Public record
Software health reportschema 0.27.0 · metrics 2.5.0 · 2026-07-28 14:39 UTC

thombashi / pytest-md-report

A pytest plugin to generate test outcomes reports with markdown table format.

PythonMIT★ 62 stars⑂ 10 forkssince May 2020View on GitHub ↗

thombashi/pytest-md-report holds a health index of 62 out of 100, placing it in the Moderate band. It scores highest on Vitality (72/100) and lowest on Community & Adoption (43/100). It was last updated 7 days ago. A single contributor accounts for most of its recent work.

62
overall / 100
Moderate

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.

62
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 58 is calibrated to 62 on the published index scale (record calibration 2026-08-02).

Ownership

Tsuyoshi HombashiPersonal account
131 followers99 public repossince Oct 2015

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

Metrics by category

Vitality

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

72Good · 21% of overall
How it's scored
36/36Push recency — last push 7 days ago
2.1/36Commit cadence — 3/52 weeks with commits
11.1/18Commit volume — 16 commits in the last year
10/10OpenSSF Scorecard: Maintained — 14 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year16
human_commit_share0.97
days_since_last_push7
active_weeks_last_year3
How it's scored
27/27Ships releases — 15 releases published
36/36Release recency — latest release 85 days ago
19.8/27Release cadence — a release every ~113.6 days
8/10OpenSSF Scorecard: Signed-Releases — 5 out of the last 5 releases have a total of 5 signed artifacts.
Inputs used
releases_count15
latest_release_tagv0.8.0
releases_from_tagsno
days_since_latest_release85
mean_days_between_releases113.6

Community & Adoption

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

43Weak · 17% of overall
How it's scored
29/60Stars — 62 stars
8/25Forks — 10 forks
0/15Watchers — 1 watchers
Inputs used
forks10
stars62
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingno
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?

44Weak · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.2/22.5Commit distribution — top contributor authored 99% of commits
5.4/13.5Contributor breadth — 4 contributors
0/10OpenSSF Scorecard: Contributors — project has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled4
top_contributor_share0.991
How it's scored
33.6/42Issue resolution — 80% of issues closed
19.4/30PR acceptance — 11/17 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 2/22 approved changesets -- score normalized to 0
Inputs used
merged_prs11
open_issues3
closed_issues12
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.8
closed_unmerged_prs6
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
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
15.2/25Owner reach — 131 followers of thombashi
25/25Track record — 99 public repos, account ~10 yr old
Inputs used
followers131
owner_typeUser
is_verified
owner_loginthombashi
public_repos99
account_age_days3,923
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

66Good · 19% of overall
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
12/20OpenSSF Scorecard: CI-Tests — 4 out of 6 merged PRs checked by a CI test -- score normalized to 6
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 4 topics
0/10Wiki
Inputs used
topicspytest-plugin, markdown, pytest, gfm
has_wikino
homepage
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

67Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
1.5/2.5CI-Tests — 4 out of 6 merged PRs checked by a CI test -- score normalized to 6
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 2/22 approved changesets -- score normalized to 0
0/2.5Contributors — project has 0 contributing companies or organizations -- score normalized to 0
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 — 14 commit(s) and 2 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
3/5SAST — SAST tool is not run on all commits -- score normalized to 6
0/5Security-Policy — security policy file not detected
6/7.5Signed-Releases — 5 out of the last 5 releases have a total of 5 signed artifacts.
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate6.7
Excluded from scoring (no data or not applicable): branch_protection. 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.

50Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
14.3/40Legible commit history — 26 of 97 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share0.268
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — tox.ini
11/11Static type checking — pytest_md_report/py.typed
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 3 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configspytest_md_report/py.typed
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0.03
How it's scored
27/45Type-checkable code — Python with type-check config (pytest_md_report/py.typed)
55/55Manageable file sizes — 0/15 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes26,238
source_files_sampled15
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files

Key facts

62GitHub stars
4contributors
16commits, last 12 months
7days since last push
15releases
1bus factor
3open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • No resolved dependencies carried a version and a supported ecosystem

More detail

Star and fork history 0 ★ / 10 ⇿
0Stars
10Forks
14Releases

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.

2468101022021-052023-122026-07
Major 0Minor 7Patch 7

Each point covers 5 days.

OpenSSF Scorecard 6.7 / 10
6.7aggregate

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 · 2026-07-28 14:38 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
6CI-Tests4 out of 6 merged PRs checked by a CI test -- score normalized to 6
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 2/22 approved changesets -- score normalized to 0
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained14 commit(s) and 2 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
6SASTSAST tool is not run on all commits -- score normalized to 6
0Security-Policysecurity policy file not detected
8Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
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
PyPIpytablewriterindirect
PyPIpytestindirect
PyPIsetuptoolsindirect
PyPIsetuptools-scmindirect
PyPItcolorpyindirect
PyPItypepyindirect
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies carried a version and a supported ecosystem

Raw JSON report machine-readable

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 v2.5.0, schema v0.27.0 — full methodology · metrics wiki.

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