github-statuses

A Flat Data attempt at historically documenting GitHub statuses.

About

This project builds the "missing GitHub status page": a historical mirror that shows actual uptime percentages and incidents across the entire platform, plus per-service uptime based on the incident data. It reconstructs timelines from the Atom feed history and turns them into structured outputs and a static site.

Prerequisites

uv

Install uv per the installation docs.

Python (for running the extractor)

The extractor depends on onnxruntime, which supports Python 3.11–3.13 (not 3.14+). Check your version: python --version or python3 --version.

If you need a compatible version, uv can install it:

uv python install 3.13

Then create the virtual environment with that Python:

uv venv --python 3.13
uv sync

Or use uv sync with an existing venv. If uv sync fails with an onnxruntime error, your Python is likely 3.14+; switch to 3.11–3.13 as above.

Dependencies

Run uv sync before using the extractor scripts. To view the static site only, no dependencies are needed—the parsed/ directory in the repo contains pre-generated data; just serve the repo root with any HTTP server.

Quick start (uv)

uv venv
uv sync

Run the extractor across all history:

uv run python scripts/extract_incidents.py --out out

Run the extractor for the last year (UTC example):

uv run python scripts/extract_incidents.py --out out_last_year --since 2025-01-03 --until 2026-01-03

Use JSONL (default) or split per-incident outputs:

uv run python scripts/extract_incidents.py --out out --incidents-format jsonl
uv run python scripts/extract_incidents.py --out out --incidents-format split

Enrich incidents with impact level by scraping the incident pages (cached):

uv run python scripts/extract_incidents.py --out out --enrich-impact

Infer missing components with GLiNER2 (used only when the incident page lacks "affected components"):

uv run python scripts/extract_incidents.py --out out --infer-components gliner2

Automation

After each Flat data update, a GitHub Action runs the parser and commits outputs to parsed/.

Run tests:

uv run python -m unittest discover -s tests

Status site

Static site lives in site/ and reads data from parsed/. No build step—serve the repo root with any static HTTP server:

python -m http.server 8000
# or: python3 -m http.server 8000

Then open http://localhost:8000/site/ in your browser.

GLiNER2 component inference

Some incident pages do not list "affected components". In those cases we use GLiNER2 as a fallback:

• Input text: incident title + non-Resolved updates.
• Labels: the 10 GitHub services with short descriptions.
• Thresholded inference (default: 0.75 confidence).
• Final filter: the label must also appear via explicit service aliases in the text.

This keeps HTML tags as the source of truth and uses ML only to fill gaps.

GLiNER2 experiment (evaluation + audit)

To validate the fallback approach, an experiment is run that produces:

• Audit: every GLiNER2-tagged incident with text evidence snippets.
• Evaluation: GLiNER2 predictions compared against incidents that do have HTML "affected components".

Reproduce the experiment at a fixed time point (numbers will change as new data arrives):

uv run python scripts/run_gliner_experiment.py --as-of 2026-01-08 --output-dir tagging-experiment

Outputs are written to:

• tagging-experiment/gliner2_audit.jsonl (tagged incidents + evidence snippets)
• tagging-experiment/gliner2_eval.json (metrics, per-label breakdown, sample mismatches)
• tagging-experiment/error_analysis.md (diff-style table of sample errors)

Latest results (as-of 2026-01-08, threshold 0.75, alias filter on, non-Resolved text only):

Metric	Value
Evaluated incidents	447
Predicted non-empty	419
Precision	0.950
Recall	0.883
Exact match rate	0.785
Audit count (missing-tag incidents)	51

Per-label precision/recall (top-level service components):

Label	Precision	Recall	TP	FP	FN
Git Operations	0.968	0.909	60	2	6
Webhooks	0.938	0.918	45	3	4
API Requests	0.915	0.915	54	5	5
Issues	1.000	0.286	22	0	55
Pull Requests	0.948	0.979	92	5	2
Actions	0.958	0.947	161	7	9
Packages	0.917	0.971	33	3	1
Pages	0.855	0.964	53	9	2
Codespaces	0.982	0.982	110	2	2
Copilot	1.000	0.967	58	0	2

Summary: the fallback is high-precision and mostly conservative. Most errors are false negatives (missing a true component), while false positives are typically "extra" components inferred from multi-service incident titles.

Sample errors (diffs highlighted with + for extra and - for missing):

Type	Incident	Predicted	Truth
false_positive	Incident with GitHub Actions and Codespaces	Actions, +Codespaces	Actions
false_positive	Incident with GitHub Packages and GitHub Pages	Packages, +Pages	Packages
false_positive	Incident with Pull Requests and Webhooks	Pull Requests, +Webhooks	Pull Requests
false_negative	Incident on 2022-09-06 22:05 UTC	none	-Git Operations, -Visit www, -Webhooks
false_negative	Incident on 2022-09-06 22:56 UTC	none	-Git Operations, -Visit www, -Webhooks
false_negative	Incident with API Requests	none	-API Requests

Outputs

Outputs are written to the directory passed to --out (local examples use out/). The GLiNER2 experiment writes to tagging-experiment/ by default. The automation workflow writes to parsed/.

• <out>/incidents.json: merged incident timeline records
• <out>/incidents.jsonl: one JSON object per incident (default)
• <out>/incidents/: per-incident JSON files when using --incidents-format split
• <out>/segments.csv: per-status timeline segments for Gantt/phase views
• <out>/downtime_windows.csv: downtime windows for incident bar charts

Incident records include optional impact and components fields when enrichment is enabled. Service components are sourced as follows:

• Primary: the incident page "affected components" section (if present).
• Fallback: GLiNER2 schema-driven extraction from the incident title + non-resolved updates, filtered by explicit service aliases to avoid generic matches.