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 score

Show the Production Readiness Score (PRS) for your codebase.

Usage

quell score [PATH]
quell score [PATH] --badge
quell score [PATH] --json

Options

Option	Description
`--badge`	Output a shields.io badge URL for your README.
`--json`	Output score data as JSON.
`--format`	Output format: `table` (default), `json`, `badge`.

Example output

Production Readiness Score

Path: src/
Requirements found: 13
  ✓ WRITTEN:     8  (61.5%)
  ~ SCAFFOLDED:  3  (23.1%)
  ✗ FLAGGED:     2  (15.4%)

PRS: 84 / 100 — Production Ready

Breakdown by file:
  src/payments.py     92 / 100
  src/auth.py         76 / 100
  src/users.py        88 / 100

Badge

quell score --badge
# → https://img.shields.io/badge/PRS-84-brightgreen

Add to your README:

![PRS](https://img.shields.io/badge/PRS-84-brightgreen)

Score tiers

Score	Tier	Meaning
80–100	Production Ready	Edge cases are well-covered
60–79	Review Needed	Significant gaps remain
0–59	Needs Work	Many untested edge cases