Public record
Software health reportschema 0.12.0 · metrics 2.10.0 · 2026-07-18 14:30 UTC

docling-project / docling-ibm-models

PythonMIT★ 207 stars⑂ 77 forkssince Jul 2024View on GitHub ↗

docling-project/docling-ibm-models holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (78/100) and lowest on Security (49/100). It was last updated 44 days ago. 2 contributors account for most of its recent work.

81
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.

81
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 69 is calibrated to 81 on the published index scale (record calibration 2026-08-02).

Ownership

Docling ProjectOrganization
1,193 followers26 public repossince Nov 2024

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIdocling-ibm-models3.13.3-5944 days agodoclingconvertdocumentpdflayout-modelsegmentationtable-structuretable-former

Metrics by category

Vitality

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

69Good · 21% of overall
How it's scored
18/36Push recencylast push 44 days ago
9.7/36Commit cadence14/52 weeks with commits
13.6/18Commit volume32 commits in the last year
7/10OpenSSF Scorecard: Maintained9 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 7
Inputs used
commits_last_year32
human_commit_share
days_since_last_push44
active_weeks_last_year14

Release discipline

100Exceptional
How it's scored
27/27Ships releases57 releases published
36/36Release recencylatest release 44 days ago
27/27Release cadencea release every ~25.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count57
latest_release_tagv3.13.3
releases_from_tagsno
days_since_latest_release44
mean_days_between_releases25.5
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?

73Good · 17% of overall
How it's scored
37.5/60Stars207 stars
15.7/25Forks77 forks
3.9/15Watchers6 watchers
Inputs used
forks77
stars207
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateyes

Sustainability & Governance

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

73Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
14.1/22.5Commit distributiontop contributor authored 37% of commits
13.5/13.5Contributor breadth17 contributors
10/10OpenSSF Scorecard: Contributorsproject has 8 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled17
top_contributor_share0.373
How it's scored
11/42Issue resolution26% of issues closed
25.9/30PR acceptance96/111 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
9/15OpenSSF Scorecard: Code-ReviewFound 18/29 approved changesets -- score normalized to 6
Inputs used
merged_prs96
open_issues34
closed_issues12
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.261
closed_unmerged_prs15
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
22.1/25Owner reach1,193 followers of docling-project
13.8/25Track record26 public repos, account ~1 yr old
Inputs used
followers1,193
owner_typeOrganization
is_verified
owner_logindocling-project
public_repos26
account_age_days611
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 44 days ago
20/20Version history59 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesdocling-ibm-models
ecosystemspypi
any_deprecatedno
min_days_since_publish44

Engineering Quality

Are baseline engineering and documentation practices in place?

78Good · 19% of overall
How it's scored
24/24CI workflows6 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests19 out of 19 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

55Moderate
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
0/10Repository description
0/10Topics
0/10Wiki
Inputs used
topics
has_wikino
homepage
docs_site
has_readmeyes
has_docs_diryes
has_descriptionno

Security

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

49Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
3/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
2.5/2.5CI-Tests19 out of 19 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
4.5/7.5Code-ReviewFound 18/29 approved changesets -- score normalized to 6
2.5/2.5Contributorsproject has 8 contributing companies or organizations
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
5.2/7.5Maintained9 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 7
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
5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities56 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4.9
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

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.

54Moderate · 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 config
11/11Static type checkingdocling_ibm_models/py.typed
10/10Reproducible environmentlockfile
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
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configsdocling_ibm_models/py.typed
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
27/45Type-checkable codePython with type-check config (docling_ibm_models/py.typed)
55/55Manageable file sizes0/53 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes56,449
source_files_sampled53
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 examplessamples
Inputs used
example_dirssamples
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

207GitHub stars
17contributors
32commits, last 12 months
44days since last push
57releases
2bus factor
34open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 4.9 / 10
4.9aggregate

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 · 2026-07-18 14:30 UTC

10Binary-Artifactsno binaries found in the repo
4Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests19 out of 19 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6Code-ReviewFound 18/29 approved changesets -- score normalized to 6
10Contributorsproject has 8 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
7Maintained9 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 7
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
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities56 existing vulnerabilities detected
Direct dependencies 14
RegistryPackageVersion constraintManifest
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All dependencies 166

Full resolved dependency set from the GitHub dependency graph: 14 direct and 152 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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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 v0.12.0 — full methodology · metrics wiki.

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