If a scratch copy is necessary and permitted, use a uniquely owned temporary-directory context with cleanup in finally, and verify its absence afterward. Never use a shared fixed /tmp name or a project sibling. Read the failure. This is the step that makes it a regression test rather than an assertion. The failure message should name the defect; if it says "expected true, got false", improve it now, because the next person to see it will be debugging under time pressure.
  7. Restore the fix and run it green. Both observations, in that order, are the deliverable.
  8. Make it deterministic. No real clock, no network, no sleeps, no dependence on test order or on a fixture another test mutates. Run it several times and on its own as well as in the suite. A flaky regression test gets skipped, then deleted, and the bug comes back unguarded.
  9. Name it for the behaviour, not the ticket. test_expired_token_is_rejected, with the issue link in a comment. test_bug_4417 tells a future reader nothing about what broke.
  10. Put it where the suite already keeps tests of that kind, then run the whole file and the suite once. A new test that quietly breaks an existing one through a shared fixture is a new bug, not coverage.

When not to write one

  • The failure was environmental — an expired certificate, a bad deploy value, a full disk. The guard belongs in configuration validation, a startup check or monitoring. A unit test asserting the certificate is valid tests nothing about the code.
  • The assertion would restate the implementation line for line — a corrected constant or a copy string, where the test is the same edit written twice and fails on every legitimate change.
  • The bug is timing-dependent and only reproduces occasionally. A test that fires one run in fifty is a future flake. Prefer a deterministic reproduction — injected clock, controlled scheduler, forced ordering — or assert the invariant that the race violates. If neither is reachable, say so rather than committing a coin flip.
  • The bug is in a dependency. Test your workaround and its trigger condition, not their defect.
  • The code is being deleted or rewritten within the change — the test would be born stale.

In each case the report says a regression test was deliberately not written, and why. That is a decision; silence looks like an oversight.

Checklist

  • The defect is stated as a behavioural assertion
  • The level chosen is the cheapest one that can observe it
  • The assertion is on observable behaviour, not on the fix's internals
  • The test was run against the old code and seen to fail
  • The failure message names the defect
  • The test passes with the fix restored
  • It is deterministic — run repeatedly, alone and in the suite
  • The full suite still passes
  • Or: no test was written, and the reason is recorded

Failure handling

  • It passes against the old code — it is not testing the defect. Go back to step 1; usually the assertion is on the wrong layer or too loose.
  • It fails against the old code for the wrong reason — an import error, a missing fixture. That is not a reproduction. Fix the harness and look again.
  • Old behavior cannot be compared within scope — report that limitation. Do not expand filesystem scope merely to obtain red/green proof. Reconstruct it in memory or use the permitted owned-directory procedure above; never edit shared/deployed code to force failure.
  • Cleanup fails or is denied — report the exact owned path as unresolved. Do not claim only the requested files were changed. A clean project diff does not cover temporary writes.
  • Reproducing it needs a real outward-facing or destructive action — a live payment, a real email, production data, a shared account. Stop and ask. Use a sandbox, a fixture or a recorded interaction; never wire a test to send real messages or mutate real records.
  • The suite has no place for this kind of test — say so and propose where it should live. Inventing a parallel test setup nobody runs is worse than no test.

Evidence to report

The test's path and name; the command that runs it; the failure output from the run against the old code, quoted; the passing output with the fix; and confirmation the rest of the suite still passes. A test that was created and one that was executed and one that was proven to fail without the fix are three different claims — report which you have. Where no test was written, report the reason in its place.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Regression Testing AI skill do?

Turn a fixed bug into a test that fails on the old code and passes on the new one. Assert behavior at the cheapest useful level and compare old behavior without reverting unrelated work or leaving scratch files. Use after a fix or when recurring defects lack coverage; not for a new feature's test strategy or an environmental/cosmetic failure.

Why use Regression Testing on TypingMind?

Because you install it once and use it with any model. Regression Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Regression Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/quality/regression-testing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Regression Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Regression Testing?

As many as you like. As long as a model supports skills, you can use Regression Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Regression Testing AI skill free?

Yes. It is published on GitHub by nahid-sparktales under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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