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.

quell reproduce

Turn a natural-language bug description into a failing pytest test.

Usage

quell reproduce "<bug description>"
quell reproduce "<bug description>" --file src/payments.py

Requirements

This command requires a configured LLM provider (Groq or OpenAI-compatible). Set up with:

quell auth set GROQ_API_KEY=gsk_...

Arguments

Argument	Description
`"<bug description>"`	A natural-language description of the bug to reproduce.

Options

Option	Description
`--file`	Path to the source file containing the relevant code. Auto-detected if omitted.
`--output`	Test output file. Default: auto-detect from existing test structure.
`--dry-run`	Generate the test but don't write it.

Example

quell reproduce "payment accepts zero amount and creates zero-value order"

Output:

def test_payment_accepts_zero_amount_bug():
    """Reproduces: payment accepts zero amount and creates zero-value order"""
    with pytest.raises(ValueError):
        process_payment(amount=0, currency="USD")

How it works

Quell sends your bug description + relevant source code to the LLM
The LLM generates a test that should fail on the current (buggy) code
Quell runs the test — if it passes, you've confirmed the bug is fixed
The test is verified through the 5-gate pipeline before writing

Only source code is sent to the LLM. No config files, secrets, or unrelated code.