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Testing Codegen

Organization
modem-dev
testing-codegen

Use this skill when selecting or running Biome test fixtures, quick tests, `insta` snapshot workflows, expectation comments, orphan checks, or required code generators. Do not use for subsystem implementation design.

Overview

Publishermodem-dev
Repositoryossrules
Skill nametesting-codegen
Stars
65
Forks
2
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by modem-dev on GitHub. Read the source before you install it.

Installation

Install the Testing Codegen 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/modem-dev/ossrules/tree/main/public/files/biomejs/.claude/skills/testing-codegen
  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/modem-dev/ossrules.git /tmp/ossrules
mkdir -p .claude/skills
cp -r /tmp/ossrules/public/files/biomejs/.claude/skills/testing-codegen .claude/skills/testing-codegen
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testing Codegen 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 Testing Codegen 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 Testing Codegen 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.

Testing and Code Generation

Choose the narrowest test that exercises the changed behavior, then broaden only when shared infrastructure or integration risk justifies it.

Test Selection

ChangeStart with
Lint rulejust test-lintrule <ruleName>
One cratecargo test -p <crate> or the crate's focused test target
Parser or formatter investigationjust qt <package>
CLI migrationfocused biome_cli migration tests
Documentation codejust test-doc

Use -- --show-output or --nocapture only when the test's diagnostic output is needed.

Quick tests are scratch space for inspecting CST, formatter IR, or one analyzer query. Persistent behavior belongs in the subsystem's normal fixture directory before finishing.

Snapshot Workflow

Run the focused test to produce snapshots, then review every changed section:

shell
cargo insta review

Use cargo insta accept or cargo insta reject only after inspecting the pending changes. A passing snapshot test proves output matches the checked-in snapshot, not that the snapshot describes correct behavior.

Orphaned Snapshots

Do not delete suspected orphan snapshots manually. Deletion is safe only after running the complete workspace snapshot suite without package, target, or test filters:

shell
cargo insta test --workspace --unreferenced delete

For a scoped run, use --unreferenced warn or --unreferenced reject; incomplete test selection cannot prove that a snapshot is orphaned. Inspect every deletion from a complete run.

Analyzer Fixtures

Place rule fixtures under the language analyzer's current tests/specs/<group>/<rule>/ hierarchy. The directory group must match the rule declaration.

Use focused source files for parser-dependent cases. Use .jsonc arrays when multiple independent script snippets share the same configuration and module semantics are not required. Use options.json in a subdirectory when tests need different rule configuration.

Diagnostic Expectation Comments

The test utilities recognize these marker texts in source comments:

text
should generate diagnostics
should not generate diagnostics

Current enforcement:

  • a filename containing valid but not invalid must contain one of the expectation markers;
  • when either marker is present, actual diagnostics must match it;
  • an invalid or neutral filename without a marker is accepted, but adding the correct marker makes intent explicit;
  • .snap, .json, .jsonc, and .md are exempt from the mandatory valid-file marker check;
  • HTML-family workspace fixtures are checked from raw file content and should use a top-level HTML comment.

Put the marker at the top of a primary fixture. Sidecar files with neutral names do not need a marker unless they are independently analyzed as cases.

Parser Fixtures

Use the parser crate's established ok/ and error/ directories. A recovery regression should include valid syntax after the malformed construct to prove the parser resumes at the intended boundary.

Use just qt <parser-package> to inspect a CST during development; do not leave the quick test as the only regression coverage.

Formatter Fixtures

Use internal specs for behavior introduced or fixed by the change. External Prettier snapshots record comparison results but do not replace focused internal coverage.

The formatter harness performs its idempotency reformat during one test invocation for eligible files. Inspect both formatted output and any IR shown for a mismatch.

Required Code Generation

Changed sourceCommand
.ungram grammarjust gen-grammar <lang>
Formatter sourcejust gen-formatter <lang>
Lint rule or assistjust gen-rules and just gen-configuration
Bindings needed locallyjust gen-bindings

Root AGENTS.md is canonical for which artifacts must be committed and which full outputs CI Autofix may provide.

Do not run just ready in a dirty working tree: the recipe checks for a clean diff before and after its full verification sequence. Use the focused commands required by the current task, then just f and just l.

Completion Checklist

  • A code change has focused persistent coverage.
  • A bug fixture fails without the fix.
  • Snapshot contents were inspected.
  • Expectation comments match fixture intent.
  • Orphan snapshots were pruned through insta.
  • Required generated artifacts are present.
  • Narrow tests pass before broader checks run.

References

  • Main test guide: CONTRIBUTING.md#testing
  • Analyzer guide: crates/biome_analyze/CONTRIBUTING.md
  • Expectation enforcement: crates/biome_test_utils/src/lib.rs
  • Formatter harness: crates/biome_formatter_test/src/spec.rs
  • Generator recipes: justfile

Frequently asked questions

What does the Testing Codegen AI skill do?

Use this skill when selecting or running Biome test fixtures, quick tests, `insta` snapshot workflows, expectation comments, orphan checks, or required code generators. Do not use for subsystem implementation design.

Why use Testing Codegen on TypingMind?

Because you install it once and use it with any model. Testing Codegen 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 Testing Codegen in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/modem-dev/ossrules/tree/main/public/files/biomejs/.claude/skills/testing-codegen. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Testing Codegen?

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 Testing Codegen?

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

Is the Testing Codegen AI skill free?

Yes. It is published on GitHub by modem-dev 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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