Metadata-Version: 2.4
Name: csv-quality-gate
Version: 0.2.0
Summary: CSV preflight validation and batch CSV quality checks that fail fast before pipeline runs.
Author: Rolando Bosch
License-Expression: MIT
Project-URL: Homepage, https://github.com/hermes-labs-ai/csv-quality-gate
Project-URL: Documentation, https://github.com/hermes-labs-ai/csv-quality-gate#readme
Project-URL: Repository, https://github.com/hermes-labs-ai/csv-quality-gate
Project-URL: Issues, https://github.com/hermes-labs-ai/csv-quality-gate/issues
Project-URL: Changelog, https://github.com/hermes-labs-ai/csv-quality-gate/blob/main/CHANGELOG.md
Keywords: csv preflight validation,batch CSV quality checks,fail fast,pipeline validation,csv lint
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Software Development :: Quality Assurance
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: dev
Requires-Dist: pytest>=8.0; extra == "dev"
Requires-Dist: ruff>=0.5; extra == "dev"
Dynamic: license-file

# csv-quality-gate

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[![PyPI](https://img.shields.io/pypi/v/csv-quality-gate.svg)](https://pypi.org/project/csv-quality-gate/)
[![Python](https://img.shields.io/pypi/pyversions/csv-quality-gate.svg)](https://pypi.org/project/csv-quality-gate/)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)

csv-quality-gate is a command-line data quality gate that runs CSV preflight validation, failing fast before a pipeline ingests broken, incomplete, duplicated, or junk input.

It runs batch quality checks on a single CSV and returns `pass`, `warn`, or `fail` (with matching exit codes) before expensive pipeline steps burn time on bad input. It checks for missing required columns, empty files, empty critical cells, duplicate rows, and — under the `outreach` profile — suspicious company-name patterns. It is stdlib-only: no third-party runtime dependencies.

The problems it is built for:

- "We keep running expensive pipeline steps on broken CSVs."
- "A batch run fails 20 minutes in because the input CSV was junk."
- "We only discover missing required columns after the job already started."
- "Duplicate rows and empty contact fields keep polluting our batch runs."
- "I want CSV preflight validation, not a whole data platform."

## Quickstart (60 seconds)

```bash
pip install csv-quality-gate
csv-quality-gate check leads.csv --profile outreach
```

Example output for a CSV with a missing column and a borderline duplicate rate:

```text
csv-quality-gate: FAIL
file: leads.csv
profile: outreach
rows: 125
  ERROR: missing required column: person_name
  WARNING: duplicate rate 12% exceeds warning threshold 10%
```

The process exits `0` on pass, `1` on warnings only, and `2` on fail, so you can wire it directly into a shell script or CI step.

![csv-quality-gate preview](assets/preview.png)

## Install

```bash
pip install csv-quality-gate
```

For development:

```bash
pip install -e ".[dev]"
```

## Usage

```bash
csv-quality-gate check leads.csv
csv-quality-gate check leads.csv --profile outreach
csv-quality-gate check leads.csv --profile generic --json
```

Exit codes:

- `0` pass
- `1` warnings only
- `2` fail (or `2` when the file does not exist)

## Profiles

Built-in profiles:

- `generic`
  - checks for a required `company` column, empty `company` cells, duplicate `company` values, and empty files
- `outreach`
  - requires `company` and `person_name`, with higher empty-rate tolerances and an added suspicious company-name heuristic for GTM/contact pipelines

The thresholds for each profile are defined in `src/csv_quality_gate/profiles.py`.

## Output

Text mode (default):

```text
csv-quality-gate: FAIL
file: leads.csv
profile: outreach
rows: 125
  ERROR: missing required column: person_name
  WARNING: duplicate rate 12% exceeds warning threshold 10%
```

JSON mode (`--json`) emits an object with `path`, `profile`, `rows`, `status`, and `issues[]`:

```bash
csv-quality-gate check leads.csv --json
```

## Limitations / What it does not do

- Heuristics are intentionally simple: empty-rate, duplicate-rate, and regex-based name patterns. They do not learn from your data.
- It validates shape and obvious noise, not semantic correctness — it cannot tell whether `company` values are real, only whether they are present, unique, and not obviously junk.
- The `outreach` profile is opinionated. Its suspicious-name patterns and thresholds were chosen for GTM contact lists and should not be treated as universal truth.
- Duplicate and empty checks operate on a fixed set of columns per profile (`company`, `person_name`); it does not auto-detect which columns matter.
- It validates one CSV file at a time and assumes UTF-8 (BOM-tolerant) input.
- It is not a data quality platform: no lineage, no profiling reports, no schema inference, no row-level remediation.

## When to use it

- Before enrichment, outreach, ETL, or batch scoring runs
- In CI for checked-in CSV inputs
- As a preflight gate before expensive pipeline work

## When not to use it

- When you need semantic validation of the data itself
- When your input is not CSV
- When you need a full data quality framework with lineage and profiling

## CI / GitHub Actions

Use this repository directly as a composite Action. It installs the packaged CLI,
runs the selected profile, and always writes a JSON receipt at
`$GITHUB_WORKSPACE/csv-quality-gate-receipt.json`. The `status` and `receipt`
outputs remain available even when the Action exits with a warning or failure.

```yaml
- id: csv_gate
  uses: hermes-labs-ai/csv-quality-gate@v0.2.0
  with:
    csv-path: data/leads.csv
    profile: outreach

- run: echo "${{ steps.csv_gate.outputs.status }}"
```

The only inputs are `csv-path` and `profile` (`generic` or `outreach`); the
Action deliberately accepts no free-form command or shell arguments. It returns
the same exit codes as the CLI: `0` for pass, `1` for warn, and `2` for fail.
Use `continue-on-error: true` on a calling step if your workflow needs to inspect
warning or failure outputs before deciding how to proceed.

The receipt path is fixed per workspace, so do not run more than one instance in
parallel in the same workspace. The Action validates the package's existing CSV
heuristics only; it does not add schema inference, semantic verification, or
arbitrary CLI options.

A ready-to-copy install-based workflow also lives in
[`examples/github-action.yml`](examples/github-action.yml).

## Development

```bash
pip install -e ".[dev]"
ruff check .
python3 -m pytest -q
```

## Part of the Hermes Labs reliability stack

csv-quality-gate is part of the [Hermes Labs](https://github.com/hermes-labs-ai) reliability stack — open-source tools that catch silent failure modes in production AI and data pipelines. csv-quality-gate guards the data that goes into a pipeline; it is complementary to, not a replacement for, the agent- and prompt-level tools in the stack.

## About Hermes Labs

[Hermes Labs](https://hermes-labs.ai) is an AI reliability engineering studio for product and engineering teams shipping production agents and LLM applications. We find the structural AI failures standard evals miss, then harden retrieval, memory, agents, and the language layers around production AI systems with runtime controls and defensible evidence.

Browse the [open-source catalog](https://hermes-labs.ai/open-source) or contact [roli@hermes-labs.ai](mailto:roli@hermes-labs.ai).

## License

MIT — see [LICENSE](LICENSE).

## Citation

If you use this software, please cite it using the metadata in [`CITATION.cff`](CITATION.cff).
