Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-21 18:33 UTC

aperture-data / aperturedb-python

ApertureDB Python Client

PythonCustom license★ 13 stars⑂ 4 forkssince Dec 2020View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

aperture-data/aperturedb-python holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Vitality (86/100) and lowest on Community & Adoption (39/100). It was last updated today. A single contributor accounts 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

ApertureDataOrganization
27 followers12 public repossince Sep 2019

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIaperturedb0.5.34,3271040 days agoaperturedbgraphdatabaseimagevideometadatasearchindexing

Metrics by category

Vitality

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

86Excellent · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
13.2/36Commit cadence — 19/52 weeks with commits
18/18Commit volume — 101 commits in the last year
9/10OpenSSF Scorecard: Maintained — 11 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 9
Inputs used
commits_last_year101
human_commit_share0.93
days_since_last_push0
active_weeks_last_year19

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 83 releases published
36/36Release recency — latest release 6 days ago
27/27Release cadence — a release every ~41.2 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count83
latest_release_tagv0.5.2
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases41.2
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?

39Weak · 17% of overall
How it's scored
17.5/60Stars — 13 stars
4/25Forks — 4 forks
0/15Watchers — 2 watchers
Inputs used
forks4
stars13
watchers2
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges4
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesbadge.fury.io, github.com, shields.io
has_pull_request_templateno
How it's scored
48.5/80Monthly downloads — 4,327 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesaperturedb
dependents—
ecosystemspypi
total_downloads—
monthly_downloads4,327
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?

68Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
11.2/22.5Commit distribution — top contributor authored 50% of commits
13.5/13.5Contributor breadth — 10 contributors
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled10
top_contributor_share0.501
How it's scored
38.3/42Issue resolution — 91% of issues closed
25.8/30PR acceptance — 534/622 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
7.5/15OpenSSF Scorecard: Code-Review — Found 14/28 approved changesets -- score normalized to 5
Inputs used
merged_prs534
open_issues10
closed_issues104
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.912
closed_unmerged_prs88
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.
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
10.4/25Owner reach — 27 followers of aperture-data
20.1/25Track record — 12 public repos, account ~7 yr old
Inputs used
followers27
owner_typeOrganization
is_verifiedno
owner_loginaperture-data
public_repos12
account_age_days2,583

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 104 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesaperturedb
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

86Excellent · 19% of overall
How it's scored
24/24CI workflows — 7 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 17 out of 17 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
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

60Moderate · 16% of overall
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 — 17 out of 17 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
3.8/7.5Code-Review — Found 14/28 approved changesets -- score normalized to 5
1.5/2.5Contributors — project has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5License — license file detected
6.8/7.5Maintained — 11 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 9
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
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages3
unassessed_packages4
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 3 resolved dependencies against OSV. 4 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

72Good · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.aperturedata.io/llms-full.txt)
40/40Legible commit history — 74 of 93 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.aperturedata.io/llms-full.txt
legible_history_share0.796
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes3,495
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config
0/11Static type checking
10/10Reproducible environment — devcontainer, Dockerfile
4/10Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontaineryes
has_linter_configyes
typecheck_configs—
agent_commit_share0.02
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/134 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes53,142
source_files_sampled134
oversized_source_files0
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, notebooks
Inputs used
example_dirsexamples, notebooks
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

13GitHub stars
10contributors
101commits, last 12 months
0days since last push
83releases
1bus factor
10open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:aperturedb@0.5.3; advisories assessed against the repository dependency graph instead

More detail

Star and fork history 0 ★ / 4 ⇿
0Stars
4Forks
59Releases

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.

1223344412022-052023-102025-03
Major 0Minor 3Patch 56

Each point covers 3 days.

OpenSSF Scorecard 5.0 / 10
5.0aggregate

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 · 2026-10-21 18:32 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests17 out of 17 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
5Code-ReviewFound 14/28 approved changesets -- score normalized to 5
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
9Maintained11 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 9
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
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 21
RegistryPackageVersion constraintManifest
PyPIprotobuf>=3.20.3,<7.0.0pyproject.toml
PyPIrequests—pyproject.toml
PyPIboto3—pyproject.toml
PyPInumpy<2pyproject.toml
PyPInumpy—pyproject.toml
PyPIdistributed—pyproject.toml
PyPImatplotlib—pyproject.toml
PyPIpandas—pyproject.toml
PyPIkaggle!=1.6.15pyproject.toml
PyPIgoogle-cloud-storage—pyproject.toml
PyPIipython—pyproject.toml
PyPIdask—pyproject.toml
PyPIipywidgets—pyproject.toml
PyPIpydantic>=2.6.0pyproject.toml
PyPIdevtools—pyproject.toml
PyPItyper—pyproject.toml
PyPIopencv-python-headless—pyproject.toml
PyPIipywidgets==8.0.4pyproject.toml
PyPIkeepalive-socket==0.0.1pyproject.toml
PyPIgraphviz==0.20.2pyproject.toml
PyPIpython-dotenv—pyproject.toml
All dependencies 7

Full resolved dependency set from the GitHub dependency graph: 6 direct and 1 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIgraphviz0.20.2direct
PyPIipywidgets8.0.4direct
PyPIkaggle—direct
PyPIkeepalive-socket0.0.1direct
PyPIprotobuf—direct
PyPIpydantic—direct
PyPIsetuptools—indirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 3 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 4 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

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 v0.34.0 — full methodology · metrics wiki.

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