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Fixing Flaky Tests

Community
rileyhilliard
fixing-flaky-tests

Diagnose and fix tests that pass in isolation but fail when run concurrently. Covers shared state isolation, resource conflicts, and timing-based flakiness.

Overview

Publisherrileyhilliard
Repositoryclaude-essentials
Skill namefixing-flaky-tests
Stars
127
Forks
19
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by rileyhilliard on GitHub. Read the source before you install it.

Installation

Install the Fixing Flaky Tests 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.
  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/rileyhilliard/claude-essentials.git /tmp/claude-essentials
mkdir -p .claude/skills
cp -r /tmp/claude-essentials/plugins/ce/skills/fixing-flaky-tests .claude/skills/fixing-flaky-tests
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fixing Flaky Tests 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 Fixing Flaky Tests 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 Fixing Flaky Tests 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.

If the current repo has its own rules/skills covering this topic (check .claude/rules/ and repo CLAUDE.md), those take precedence — apply this skill only where they're silent.

Fixing Flaky Tests

Target symptom: Tests pass when run alone, fail when run with other tests.

Diagnose first

Test passes alone, fails with others?
    ├─ Same error every time → Shared state
    │   └─ Database, globals, files, singletons
    ├─ Random/timing failures → Race condition
    │   └─ See async waiting patterns in `writing-tests` skill
    └─ Resource errors (port, file lock) → Resource conflict
        └─ Need unique resources per test/worker

Quick diagnosis:

  1. Run failing test 10x alone - does it always pass?
  2. Run failing test 10x with the suite - same error or different?
  3. Check error message - mentions port/file/connection?

Shared state (deterministic failures)

Tests pollute state that other tests depend on. Fix by isolating state per test.

State TypeIsolation Pattern
DatabaseTransaction rollback, savepoints, worker-specific DBs
Global variablesReset in beforeEach/afterEach
SingletonsProvide fresh instance per test
Module statejest.resetModules() or equivalent
FilesUnique paths per test, temp directories
Environment varsSave/restore in setup/teardown

Database isolation (most common):

python
# Python: Savepoint rollback - each test gets rolled back
@pytest.fixture
async def db_session(db_engine):
    async with db_engine.connect() as conn:
        await conn.begin()
        await conn.begin_nested()  # Savepoint
        # ... yield session ...
        await conn.rollback()  # All changes vanish
typescript
// Jest: Reset mocks between tests
beforeEach(() => {
  jest.clearAllMocks()
  jest.resetModules()  // Clear module cache before test
})

afterEach(() => {
  jest.restoreAllMocks()  // Restore spied functions
})

See language-specific references for complete patterns.

Race conditions (random failures)

Tests don't wait for async operations to complete.

See the writing-tests skill for async waiting patterns:

  • Framework-specific waiting (Testing Library findBy, Playwright auto-wait)
  • Custom polling helpers
  • When arbitrary timeouts are acceptable

Quick summary: Wait for conditions, not time:

typescript
// Bad
await sleep(500)

// Good
await waitFor(() => expect(result).toBe('done'))

Resource conflicts (port/file errors)

Multiple tests or workers compete for same resource.

Worker-specific resources:

python
# Python pytest-xdist: unique DB per worker
@pytest.fixture(scope="session")
def database_url(worker_id):
    if worker_id == "master":
        return "postgresql://localhost/test"
    return f"postgresql://localhost/test_{worker_id}"
typescript
// Jest/Node: dynamic port allocation
const server = app.listen(0)  // OS assigns available port
const port = server.address().port

File conflicts:

python
import tempfile

@pytest.fixture
def temp_dir():
    with tempfile.TemporaryDirectory() as d:
        yield d

Language-specific isolation patterns

StackReference
Python (pytest, SQLAlchemy)references/python.md
Jest / Testing Libraryreferences/jest.md
Playwright E2Ereferences/playwright.md
Async waiting patterns (TypeScript)writing-tests waiting-typescript
Async waiting patterns (Python)writing-tests waiting-python

Verification

After fixing, verify the fix worked:

bash
# Run the specific test many times
pytest tests/test_flaky.py -x --count=20

# Run with parallelism
pytest -n auto

# Jest equivalent
jest --runInBand  # First verify serial works
jest              # Then verify parallel works

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 Fixing Flaky Tests AI skill do?

Diagnose and fix tests that pass in isolation but fail when run concurrently. Covers shared state isolation, resource conflicts, and timing-based flakiness.

Why use Fixing Flaky Tests on TypingMind?

Because you install it once and use it with any model. Fixing Flaky Tests 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 Fixing Flaky Tests in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rileyhilliard/claude-essentials/tree/main/plugins/ce/skills/fixing-flaky-tests. 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 Fixing Flaky Tests?

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 Fixing Flaky Tests?

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

Is the Fixing Flaky Tests AI skill free?

Yes. It is published on GitHub by rileyhilliard 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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