Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-11 09:00 UTC

PriorLabs / tabpfn-client

⚡ Easy API access to the tabular foundation model TabPFN ⚡

PythonApache-2.0★ 249 stars⑂ 25 forkssince Jul 2023View on GitHub ↗

PriorLabs/tabpfn-client holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on Sustainability & Governance (86/100) and lowest on Vitality (54/100). It was last updated today. 4 contributors account for most of its recent work.

86
overall / 100
Excellent

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.

86
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 72 is calibrated to 86 on the published index scale (record calibration 2026-08-02).

Ownership

Prior LabsOrganization
394 followers12 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
PyPItabpfn-client0.3.3-4933 days ago

Metrics by category

Vitality

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

54Moderate · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
26.3/36Commit cadence38/52 weeks with commits
18/18Commit volume136 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year136
human_commit_share0.69
days_since_last_push0
active_weeks_last_year38
How it's scored
0/27Ships releasesno releases published
0/36Release recencyno releases
0/27Release cadenceno releases
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count0
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?

55Moderate · 17% of overall
How it's scored
38.8/60Stars249 stars
11.5/25Forks25 forks
3.9/15Watchers6 watchers
Inputs used
forks25
stars249
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges7
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesbadge.fury.io, shields.io
has_pull_request_templateyes

Sustainability & Governance

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

86Excellent · 23% of overall
How it's scored
43.2/54Bus factor4 contributor(s) cover half of all commits
17.2/22.5Commit distributiontop contributor authored 24% of commits
13.5/13.5Contributor breadth20 contributors
10/10OpenSSF Scorecard: Contributorsproject has 5 contributing companies or organizations
Inputs used
bus_factor4
contributors_sampled20
top_contributor_share0.237
How it's scored
40/42Issue resolution95% of issues closed
20.6/30PR acceptance207/302 decided PRs merged
13/13Newcomer PR acceptance1/1 first-time contributors' PRs merged in 30d
13.5/15OpenSSF Scorecard: Code-ReviewFound 17/18 approved changesets -- score normalized to 9
Inputs used
merged_prs207
open_issues2
closed_issues40
prs_merged_7d1
prs_decided_7d1
prs_merged_30d4
prs_decided_30d5
issue_closed_ratio0.952
closed_unmerged_prs95
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
18.7/25Owner reach394 followers of PriorLabs
14/25Track record12 public repos, account ~2 yr old
Inputs used
followers394
owner_typeOrganization
is_verified
owner_loginPriorLabs
public_repos12
account_age_days1,068
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 33 days ago
20/20Version history49 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagestabpfn-client
ecosystemspypi
any_deprecatedno
min_days_since_publish33

Engineering Quality

Are baseline engineering and documentation practices in place?

80Excellent · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
16/16Linter configruff.toml
0/9.6Pre-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_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage sitehttps://www.priorlabs.ai
10/10Repository description
10/10Topics5 topics
10/10Wiki
Inputs used
topicsdata-science, foundation-models, machine-learning, tabpfn, tabular-data
has_wikiyes
homepagehttps://www.priorlabs.ai
docs_sitehttps://www.priorlabs.ai
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

82Excellent · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
4.5/7.5Branch-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
6.8/7.5Code-ReviewFound 17/18 approved changesets -- score normalized to 9
2.5/2.5Contributorsproject has 5 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
0/5Packagingno data
2.5/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
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_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate7.7
Excluded from scoring (no data or not applicable): Packaging, 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
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages46
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:tabpfn-client@0.3.3 runtime dependency closure — what installing the published package pulls in — 46 packages. 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.

70Good · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history66 of 69 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.957
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configruff.toml
11/11Static type checkingpyrightconfig.json
10/10Reproducible environmentlockfile
10/10Demonstrated agent practice15 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance31 of the last 100 commits are automated dependency updates
5/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configspyrightconfig.json
agent_commit_share0.15
toolchain_manifests
dependency_bot_commit_share0.31
How it's scored
27/45Type-checkable codePython with type-check config (pyrightconfig.json)
55/55Manageable file sizes0/45 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes48,383
source_files_sampled45
oversized_source_files0
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

249GitHub stars
20contributors
136commits, last 12 months
0days since last push
0releases
4bus factor
2open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 25 ⇿
0Stars
25Forks

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.

05101520252542024-092025-072026-05

Each point covers 2 days.

OpenSSF Scorecard 7.7 / 10
7.7aggregate

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 · 2026-08-11 09:00 UTC

10Binary-Artifactsno binaries found in the repo
6Branch-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
9Code-ReviewFound 17/18 approved changesets -- score normalized to 9
10Contributorsproject has 5 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
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
PyPIhttpx>=0.25.0,<=0.28.1pyproject.toml
PyPIomegaconf>=2.1.2,<=2.3.0pyproject.toml
PyPIpandas>=2.1.2,<=2.3.3pyproject.toml
PyPIpassword-strength>=0.0.3.post2,<=0.0.3.post2pyproject.toml
PyPIpydantic>=2.11.7,<=2.13.4pyproject.toml
PyPIscikit-learn>=1.3.1,<=1.9.0pyproject.toml
PyPIsseclient-py>=1.8.0,<=1.9.0pyproject.toml
PyPItqdm>=4.63.0,<=4.67.3pyproject.toml
PyPItyping_extensions>=4.12.2,<=4.16.0pyproject.toml
PyPIxxhash>=1.1.0,<=3.8.1pyproject.toml
PyPIbackoff>=2.2.0,<=2.2.1pyproject.toml
PyPIrich>=13.7.0,<=15.0.0pyproject.toml
PyPItabpfn-common-utils>=0.2.10,<=0.2.19pyproject.toml
PyPIgoogle-crc32c>=1.5.0,<=1.8.0pyproject.toml
PyPIpyarrow>=14.0.0,<=23.0.1pyproject.toml
PyPIpydantic-settings>=2.14.2,<=2.14.2pyproject.toml
All dependencies 80

Full resolved dependency set from the GitHub dependency graph: 17 direct and 63 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIbackoff2.2.1direct
PyPIgoogle-crc32c1.8.0direct
PyPIhttpx0.28.1direct
PyPIomegaconf2.3.0direct
PyPIpandas2.3.1direct
PyPIpassword-strength0.0.3.post2direct
PyPIpyarrow23.0.1direct
PyPIpydantic2.12.4direct
PyPIpydantic-settings2.14.2direct
PyPIrich14.0.0direct
PyPIscikit-learn1.7.2direct
PyPIscikit-learn1.9.0direct
PyPIsseclient-py1.8.0direct
PyPItabpfn-common-utils0.2.13direct
PyPItqdm4.67.1direct
PyPItyping-extensions4.15.0direct
PyPIxxhash3.5.0direct
PyPIannotated-types0.7.0indirect
PyPIantlr4-python3-runtime4.9.3indirect
PyPIanyio4.10.0indirect
PyPIappdirs1.4.4indirect
PyPIattrs23.2.0indirect
PyPIbasedpyright1.39.9indirect
PyPIboolean-py5.0indirect
PyPIboto31.43.21indirect
PyPIbotocore1.43.21indirect
PyPIcattrs24.1.3indirect
PyPIcertifi2025.8.3indirect
PyPIcharset-normalizer3.4.4indirect
PyPIcolorama0.4.6indirect
PyPIdistro1.9.0indirect
PyPIexceptiongroup1.3.0indirect
PyPIfhconfparser2024.1indirect
PyPIfilelock3.28.0indirect
PyPIh110.16.0indirect
PyPIhttpcore1.0.9indirect
PyPIidna3.15indirect
PyPIiniconfig2.1.0indirect
PyPIjmespath1.1.0indirect
PyPIjoblib1.5.2indirect
PyPIlicense-expression30.4.4indirect
PyPIlicensecheck2025.1.0indirect
PyPIloguru0.7.3indirect
PyPImarkdown3.10.2indirect
PyPImarkdown-it-py4.0.0indirect
PyPImdurl0.1.2indirect
PyPInarwhals2.22.1indirect
PyPInodejs-wheel-binaries24.16.0indirect
PyPInumpy2.2.6indirect
PyPInumpy2.3.3indirect
PyPIpackaging25.0indirect
PyPIplatformdirs4.4.0indirect
PyPIpluggy1.6.0indirect
PyPIposthog6.9.3indirect
PyPIpydantic-core2.41.5indirect
PyPIpygments2.20.0indirect
PyPIpytest9.0.3indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-dotenv1.2.2indirect
PyPIpytz2025.2indirect
PyPIpyyaml6.0.2indirect
PyPIrequests2.33.0indirect
PyPIrequests-cache1.3.0indirect
PyPIrequirements-parser0.13.0indirect
PyPIrespx0.23.1indirect
PyPIruff0.15.21indirect
PyPIs3transfer0.18.0indirect
PyPIscipy1.15.3indirect
PyPIscipy1.16.2indirect
PyPIsix1.17.0indirect
PyPIsniffio1.3.1indirect
PyPItabpfn-client0.4.1indirect
PyPIthreadpoolctl3.6.0indirect
PyPItomli2.2.1indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2025.2indirect
PyPIurl-normalize2.2.1indirect
PyPIurllib32.7.0indirect
PyPIuv0.11.15indirect
PyPIwin32-setctime1.2.0indirect
Dependency advisories 0

Installing pypi:tabpfn-client@0.3.3 pulls in 46 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

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 v0.31.0 — full methodology · metrics wiki.

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