Public record
Software health reportschema 0.30.0 · metrics 2.10.0 · 2026-08-04 18:13 UTC

executablebooks / markdown-it-py

Markdown parser, done right. 100% CommonMark support, extensions, syntax plugins & high speed. Now in Python!

Python · HTMLMIT★ 1,348 stars⑂ 114 forkssince Mar 2020View on GitHub ↗
KindLibraryCommand-line toolhow this is determined

executablebooks/markdown-it-py holds a health index of 90 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (89/100) and lowest on Vitality (67/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

90
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.

90
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 76 is calibrated to 90 on the published index scale (record calibration 2026-08-02).

Ownership

Executable BooksOrganization
523 followers72 public repossince Nov 2019

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPImarkdown-it-py4.2.0-4589 days agomarkdownlexerparsercommonmarkmarkdown-it

Metrics by category

Vitality

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

67Good · 21% of overall
How it's scored
36/36Push recencylast push 1 days ago
4.2/36Commit cadence6/52 weeks with commits
12.2/18Commit volume22 commits in the last year
3/10OpenSSF Scorecard: Maintained4 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 3
Inputs used
commits_last_year22
human_commit_share0.87
days_since_last_push1
active_weeks_last_year6
How it's scored
27/27Ships releases44 releases published
36/36Release recencylatest release 89 days ago
12.6/27Release cadencea release every ~203.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count44
latest_release_tagv4.2.0
releases_from_tagsno
days_since_latest_release89
mean_days_between_releases203.5
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?

80Excellent · 17% of overall
How it's scored
50.8/60Stars1,348 stars
17.1/25Forks114 forks
7.2/15Watchers21 watchers
Inputs used
forks114
stars1,348
watchers21
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
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
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateno

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
9/54Bus factor1 contributor(s) cover half of all commits
7/22.5Commit distributiontop contributor authored 69% of commits
13.5/13.5Contributor breadth31 contributors
10/10OpenSSF Scorecard: Contributorsproject has 9 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled31
top_contributor_share0.691
How it's scored
26.8/42Issue resolution64% of issues closed
25.4/30PR acceptance235/278 decided PRs merged
13/13Newcomer PR acceptance1/1 first-time contributors' PRs merged in 30d
4.5/15OpenSSF Scorecard: Code-ReviewFound 8/26 approved changesets -- score normalized to 3
Inputs used
merged_prs235
open_issues35
closed_issues62
prs_merged_7d0
prs_decided_7d0
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio0.639
closed_unmerged_prs43
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
19.6/25Owner reach523 followers of executablebooks
25/25Track record72 public repos, account ~6 yr old
Inputs used
followers523
owner_typeOrganization
is_verified
owner_loginexecutablebooks
public_repos72
account_age_days2,457
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 89 days ago
20/20Version history45 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesmarkdown-it-py
ecosystemspypi
any_deprecatedno
min_days_since_publish89

Engineering Quality

Are baseline engineering and documentation practices in place?

89Excellent · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter configtox.ini
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
14/20OpenSSF Scorecard: CI-Tests22 out of 30 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

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://markdown-it-py.readthedocs.io
10/10Repository description
10/10Topics3 topics
0/10Wiki
Inputs used
topicsmarkdown, markdown-parser, markdown-it
has_wikino
homepagehttps://markdown-it-py.readthedocs.io
docs_sitehttps://markdown-it-py.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

72Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
1.8/2.5CI-Tests22 out of 30 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
2.2/7.5Code-ReviewFound 8/26 approved changesets -- score normalized to 3
2.5/2.5Contributorsproject has 9 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
5/5Fuzzingproject is fuzzed
2.5/2.5Licenselicense file detected
2.2/7.5Maintained4 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 3
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
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.5
Excluded from scoring (no data or not applicable): Branch-Protection, 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_packages1
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:markdown-it-py@4.2.0 runtime dependency closure — what installing the published package pulls in — 1 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.

82Excellent · 4% of overall
How it's scored
45/45Agent instructionsAGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history83 of 87 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.954
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes14,450
How it's scored
18/18One-command bootstrapdocs/Makefile
22/22Automated tests
11/11Lint / format configtox.ini
11/11Static type checkingmarkdown_it/py.typed
0/10Reproducible environment
4/10Demonstrated agent practice2 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance5 of the last 100 commits are automated dependency updates
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_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsmarkdown_it/py.typed
agent_commit_share0.02
toolchain_manifests
dependency_bot_commit_share0.05
How it's scored
27/45Type-checkable codePython with type-check config (markdown_it/py.typed)
55/55Manageable file sizes0/89 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes12,759
source_files_sampled89
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

1,348GitHub stars
31contributors
22commits, last 12 months
1days since last push
44releases
1bus factor
35open 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 ★ / 114 ⇿
0Stars
114Forks
30Releases

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.

02040608010012011242020-042023-052026-07
Major 4Minor 7Patch 16

Each point covers 6 days.

OpenSSF Scorecard 6.5 / 10
6.5aggregate

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 · 2026-08-04 18:12 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
7CI-Tests22 out of 30 merged PRs checked by a CI test -- score normalized to 7
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
3Code-ReviewFound 8/26 approved changesets -- score normalized to 3
10Contributorsproject has 9 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
10Fuzzingproject is fuzzed
10Licenselicense file detected
3Maintained4 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 3
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
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPImdurl~=0.1pyproject.toml
All dependencies 10

Full resolved dependency set from the GitHub dependency graph: 1 direct and 9 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPImdurldirect
PyPIcommonmarkindirect
PyPIflit-coreindirect
PyPIlinkify-it-pyindirect
PyPImarkdownindirect
PyPImdit-py-pluginsindirect
PyPImistletoeindirect
PyPImistuneindirect
PyPIpanfluteindirect
PyPIsphinx-book-themeindirect
Dependency advisories 0

Installing pypi:markdown-it-py@4.2.0 pulls in 1 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 v0.30.0 — full methodology · metrics wiki.

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