sktime/pytorch-forecasting
What happens to a stranger's pull request here, in the Porchlamp snapshot of 17 October 2026.
Outside pull requests to sktime/pytorch-forecasting got a first human reply in a median of 29.7 hours (61 pull requests, 19 April to 16 October 2026); 37% got none within 14 days. It ranks 962 of 3,219 overall. For first-time contributors it ranks 827 of 1,525 (the bottom 46%).
| Rank | 962 of 3,219 |
|---|---|
| Score | 55.65 |
| Tier | B |
| Median first reply | 29.7 hours |
| 14-day no-reply share | 37% |
| First-timer rank | 827 of 1,525 |
| Ecosystem | Python |
| Window | 19 April to 16 October 2026 |
Composite
- Rank
- 962 of 3,219
- Score
- 55.65
- Tier
- B
- Coverage
- 100% · Full
- Quadrant
- Sweet Spot
Coordinates: leverage 54.0 (coverage 100%), friction 40.3 (coverage 100%)
Five pillars
| Pillar | Score | Coverage |
|---|---|---|
| Responsiveness | 52.3 | 100% |
| Criticality | 46.7 | 100% |
| Governance | 76.8 | 100% |
| Rigor | 51.2 | 100% |
| Accessibility | 52.4 | 100% |
The measured parameters
Responsiveness
| Parameter | Value | Score | Count | Window | Source |
|---|---|---|---|---|---|
| Decision Rate (90d)Share of outside pull requests that were merged or closed within 90 days. | 30.8% | 38.5 | 13 | 19 April to 18 July 2026 | primaryraw/pr_window/sktime__pytorch-forecasting.json |
| PR Ghost Rate (14d)Share of outside pull requests with no maintainer response, merge or close within 14 days. | 37.3% | 25.4 | 67 | 20 April to 17 October 2026 | primaryraw/pr_window/sktime__pytorch-forecasting.json |
| First-timer Merge RateShare of first-time outside authors' decided pull requests that were merged. | 57.1% | 71.4 | 28 | — | primaryraw/author_history/sktime__pytorch-forecasting.json |
| Time to First ResponseMedian hours from an outside pull request opening to its first substantive human reply. | 29.7 hours (1.2 days) | 67.3 | 61 | 19 April to 16 October 2026 | primaryraw/pr_window/sktime__pytorch-forecasting.json |
| Time to Merge (p90)Hours within which 90% of merged outside pull requests were merged. | 389.5 hours (16.2 days) | 44.5 | 33 | 19 April to 16 October 2026 | primaryraw/pr_window/sktime__pytorch-forecasting.json |
Criticality
| Parameter | Value | Score | Count | Window | Source |
|---|---|---|---|---|---|
| DependentsPackages in the registry that depend on this project, counted once each. | 9 | 49.5 | — | — | primaryraw/dependents/sktime__pytorch-forecasting.json |
| Monthly DownloadsDownloads from the package registry in a month. | 167,124 downloads a month | 49.1 | — | — | primaryraw/downloads/pypi__pytorch-forecasting.json |
| OpenSSF CriticalityOpenSSF criticality score from 0 to 1, computed from the project's public activity signals. | 0.4172 | 41.7 | — | — | primaryraw/github_meta/sktime__pytorch-forecasting.json |
Governance
| Parameter | Value | Score | Count | Window | Source |
|---|---|---|---|---|---|
| Elephant FactorThe fewest organisations that account for half of the last year's commits. | 6 | 100.0 | 97 | — | primaryraw/commits_365d/sktime__pytorch-forecasting.json |
| Legal IP RegimeThe contributor licence regime: DCO, none, a foundation CLA or a corporate CLA. | No CLA or sign-off found | 85.0 | — | — | primaryraw/governance/sktime__pytorch-forecasting.json |
| Promotion LadderHow openly a contributor can become a maintainer, from closed to verified. | No written path to becoming a maintainer | 40.0 | — | — | primaryraw/governance/sktime__pytorch-forecasting.json |
Rigor
| Parameter | Value | Score | Count | Window | Source |
|---|---|---|---|---|---|
| AI Policy PostureWhether the project has a written policy on AI-assisted contributions. | No written AI policy | 40.0 | — | — | primaryraw/governance/sktime__pytorch-forecasting.json |
| Pipeline DefensesHow well the project's build and review pipeline defends against bad changes, 0 to 100. | 68.3019 | 68.3 | 53 | — | proxy:review_ciraw/pr_window/sktime__pytorch-forecasting.json |
| Substantive Review RateShare of decided pull requests that received a substantive review. | 31.7% | 39.6 | 41 | 19 April to 16 October 2026 | primaryraw/pr_window/sktime__pytorch-forecasting.json |
Accessibility
| Parameter | Value | Score | Count | Window | Source |
|---|---|---|---|---|---|
| Developer ExperienceDeveloper experience, 0 to 100: half CI run time, half setup signals. | 0 | 0.0 | 30 | — | primaryraw/actions_runs/sktime__pytorch-forecasting.json |
| Newcomer ConversionShare of first-time authors whose first pull request merged within 180 days. | 27.8% | 55.6 | 18 | — | primaryraw/author_history/sktime__pytorch-forecasting.json |
| Newcomer Issue SupplyOpen, unassigned, up-to-date issues marked for newcomers. | 6 | 60.0 | — | — | primaryraw/gfi/sktime__pytorch-forecasting.json |
| Newcomer Return RateShare of those merged first-time authors who sent a second pull request within 90 days. | 40.0% | 80.0 | 5 | — | primaryraw/author_history/sktime__pytorch-forecasting.json |
Persona view
| Persona | Rank | Score | Tier | Status |
|---|---|---|---|---|
| Balanced | 962 of 3,219 | 55.65 | B | Rated |
| First-timer | 827 of 1,525 | 55.35 | B | Rated |
| Career capital | This repository's production-criticality signal is not yet high enough for career-capital ranking. | |||
| Senior systems | 549 of 1,276 | 56.24 | B | Rated |
| AI-assisted | 1,251 of 3,696 | 54.48 | C | Rated |
Integrity
- Snapshot
- 2026-10-17
- Raw hash
- de8f6428214ad224e0e7f23bc11638d0c1c5261acc9d1d93efe295501e1e5a9b
No suspicion flags on this repository.
The full record is in data/2026-10-17/repos/d1.json, under the key sktime/pytorch-forecasting.
How every number is measured: Porchlamp methodology.
Open an issue about this record and say what looks wrong.
Porchlamp (2026). sktime/pytorch-forecasting. Snapshot 2026-10-17. https://porchlamp.org/projects/sktime/pytorch-forecasting/. CC BY 4.0.
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