atsushi-green/ds-ai-coding-skills
Grade a repo

67

/ 100

GradeC

Solid engineering. The biggest lever is CI.

Top 38% of 5,156 graded repos

A collection of AGENTS.md configurations, skills, and prompts tailored for Data Scientists to maximize productivity with AI coding assistants.

Documentation72
Engineering73
Health90
Python115MIT1mo ago

Top fixes

Highest-impact changes first, ranked by point weight

13 to address
  1. 1
    CI/CD14pt

    Add a step like `run: npm test`, `run: pytest`, or `run: tox` to your workflow file.

  2. 2
    CI/CD14pt

    Add a lint step to catch style issues automatically.

  3. 3
    CI/CD14pt

    Add `tsc --noEmit`, `mypy`, or `cargo check` to catch type errors before they merge.

  4. 4
    CI/CD14pt

    Upload coverage to Codecov, Coveralls, or report it with `--coverage` flags.

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Scorecard

Every check, grouped by category and sorted worst-first

Documentation

72

Contributing guide5pt0

No CONTRIBUTING.md found (−47 pts base + up to −53 pts more for content).

→ Add a CONTRIBUTING.md telling newcomers how to get involved. Include setup, code style, test, and PR instructions.

Install and run instructions9pt55

No install instructions found in the README (−45 pts).

→ Add a section showing how to install dependencies.

README12pt100

README is present.

License6pt100

Licensed under MIT.

Engineering

73

Issue and PR templates6pt0

No issue or PR templates found (−100 pts).

→ Add .github/ISSUE_TEMPLATE/ with bug_report.md and feature_request.md to guide contributors. It dramatically improves issue quality.

CI/CD14pt57

CI is configured (.github/workflows/ci.yml).

Reproducibility6pt80

Lockfile present (uv.lock). Installs are reproducible.

Tests18pt100

Test files detected (tests).

Linting and formatting5pt100

Linter or formatter configured ([tool.ruff] / [tool.black] in pyproject.toml).

Project health

90

Activity5pt80

Actively maintained (pushed within 3 months).

Dependency manifest6pt86

Dependency manifest found (pyproject.toml).

Repository metadata5pt100

Repository has a description.

Housekeeping3pt100

.gitignore present.

Repository health signals

Activity, community, and responsiveness at scan time

Activity

  • 0 / 52
    Commits (30d / 90d)
  • 8
    Forks
  • 0
    Releases

Community

  • 42% - Weak
    Community health
  • 1 bus factorlow
    author own >50% of commits
  • 115
    Watchers

Responsiveness

  • -
    Median issue response
  • <1h
    Median PR merge time
  • 0
    Open issues
Repository files21 root entries
  • .agents
  • .claude
  • .github
    Good: CI is configured (.github/workflows/ci.yml).
  • assets
  • data
  • docs
  • notebooks
  • outputs
  • scripts
  • src
  • tests
    Good: Test files detected (tests).
  • .env.example
  • .gitattributes
  • .gitignore
    Good: .gitignore present.
  • .python-version
    Good: Environment pinned via .python-version.
  • AGENTS.md
  • CLAUDE.md
  • LICENSE
    Good: Licensed under MIT.
  • pyproject.toml
    Good: Dependency manifest found (pyproject.toml).
  • README.md
    Good: README is present.
    Good: README is well structured with multiple sections.
    Good: README includes screenshots or visuals. Great for first impressions.
    Good: README has code examples.
    Good: README links to a live demo or deployed app.
    Good: README includes status badges.
    Issue: No install instructions found in the README (−45 pts).Fix: Add a section showing how to install dependencies.
    Good: README documents how to run the project.
  • uv.lock
    Good: Lockfile present (uv.lock). Installs are reproducible.
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