Repository statistics infographic

Use when asked to consolidate repository-statistics slides into one image, regenerate the infographic with updated data, or verify its calculations. This is a reusable six-panel statistical template, not an arbitrary PowerPoint screenshot collage.

The panels cover inferred PR initiation, merge/closed outcomes, start-to-merge durations, community issue outcomes, Go/JavaScript code composition, and weekly first-parent code changes. Repository names, dates, counts, proportions, and weeks come from the input, never from this skill's original dataset.

Generate

Prefer already collected source evidence. Do not fetch repository data, trigger workflows, or invent missing statistics. Keep raw data and generated images out of the repository. Use a temporary or session artifact directory.

Create a Python 3.9+ virtual environment and install the runtime manifest:

python3 -m venv /tmp/statistics-infographic-venv
/tmp/statistics-infographic-venv/bin/python -m pip install \
  -r .github/skills/statistics-infographic/requirements.txt

Supply either one JSON object following examples/example.json, or a directory of existing summaries:

/tmp/statistics-infographic-venv/bin/python \
  .github/skills/statistics-infographic/scripts/render_infographic.py \
  --input .github/skills/statistics-infographic/examples/example.json \
  --output /tmp/statistics-infographic.png

For real data, replace --input with --data-dir /path/to/summaries. See references/data-contract.md for schemas and statistical definitions. The example is explicitly synthetic, not a gh-aw report.

Optional arguments: --title, --font-regular, and --font-bold. Supply both font paths together. Automatic discovery supports Arial on macOS/Windows and Liberation Sans or DejaVu Sans on Linux. Missing fonts fail explicitly; no bitmap fallback is used.

The output is a static, RGB, 2560 x 3200 PNG with 300-DPI metadata. A successful render atomically replaces the explicit output path. Invalid data, unreadable layout, or invalid fonts leave an existing output intact. Counts too large for their slots, more than 10 duration bands, or more than 260 weeks require a different layout; do not silently truncate, rescale text to illegibility, or aggregate data.

Verify accuracy

Input validation proves internal reconciliation, not correctness of the evidence. When cached raw API records are available, independently recompute the displayed statistics before delivery:

/tmp/statistics-infographic-venv/bin/python \
  .github/skills/statistics-infographic/scripts/audit_sources.py \
  --data-dir /path/to/summaries \
  --raw-dir /path/to/raw-records \
  --repo /path/to/local-checkout

The auditor also accepts --input instead of --data-dir. Omit --repo only if local Git evidence is unavailable, and disclose that code/history were not audited. For a repository migration, explicitly supply a repeatable --repository-alias previous-owner/previous-name only when issue numbering is known to be shared.

The Git audit reads immutable objects at the full input commit, not the worktree. It requires complete local history, disables lazy object fetching, and never requests credentials or performs network operations. Missing objects or shallow history fail explicitly. Large repositories may require substantial memory for the batched blob inventory.

Open the resulting image and inspect all six panels at normal viewing size: titles, axes, labels, footnotes, chart proportions, and snapshot metadata. Automated text-bound and overlap checks complement but do not replace visual inspection.

Deliver the image with its path and disclose missing independent checks or evidence limitations. In particular, inferred source issues do not prove who initiated an agent session or which device they used. The no-issue human assumption must be explicitly authorized for the dataset.

Maintain

Install the development manifest only for validation work:

/tmp/statistics-infographic-venv/bin/python -m pip install \
  -r .github/skills/statistics-infographic/requirements-dev.txt
HYPOTHESIS_STORAGE_DIRECTORY=/tmp/statistics-infographic-hypothesis \
  /tmp/statistics-infographic-venv/bin/python -m unittest discover \
  -s .github/skills/statistics-infographic/tests -v
/tmp/statistics-infographic-venv/bin/python -m mypy --strict \
  .github/skills/statistics-infographic/scripts

The implementation uses immutable typed records, explicit invariants, exact rational count ratios with half-up rounding, Decimal-oracle property tests, source-mutation regressions, deterministic rendering, safe CLI output tests, and pinned-object Git fixtures. These are rigorous software checks, not a mathematical proof of the whole program or of causal attribution.