Public record
Software health reportschema 0.26.0 · metrics 2.10.0 · 2026-07-21 23:42 UTC

mkleehammer / pyodbc

Python ODBC bridge

C++ · PythonMIT-0★ 3,078 stars⑂ 572 forkssince Oct 2008View on GitHub ↗

mkleehammer/pyodbc holds a health index of 84 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (90/100) and lowest on Security (59/100). It was last updated 45 days ago. 2 contributors account for most of its recent work.

84
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.

84
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 71 is calibrated to 84 on the published index scale (record calibration 2026-08-02).

Ownership

Michael KleehammerPersonal account
167 followers16 public repossince Oct 2008

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 publish
PyPIpyodbc5.3.0-63277 days ago

Metrics by category

Vitality

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

60Moderate · 21% of overall
How it's scored
18/36Push recencylast push 45 days ago
8.3/36Commit cadence12/52 weeks with commits
16.5/18Commit volume67 commits in the last year
10/10OpenSSF Scorecard: Maintained24 commit(s) and 3 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year67
human_commit_share1
days_since_last_push45
active_weeks_last_year12
How it's scored
27/27Ships releases35 releases published
16.2/36Release recencylatest release 277 days ago
19.8/27Release cadencea release every ~102.2 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count35
latest_release_tag5.3.0
releases_from_tagsno
days_since_latest_release277
mean_days_between_releases102.2
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?

76Good · 17% of overall
How it's scored
56.6/60Stars3,078 stars
23/25Forks572 forks
11.5/15Watchers117 watchers
Inputs used
forks572
stars3,078
watchers117
growth_stateorganic
growth_factor_pct100
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT-0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
7.2/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingno
has_issue_templateyes
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?

72Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
12.6/22.5Commit distributiontop contributor authored 44% of commits
13.5/13.5Contributor breadth47 contributors
10/10OpenSSF Scorecard: Contributorsproject has 40 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled47
top_contributor_share0.439
How it's scored
40.4/42Issue resolution96% of issues closed
22.3/30PR acceptance190/256 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
9/15OpenSSF Scorecard: Code-ReviewFound 19/30 approved changesets -- score normalized to 6
Inputs used
merged_prs190
open_issues41
closed_issues1,076
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.963
closed_unmerged_prs66
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 backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
16/25Owner reach167 followers of mkleehammer
21/25Track record16 public repos, account ~17 yr old
Inputs used
followers167
owner_typeUser
is_verified
owner_loginmkleehammer
public_repos16
account_age_days6,500
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable1 package(s) on pypi
26/35Publish recencylatest publish 277 days ago
20/20Version history63 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagespyodbc
ecosystemspypi
any_deprecatedno
min_days_since_publish277

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
16/16Linter config.flake8, tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests22 out of 22 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 sitehttps://github.com/mkleehammer/pyodbc/wiki
10/10Repository description
10/10Topics4 topics
10/10Wiki
Inputs used
topicspython, database, odbc, dbapi
has_wikiyes
homepagehttps://github.com/mkleehammer/pyodbc/wiki
docs_sitehttps://github.com/mkleehammer/pyodbc/wiki
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

59Moderate · 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
2.5/2.5CI-Tests22 out of 22 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 19/30 approved changesets -- score normalized to 6
2.5/2.5Contributorsproject has 40 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
5/5Fuzzingproject is fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained24 commit(s) and 3 issue activity found in the last 90 days -- score normalized to 10
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
5/5SASTSAST tool is run on all commits
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_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.9
Excluded from scoring (no data or not applicable): Packaging, 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.

65Good · 4% of overall
How it's scored
45/45Agent instructionsCLAUDE.md
0/15Machine-readable docs (llms.txt)
25.6/40Legible commit history48 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.48
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes6,882
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config.flake8, tox.ini
11/11Static type checkingC++ (statically typed)
0/10Reproducible environment
4/10Demonstrated agent practice2 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageyes
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.02
toolchain_manifests
dependency_bot_commit_share0
How it's scored
45/45Type-checkable codeC++ (statically typed)
50.7/55Manageable file sizes3/38 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes95,691
source_files_sampled38
oversized_source_files3

Key facts

3,078GitHub stars
47contributors
67commits, last 12 months
45days since last push
35releases
2bus factor
41open issues
PyPIpackage ecosystems

Data collection warnings

  • No resolved dependencies carried a version and a supported ecosystem

More detail

Star and fork history 3,078 ★ / 572 ⇿
3,078Stars
572Forks
35Releases

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

01,2502,5003,7503,078550232008-122017-092026-06
Major 2Minor 3Patch 27

Each point covers 17 days.

OpenSSF Scorecard 5.9 / 10
5.9aggregate

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 · 2026-07-21 23:42 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
10CI-Tests22 out of 22 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 19/30 approved changesets -- score normalized to 6
10Contributorsproject has 40 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
10Fuzzingproject is fuzzed
10Licenselicense file detected
10Maintained24 commit(s) and 3 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
10SASTSAST tool is run on all commits
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 3

Full resolved dependency set from the GitHub dependency graph: 0 direct and 3 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIflake8indirect
PyPIpylintindirect
PyPIpytestindirect
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

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 v0.26.0 — full methodology · metrics wiki.

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