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.

GitHub Actions

A complete workflow that runs Quell on every push and pull request, with PR comments showing your PRS delta.

Quick setup

quell install github-action

Or create .github/workflows/quell.yml manually:

name: Quell

on:
  push:
    branches: [main, master]
  pull_request:

jobs:
  edge-cases:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"
          cache: pip

      - name: Install Quell
        run: pip install quelltest

      - name: Check edge cases
        run: quell ci src/ --threshold 75 --format json > quell-report.json

      - name: Upload report
        uses: actions/upload-artifact@v4
        if: always()
        with:
          name: quell-report
          path: quell-report.json

PR comment with PRS delta

Add a comment showing the score change on each PR:

      - name: Comment PRS on PR
        if: github.event_name == 'pull_request'
        uses: actions/github-script@v7
        with:
          script: |
            const report = require('./quell-report.json');
            github.rest.issues.createComment({
              issue_number: context.issue.number,
              owner: context.repo.owner,
              repo: context.repo.repo,
              body: `**Quell PRS: ${report.score}/100** — ${report.tier}\n\n` +
                    `✓ ${report.written} written · ~ ${report.scaffolded} scaffolded · ✗ ${report.flagged} flagged`
            });

Caching

Cache pip packages to speed up runs:

      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"
          cache: pip

Environment variables for LLM

If you use LLM fallback, add your key as a GitHub secret:

      - name: Check edge cases
        env:
          GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
        run: quell ci src/ --threshold 75