Python: conda environment.yml is a dependency declaration; a docs-site static-analysis FAIL should not outweigh the product's PASS

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Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
55/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
anaconda, python
Área
documentation, tooling

Línea de trabajo

The bug is in the dependency-management check and the documentation static-analysis check of project_validator, which the payload does not name by file. Start by grepping the Python sources for requirements.txt to find the dependency message, then reproduce with a repo that contains only environment.yml. Done when the message names the conda file and the docs sub-result WARNs instead of FAILing when the product check passes.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

handson-ml3 and PythonDataScienceHandbook declare their environment in environment.yml (conda) but the message pretends only requirements.txt exists ("declared in requirements.txt but not locked"). Every Sphinx/MkDocs Python project FAILs static-analysis "Documentation site: no static analyzer runs; Python: ruff check runs" — with the product passing, the docs sub-result should at most WARN, and the hint should name an rst/markdown linter.

Evidence from the corpus audit

ageron/handson-ml3, jakevdp/PythonDataScienceHandbook / dependency-management
  • kind: UNSUPPORTED
  • actual: handson-ml3 WARN: dependencies are declared in requirements.txt (Python packages) but not locked; PDSH WARN: ... declared in requirements.txt (Python packages) but not locked
  • expected: the conda environment should be named as a declaration (and judged: environment.yml pins are not a lock file either, but the message should not pretend only requirements.txt exists)
  • evidence: handson-ml3 environment.yml:1-4: name: homl3 / channels: / - conda-forge / dependencies:; environment.yml:26: - python=3.10 # your beloved programming language! :); PDSH environment.yml (117 bytes); grep -rn -i "conda\|environment.yml" project_validator --include=*.py → nothing
  • repro: a repo with environment.yml only
  • confidence: high (conda is the dominant dependency tool of the Jupyter/data-science ecosystem)

Found by the regression-corpus audit (235 real repositories, one or more per ecosystem profile; corpus and expected results in corpus/). Evidence is quoted from the audited checkouts at the commits pinned in corpus/projects.toml; <checkouts>/<owner>__<repo> is such a checkout.

🤖 Generated with Claude Code

https://claude.ai/code/session_01ByTXNqDWdM7HzezrFtEvQt

Lenguaje dominante
Python
Estrellas
4
Forks
1
Merge medio
10 h 41 min
PR fusionados (30 d)
79

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

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  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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