Slop Report

Automatic PR quality reports for Python projects.

Slop Report posts a code quality summary as a comment on every pull request — coverage, blast radius, performance regressions, and maintainability — so reviewers have the data they need without leaving GitHub.

Metric	Score	Status	Details
Change Risk	72% covered	⚠️	28% of changed lines lack test coverage
Blast Radius	12 modules	🛑	High impact: auth, api, models affected
Performance	No regressions	✅	No tests exceeded 20% slowdown threshold
MI Regression	-10 pts	🛑	Worst: auth.py (80 → 70)
New File Quality	0.94×	⚠️	New files avg MI 68 vs main avg 72

Runs alongside your existing CI — it never blocks a merge, just surfaces the signal.

---

Quick Start

# .github/workflows/slop-report.yml
name: Slop Report

on:
  pull_request:
    types: [opened, synchronize, reopened]

permissions:
  pull-requests: write
  contents: read

jobs:
  slop-report:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0   # required — Slop Report diffs against the base branch

      - name: Run Slop Report
        uses: dlfelps/slop-report@v1
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}

---

Metrics

Metric	What it measures
Change Risk	% of added/modified lines covered by your existing test suite
Blast Radius	How many other modules import the files you changed
Performance	Per-test timing compared to the base branch — regressions are flagged
MI Regression	Worst absolute drop in Maintainability Index (pts) across modified files — detail links to the file's diff
New File Quality	Avg MI of added files divided by the base branch average — ratio < 1.0 means new code is less maintainable

---

Inputs

Input	Default	Description
github-token	${{ github.token }}	Token for posting the PR comment
enable-coverage	true	Run change risk / coverage analysis
enable-blast-radius	true	Run blast radius / dependency analysis
enable-performance	true	Run performance regression analysis
enable-maintainability	true	Run MI regression and new file quality analysis
coverage-threshold	80	Minimum % of changed lines covered (0–100)
performance-threshold	20	Max % slowdown before flagging a regression

---

Configuration Examples

Disable a metric

- uses: dlfelps/slop-report@v1
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    enable-performance: 'false'

Stricter thresholds

- uses: dlfelps/slop-report@v1
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    coverage-threshold: '90'
    performance-threshold: '10'

---

How It Works

Change Risk

Runs your test suite via coverage run -m pytest (auto-detected; falls back to unittest), then cross-references coverage data against the exact lines added or modified in the PR. Reports the percentage of those lines actually exercised by tests.

Blast Radius

Parses every Python file in the repository with Python's ast module to build an import graph. Identifies all files that import the modules you changed, and classifies impact as Low (≤3), Medium (4–10), or High (>10 affected modules).

Performance

Runs pytest --durations=0 on both the base branch and the PR branch using git archive, then compares per-test timings. Tests that slowed by more than the configured threshold are flagged, with the worst offender highlighted in the report.

MI Regression

Runs radon mi on each modified Python file and fetches the same file at the base branch via git show. Reports the worst absolute point drop across all modified files (e.g. -10 pts for a file that went from 80 → 70). The detail line names the worst file and links directly to its diff in the PR. Only files present in both branches are considered; newly added files are handled separately.

Score	Status
≥ 0 pts	✅ No regression
−10 to 0 pts	⚠️ Slight regression
< −10 pts	🛑 Significant regression

New File Quality

Runs radon mi on all Python files added in the PR and computes their average MI. Extracts the base branch via git archive and computes the average MI of all existing Python files. Reports the ratio: new files avg ÷ base avg.

Score	Status
≥ 1.0×	✅ New code meets or exceeds the existing baseline
0.9–1.0×	⚠️ New code is slightly below the baseline
< 0.9×	🛑 New code is significantly below the baseline

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Assumptions & Limitations

• Python only — all analysis (coverage, blast radius, maintainability) targets .py files. Other languages are ignored.
• pytest or unittest — the test runner must be one of these two. Coverage is collected via coverage run -m pytest; unittest is the fallback if pytest is not installed.
• Performance baseline requires a shared history — the base branch must be reachable via git archive origin/<base>. Shallow clones (fetch-depth: 1) will cause the performance metric to report N/A; use fetch-depth: 0.
• Performance compares only tests present in both branches — tests added or removed in the PR are excluded from the regression comparison.
• Blast radius uses static import analysis — dynamic imports (importlib, __import__) and conditional imports are not detected.
• MI Regression only covers files present on both branches — newly added files are measured separately by New File Quality; deleted files are ignored.
• Radon scores very short files leniently — files with fewer than ~10 lines tend to score near 100 regardless of quality.
• No blocking — Slop Report never fails the workflow. It only posts an informational comment.

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Requirements

• Python project with pytest or unittest
• fetch-depth: 0 on the actions/checkout step
• pull-requests: write permission for posting the comment

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Status Indicators

Icon	Meaning
✅	Metric is within acceptable range
⚠️	Metric is below threshold or needs attention
🛑	Significant issue detected