Public record
Software health reportschema 0.17.0 · metrics 2.10.0 · 2026-07-21 04:42 UTC

tensorflow / metadata

Utilities for passing TensorFlow-related metadata between tools

Python · StarlarkApache-2.0★ 110 stars⑂ 62 forkssince Jun 2017View on GitHub ↗

tensorflow/metadata holds a health index of 75 out of 100, placing it in the Good band. It scores highest on Sustainability & Governance (84/100) and lowest on AI Readiness (32/100). It was last updated 41 days ago. 3 contributors account for most of its recent work.

75
overall / 100
Good

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.

75
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 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

tensorflowOrganization
21,573 followers107 public repossince Nov 2015

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPItensorflow-metadata1.21.0-4641 days agotensorflowmetadatatfx

Metrics by category

Vitality

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

55Moderate · 21% of overall
How it's scored
18/36Push recencylast push 41 days ago
4.8/36Commit cadence7/52 weeks with commits
11.3/18Commit volume17 commits in the last year
1/10OpenSSF Scorecard: Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
Inputs used
commits_last_year17
human_commit_share
days_since_last_push41
active_weeks_last_year7
How it's scored
27/27Ships releases45 releases published
36/36Release recencylatest release 41 days ago
12.6/27Release cadencea release every ~128.3 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count45
latest_release_tagv1.21.0
releases_from_tagsno
days_since_latest_release41
mean_days_between_releases128.3
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?

61Moderate · 17% of overall
How it's scored
33/60Stars110 stars
14.9/25Forks62 forks
6.2/15Watchers14 watchers
Inputs used
forks62
stars110
watchers14
growth_stateorganic
growth_factor_pct100
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

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

84Excellent · 23% of overall
How it's scored
36/54Bus factor3 contributor(s) cover half of all commits
17/22.5Commit distributiontop contributor authored 24% of commits
13.5/13.5Contributor breadth10 contributors
10/10OpenSSF Scorecard: Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
Inputs used
bus_factor3
contributors_sampled10
top_contributor_share0.245
How it's scored
18.4/42Issue resolution44% of issues closed
25.6/30PR acceptance41/48 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
12/15OpenSSF Scorecard: Code-ReviewFound 7/8 approved changesets -- score normalized to 8
Inputs used
merged_prs41
open_issues9
closed_issues7
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.438
closed_unmerged_prs7
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
25/25Owner reach21,573 followers of tensorflow
25/25Track record107 public repos, account ~10 yr old
Inputs used
followers21,573
owner_typeOrganization
is_verified
owner_logintensorflow
public_repos107
account_age_days3,911
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 41 days ago
20/20Version history46 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagestensorflow-metadata
ecosystemspypi
any_deprecatedno
min_days_since_publish41

Engineering Quality

Are baseline engineering and documentation practices in place?

73Good · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
16/16Linter configruff.toml
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
14/20OpenSSF Scorecard: CI-Tests6 out of 8 merged PRs checked by a CI test -- score normalized to 7
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics
has_wikiyes
homepage
docs_site
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

56Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionbranch protection not enabled on development/release branches
1.8/2.5CI-Tests6 out of 8 merged PRs checked by a CI test -- score normalized to 7
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6/7.5Code-ReviewFound 7/8 approved changesets -- score normalized to 8
2.5/2.5Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0.8/7.5Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not 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_aggregate4.5
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
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_packages3
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:tensorflow-metadata@1.21.0 runtime dependency closure — what installing the published package pulls in — 3 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.

32At Risk · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit historyno data
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configruff.toml
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/7 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes5,820
source_files_sampled7
oversized_source_files0
How it's scored
40/40API schema (OpenAPI/GraphQL/proto)tensorflow_metadata/proto/v0/anomalies.proto, tensorflow_metadata/proto/v0/derived_feature.proto, tensorflow_metadata/proto/v0/metric.proto, tensorflow_metadata/proto/v0/path.proto, tensorflow_metadata/proto/v0/problem_statement.proto, tensorflow_metadata/proto/v0/schema.proto, tensorflow_metadata/proto/v0/statistics.proto
0/20MCP servernot applicable to this kind of software
0/40Runnable examples
Inputs used
example_dirs
has_mcp_signalno
api_schema_filestensorflow_metadata/proto/v0/anomalies.proto, tensorflow_metadata/proto/v0/derived_feature.proto, tensorflow_metadata/proto/v0/metric.proto, tensorflow_metadata/proto/v0/path.proto, tensorflow_metadata/proto/v0/problem_statement.proto, tensorflow_metadata/proto/v0/schema.proto, tensorflow_metadata/proto/v0/statistics.proto
interfaces_expected_of
Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

110GitHub stars
10contributors
17commits, last 12 months
41days since last push
45releases
3bus factor
9open issues
PyPIpackage ecosystems

More detail

Star and fork history 110 ★ / 0 ⇿
110Stars

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.

02040608010012011062018-032022-042026-04

Each point covers 8 days.

OpenSSF Scorecard 4.5 / 10
4.5aggregate

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 · 2026-07-21 04:41 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
7CI-Tests6 out of 8 merged PRs checked by a CI test -- score normalized to 7
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
8Code-ReviewFound 7/8 approved changesets -- score normalized to 8
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
1Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 6

Full resolved dependency set from the GitHub dependency graph: 0 direct and 6 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIabsl-pyindirect
PyPIgoogleapis-common-protosindirect
PyPIprecommitindirect
PyPIprotobufindirect
PyPIpytestindirect
PyPIsetuptoolsindirect
Dependency advisories 0

Installing pypi:tensorflow-metadata@1.21.0 pulls in 3 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 v0.17.0 — full methodology · metrics wiki.

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