Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-09 21:34 UTC

jrepp / docuchango

Docusaurus validation and repair framework with optimizations for agent based workflows

PythonMPL-2.0★ 5 stars⑂ 1 forksince Oct 2025View on GitHub ↗
KindCommand-line toolLibraryhow this is determined
#agentic-ai#docusaurus#linter#specification#validation

jrepp/docuchango holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (90/100) and lowest on Community & Adoption (37/100). It was last updated today. A single contributor accounts for most of its recent work.

80
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.

80
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 68 is calibrated to 80 on the published index scale (record calibration 2026-08-02).

Ownership

Jacob ReppPersonal account
46 followers116 public repossince Aug 2011IBM

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIdocuchango1.19.05,5956121 days agoclicmscollaborationdocumentationdocusaurusmarkdownvalidation

Metrics by category

Vitality

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

84Excellent · 21% of overall

Development activity

75Good
How it's scored
36/36Push recency — last push 0 days ago
11.1/36Commit cadence — 16/52 weeks with commits
18/18Commit volume — 435 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year435
human_commit_share1
days_since_last_push0
active_weeks_last_year16

Release discipline

98Exceptional
How it's scored
27/27Ships releases — 63 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~2.7 days
8/10OpenSSF Scorecard: Signed-Releases — 4 out of the last 4 releases have a total of 4 signed artifacts.
Inputs used
releases_count63
latest_release_tagv1.19.1-rc.2
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases2.7

Community & Adoption

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

37Weak · 17% of overall

Popularity & adoption

10Critical
How it's scored
9.8/60Stars — 5 stars
0/25Forks — 1 forks
0/15Watchers — 1 watchers
Inputs used
forks1
stars5
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

50Moderate
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MPL-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesbadge.fury.io, codecov.io, github.com, shields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

62Moderate
How it's scored
50/80Monthly downloads — 5,595 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesdocuchango
dependents—
ecosystemspypi
total_downloads—
monthly_downloads5,595
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?

61Moderate · 23% of overall

Maintainer resilience (bus factor)

18Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.7/22.5Commit distribution — top contributor authored 97% of commits
2.7/13.5Contributor breadth — 2 contributors
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.968

Issue & PR responsiveness

83Excellent
How it's scored
42/42Issue resolution — 100% of issues closed
30/30PR acceptance — 98/98 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/13 approved changesets -- score normalized to 0
Inputs used
merged_prs98
open_issues0
closed_issues8
prs_merged_7d1
prs_decided_7d1
prs_merged_30d31
prs_decided_30d31
issue_closed_ratio1
closed_unmerged_prs0
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

59Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
12/25Owner reach — 46 followers of jrepp
25/25Track record — 116 public repos, account ~15 yr old
Inputs used
followers46
owner_typeUser
is_verified—
owner_loginjrepp
public_repos116
account_age_days5,531
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 21 days ago
20/20Version history — 61 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesdocuchango
ecosystemspypi
any_deprecatedno
min_days_since_publish21

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall

Engineering practices

84Excellent
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 6 out of 6 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

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://pypi.org/project/docuchango/
10/10Repository description
10/10Topics — 5 topics
10/10Wiki
Inputs used
topicsagentic-ai, docusaurus, linter, specification, validation
has_wikiyes
homepagehttps://pypi.org/project/docuchango/
docs_sitehttps://pypi.org/project/docuchango/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

65Good · 16% of overall

Security posture

56Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
2.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 6 out of 6 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/13 approved changesets -- score normalized to 0
1.5/2.5Contributors — project has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
6/7.5Signed-Releases — 4 out of the last 4 releases have a total of 4 signed artifacts.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
6.8/7.5Vulnerabilities — 1 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate5.6

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages12
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:docuchango@1.19.0 runtime dependency closure — what installing the published package pulls in — 12 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.

68Good · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 95 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.95
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes1,061

Verify loop (build / test / typecheck)

48Weak
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency 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_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

52Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
52.3/55Manageable file sizes — 3/62 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes99,414
source_files_sampled62
oversized_source_files3

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples
Inputs used
example_dirsexamples
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

5GitHub stars
2contributors
435commits, last 12 months
0days since last push
63releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

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

More detail

OpenSSF Scorecard 5.6 / 10
5.6aggregate

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 · 2026-10-09 21:33 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests6 out of 6 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/13 approved changesets -- score normalized to 0
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
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
8Signed-Releases4 out of the last 4 releases have a total of 4 signed artifacts.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
9Vulnerabilities1 existing vulnerabilities detected
Direct dependencies 5
RegistryPackageVersion constraintManifest
PyPIpython-frontmatter>=1.3.0pyproject.toml
PyPIpydantic>=2.0.0pyproject.toml
PyPIpyyaml>=6.0pyproject.toml
PyPIclick>=8.0.0pyproject.toml
PyPIrich>=13.0.0pyproject.toml
All dependencies 40

Full resolved dependency set from the GitHub dependency graph: 5 direct and 35 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIclick8.4.1direct
PyPIpydantic2.13.4direct
PyPIpython-frontmatter1.3.0direct
PyPIpyyaml6.0.3direct
PyPIrich15.0.0direct
PyPIannotated-types0.7.0indirect
PyPIast-serialize0.5.0indirect
PyPIcolorama0.4.6indirect
PyPIcoverage7.14.0indirect
PyPIdefusedxml0.7.1indirect
PyPIdocuchango1.19.1rc1indirect
PyPIexceptiongroup1.3.1indirect
PyPIexecnet2.1.2indirect
PyPIiniconfig2.3.0indirect
PyPIjoblib1.5.3indirect
PyPIlibrt0.11.0indirect
PyPImarkdown-it-py4.2.0indirect
PyPImdurl0.1.2indirect
PyPImypy2.1.0indirect
PyPImypy-extensions1.1.0indirect
PyPInltk3.10.3indirect
PyPIpackaging26.2indirect
PyPIpathspec1.1.1indirect
PyPIpluggy1.6.0indirect
PyPIpydantic-core2.46.4indirect
PyPIpygments2.20.0indirect
PyPIpyphen0.17.2indirect
PyPIpytest9.0.3indirect
PyPIpytest-cov7.1.0indirect
PyPIpytest-timeout2.4.0indirect
PyPIpytest-xdist3.8.0indirect
PyPIregex2026.5.9indirect
PyPIruff0.15.14indirect
PyPIsetuptools83.0.0indirect
PyPItextstat0.7.13indirect
PyPItomli2.4.1indirect
PyPItqdm4.67.3indirect
PyPItypes-pyyaml6.0.12.20260518indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
Dependency advisories 0

Installing pypi:docuchango@1.19.0 pulls in 12 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

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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 statistics — PyPI.