Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-28 02:53 UTC

PriorLabs / TabPFN

⚡ TabPFN: Foundation Model for Tabular Data ⚡

PythonCustom license★ 7,857 stars⑂ 782 forkssince Jul 2022View on GitHub ↗

PriorLabs/TabPFN holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Vitality (99/100) and lowest on AI Readiness (63/100). It was last updated today. 3 contributors account for most of its recent work.

98
overall / 100
Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

98
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 86 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

Prior LabsOrganization
405 followers13 public repossince Sep 2023

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPItabpfn8.5.0247,653600 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

99Exceptional · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
35.3/36Commit cadence51/52 weeks with commits
18/18Commit volume521 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year521
human_commit_share0.75
days_since_last_push0
active_weeks_last_year51

Release discipline

100Exceptional
How it's scored
27/27Ships releases27 releases published
36/36Release recencylatest release 0 days ago
27/27Release cadencea release every ~9.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count27
latest_release_tagv8.5.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases9.5
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

78Good · 17% of overall
How it's scored
60/60Stars7,857 stars
24.1/25Forks782 forks
9.1/15Watchers45 watchers
Inputs used
forks782
stars7,857
watchers45
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5Licenselicense file present, not a recognized license
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges4
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesbadge.fury.io, shields.io
has_pull_request_templateyes
How it's scored
71.9/80Monthly downloads247,653 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagestabpfn
dependents
ecosystemspypi
total_downloads
monthly_downloads247,653
unverified_packages_excluded
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

78Good · 23% of overall
How it's scored
36/54Bus factor3 contributor(s) cover half of all commits
15.1/22.5Commit distributiontop contributor authored 33% of commits
13.5/13.5Contributor breadth63 contributors
10/10OpenSSF Scorecard: Contributorsproject has 6 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled63
top_contributor_share0.329
How it's scored
40.9/42Issue resolution98% of issues closed
23.5/30PR acceptance634/809 decided PRs merged
0/13Newcomer PR acceptance0/2 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Reviewall changesets reviewed
Inputs used
merged_prs634
open_issues9
closed_issues357
prs_merged_7d7
prs_decided_7d11
prs_merged_30d35
prs_decided_30d46
issue_closed_ratio0.975
closed_unmerged_prs175
first_time_authors_30d2
first_time_prs_merged_30d0
first_time_prs_decided_30d2
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
18.8/25Owner reach405 followers of PriorLabs
14.3/25Track record13 public repos, account ~2 yr old
Inputs used
followers405
owner_typeOrganization
is_verifiedno
owner_loginPriorLabs
public_repos13
account_age_days1,085

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 0 days ago
20/20Version history60 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagestabpfn
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall
How it's scored
24/24CI workflows12 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttp://priorlabs.ai
10/10Repository description
10/10Topics5 topics
10/10Wiki
Inputs used
topicsdata-science, foundation-models, machine-learning, tabpfn, tabular-data
has_wikiyes
homepagehttp://priorlabs.ai
docs_sitehttp://priorlabs.ai
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

81Excellent · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0.8/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
2.5/2.5CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Reviewall changesets reviewed
2.5/2.5Contributorsproject has 6 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.2/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
5/5Packagingpackaging workflow detected
4/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 8
5/5SASTSAST tool is run on all commits
5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate7.6
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
0/25Indirect dependencies free of known advisoriestransitive set not separable from development and test dependencies in this scope
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages2
unassessed_packages35
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 2 resolved dependencies against OSV. 35 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

63Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt)llms.txt served by the project website (https://priorlabs.ai/llms.txt)
40/40Legible commit history65 of 75 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://priorlabs.ai/llms.txt
legible_history_share0.867
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.ruff])
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice48 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance19 of the last 100 commits are automated dependency updates
8/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 8
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.48
toolchain_manifests
dependency_bot_commit_share0.19
How it's scored
0/45Type-checkable codePython without a type-check config
53.5/55Manageable file sizes5/185 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes102,812
source_files_sampled185
oversized_source_files5
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

7,857GitHub stars
63contributors
521commits, last 12 months
0days since last push
27releases
3bus factor
9open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • First-time contributor figures cover 12 of 16 authors (cap 12)
  • deps.dev does not index pypi:tabpfn@8.5.0; advisories assessed against the repository dependency graph instead

More detail

Star and fork history 0 ★ / 782 ⇿
0Stars
782Forks
26Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0200400600800771412022-102024-092026-08
Major 3Minor 7Patch 15

Each point covers 4 days.

OpenSSF Scorecard 7.6 / 10
7.6aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-28 02:52 UTC

10Binary-Artifactsno binaries found in the repo
1Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 6 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
8Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 8
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 16
RegistryPackageVersion constraintManifest
PyPItorch>=2.5pyproject.toml
PyPIsafetensors>=0.4.0pyproject.toml
PyPInumpy>=1.21.6pyproject.toml
PyPIscikit-learn>=1.2.0pyproject.toml
PyPItyping_extensions>=4.12.0pyproject.toml
PyPIscipy>=1.11.1pyproject.toml
PyPIpandas>=1.4.0pyproject.toml
PyPIeinops>=0.4.0pyproject.toml
PyPIhuggingface-hub>=0.23.0pyproject.toml
PyPIpydantic>=2.8.0pyproject.toml
PyPIpydantic-settings>=2.10.1pyproject.toml
PyPIjoblib>=1.2.0pyproject.toml
PyPItqdm>=4.66.0pyproject.toml
PyPIfilelock>=3.11.0pyproject.toml
PyPIlightgbm>=4.4pyproject.toml
PyPImlx>=0.29.3pyproject.toml
All dependencies 37

Full resolved dependency set from the GitHub dependency graph: 16 direct and 21 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIeinopsdirect
PyPIfilelockdirect
PyPIhuggingface-hubdirect
PyPIjoblibdirect
PyPIlightgbmdirect
PyPImlxdirect
PyPInumpydirect
PyPIpandasdirect
PyPIpydanticdirect
PyPIpydantic-settingsdirect
PyPIsafetensorsdirect
PyPIscikit-learndirect
PyPIscipydirect
PyPItorchdirect
PyPItqdmdirect
PyPItyping-extensionsdirect
PyPIblackindirect
PyPIlicensecheckindirect
PyPImarkdown-execindirect
PyPImatplotlibindirect
PyPImikeindirect
PyPImkdocsindirect
PyPImkdocs-autorefsindirect
PyPImkdocs-gen-filesindirect
PyPImkdocs-glightboxindirect
PyPImkdocs-literate-navindirect
PyPImkdocs-materialindirect
PyPImkdocstringsindirect
PyPImypy2.3.0indirect
PyPIonnxindirect
PyPIpre-commitindirect
PyPIpytestindirect
PyPIpytest-mockindirect
PyPIpytest-xdistindirect
PyPIruff0.15.12indirect
PyPItowncrierindirect
PyPIwandbindirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 2 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 35 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.