Quell reads your docstrings, Pydantic models, and type annotations, extracts every testable requirement, finds which ones have no test, generates pytest tests via a rule engine, verifies each test through a 5-gate pipeline, and writes only proven tests to disk.

Does Quell require an LLM API key?

The rule engine runs entirely in-process — no source code is ever transmitted. ~75% of edge cases are handled with no network call and no API key. LLM fallback is opt-in and only sends the function signature, never the full body.

What is the 5-gate pipeline?

Every generated test must pass: Gate 1 (AST valid Python), Gate 2 (not already in a test file), Gate 3 (no shell calls or file writes), Gate 4 (passes against original code), Gate 5 (fails when the requirement is violated). Only gate-5-verified tests are written to disk.

What is the Production Readiness Score (PRS)?

PRS = (WRITTEN × 1.0 + SCAFFOLDED × 0.5) / total_requirements × 100. Tiers: 80-100 Production Ready, 60-79 Review Needed, 0-59 Needs Work.

How is Quell different from GitHub Copilot or Qodo for test generation?

Quell reads specifications that already exist in your code — it does not generate tests from scratch. It finds requirements already documented in your docstrings, Pydantic models, and type annotations that have no test. The 5-gate pipeline, especially Gate 5 (violation injection), verifies each test actually catches the bug it claims to catch. This verification step is not present in Copilot, Qodo, or Hypothesis.

Can Quell be used in CI pipelines?

Yes. Run quell ci src/ --threshold 80 to fail CI if PRS falls below 80. Set prs_threshold in pyproject.toml under [tool.quell]. Works with GitHub Actions, GitLab CI, and any system that checks exit codes.

CI/CD Integration

Run Quell in CI to enforce edge-case coverage on every pull request.

Basic setup

quell ci src/ --threshold 75

Exits non-zero if the Production Readiness Score is below 75. Use in any CI system that checks exit codes.

GitHub Actions

# .github/workflows/quell.yml
name: Quell edge case check

on: [push, pull_request]

jobs:
  quell:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - run: pip install quelltest
      - run: quell ci src/ --threshold 75

Or use quell install github-action to generate this automatically.

GitLab CI

quell:
  image: python:3.11
  script:
    - pip install quelltest
    - quell ci src/ --threshold 75

CircleCI

jobs:
  quell:
    docker:
      - image: cimg/python:3.11
    steps:
      - checkout
      - run: pip install quelltest
      - run: quell ci src/ --threshold 75

Setting thresholds

Configure in pyproject.toml to avoid repeating flags:

[tool.quell]
threshold = 75
paths = ["src/"]

Then just run quell ci with no arguments.

Recommended thresholds

Project stage	Threshold
Early development	60
Pre-launch	75
Production	80+

Start lower and ratchet up. The goal is to never go backwards.