Public record
Software health reportschema 0.14.0 · metrics 2.10.0 · 2026-07-18 20:54 UTC

mabel-dev / opteryx

🦖 A SQL-on-everything Query Engine you can execute over multiple databases and file formats. Query your data, where it lives.

PythonApache-2.0★ 113 stars⑂ 14 forkssince Dec 2021View on GitHub ↗
KindLibraryhow this is determined
#python#sql#analytics#big-data#serverless#sql-engine#arrow#cloud-native#query-engine#database#serverless-database#query-processor#command-line#cross-platform#gcp#aws#data-analytics#query-federation#zero-etl#iceberg

mabel-dev/opteryx holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (92/100) and lowest on AI Readiness (57/100). It was last updated 3 days ago. A single contributor accounts for most of its recent work.

86
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.

86
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 72 is calibrated to 86 on the published index scale (record calibration 2026-08-02).

Ownership

OpteryxOrganization
4 followers25 public repossince May 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
crates.iocomputepoints to another repo — not scored0.2.3167331773 days agocomputestatisticsdatascience
PyPIopteryx0.26.811,341164136 days ago—

Metrics by category

Vitality

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

78Good · 21% of overall

Development activity

70Good
How it's scored
36/36Push recency — last push 3 days ago
15.9/36Commit cadence — 23/52 weeks with commits
18/18Commit volume — 664 commits in the last year
0/10OpenSSF Scorecard: Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year664
human_commit_share—
days_since_last_push3
active_weeks_last_year23

Release discipline

90Excellent
How it's scored
27/27Ships releases — 100 releases published
27/36Release recency — latest release 136 days ago
27/27Release cadence — a release every ~10.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tag0.26.8
releases_from_tagsno
days_since_latest_release136
mean_days_between_releases10.8
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?

62Moderate · 17% of overall

Popularity & adoption

45Weak
How it's scored
33.2/60Stars — 113 stars
9.3/25Forks — 14 forks
2.7/15Watchers — 4 watchers
Inputs used
forks14
stars113
watchers4
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

77Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges—
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateyes

Ecosystem adoption (downloads)

68Good
How it's scored
54.1/80Monthly downloads — 11,341 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesopteryx
dependents—
ecosystemspypi
total_downloads—
monthly_downloads11,341
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

Maintainer resilience (bus factor)

36Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
4.5/22.5Commit distribution — top contributor authored 80% of commits
12.2/13.5Contributor breadth — 9 contributors
10/10OpenSSF Scorecard: Contributors — project has 7 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled9
top_contributor_share0.798

Issue & PR responsiveness

77Good
How it's scored
38.6/42Issue resolution — 92% of issues closed
28.2/30PR acceptance — 1,359/1,448 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/10 approved changesets -- score normalized to 0
Inputs used
merged_prs1,359
open_issues123
closed_issues1,378
prs_merged_7d—
prs_decided_7d—
prs_merged_30d—
prs_decided_30d—
issue_closed_ratio0.918
closed_unmerged_prs89
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.

Ownership & stewardship

70Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
5/25Owner reach — 4 followers of mabel-dev
20.7/25Track record — 25 public repos, account ~5 yr old
Inputs used
followers4
owner_typeOrganization
is_verified—
owner_loginmabel-dev
public_repos25
account_age_days1,891
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 136 days ago
20/20Version history — 164 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesopteryx
ecosystemspypi
any_deprecatedno
min_days_since_publish136

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 13 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 11 out of 11 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

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://opteryx.dev
10/10Repository description
10/10Topics — 20 topics
0/10Wiki
Inputs used
topicspython, sql, analytics, big-data, serverless, sql-engine, arrow, cloud-native, query-engine, database, serverless-database, query-processor, command-line, cross-platform, gcp, aws, data-analytics, query-federation, zero-etl, iceberg
has_wikino
homepagehttps://opteryx.dev
docs_sitehttps://opteryx.dev
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

62Moderate · 16% of overall

Security posture

62Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.5/2.5CI-Tests — 11 out of 11 merged PRs checked by a CI test -- score normalized to 10
0.5/2.5CII-Best-Practices — badge detected: InProgress
0/7.5Code-Review — Found 0/10 approved changesets -- score normalized to 0
2.5/2.5Contributors — project has 7 contributing companies or organizations
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
0/7.5Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
5/5SAST — SAST tool is run on all commits
5/5Security-Policy — security policy file 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_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.2
Excluded from scoring (no data or not applicable): Branch-Protection, 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.

57Moderate · 4% of overall

Agent context & guidance

1Critical
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share—
agent_instruction_files—
agent_instruction_max_bytes—
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.

Verify loop (build / test / typecheck)

74Good
How it's scored
18/18One-command bootstrap — Makefile, third_party/re2/Makefile
22/22Automated tests
11/11Lint / format config
11/11Static type checking — third_party/tktech/simdjson/py.typed
0/10Reproducible environment
0/10Demonstrated agent practice — no data
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
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, third_party/re2/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsthird_party/tktech/simdjson/py.typed
agent_commit_share—
toolchain_manifests—
dependency_bot_commit_share0
Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (third_party/tktech/simdjson/py.typed)
54.9/55Manageable file sizes — 1/508 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes69,450
source_files_sampled508
oversized_source_files1

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

113GitHub stars
9contributors
664commits, last 12 months
3days since last push
100releases
1bus factor
123open issues
crates.io, PyPIpackage ecosystems

Data collection warnings

  • crates package 'compute' points at a different repository (https://github.com/al-jshen/compute); excluded from ecosystem scoring
  • Could not fetch pypi package 'opteryx.third_party.fuzzy' from its registry

More detail

OpenSSF Scorecard 6.2 / 10
6.2aggregate

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 · 2026-07-18 20:53 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
10CI-Tests11 out of 11 merged PRs checked by a CI test -- score normalized to 10
2CII-Best-Practicesbadge detected: InProgress
0Code-ReviewFound 0/10 approved changesets -- score normalized to 0
10Contributorsproject has 7 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 12
RegistryPackageVersion constraintManifest
crates.iopythonize0.27Cargo.toml
crates.ioserde1.0.171Cargo.toml
crates.ioregex1.10Cargo.toml
crates.iopyo30.27Cargo.toml
crates.iosqlparser0.59.0Cargo.toml
PyPIaiohttp—pyproject.toml
PyPInumpy>=2.0.0pyproject.toml
PyPIorjson—pyproject.toml
PyPIorso>=0.0.204pyproject.toml
PyPIpsutil—pyproject.toml
PyPIpyarrow>=20.0.0pyproject.toml
PyPIrequests—pyproject.toml
All dependencies 57

Full resolved dependency set from the GitHub dependency graph: 12 direct and 45 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
crates.iopyo3—direct
crates.iopythonize—direct
crates.ioregex—direct
crates.ioserde—direct
crates.iosqlparser—direct
PyPIaiohttp—direct
PyPInumpy—direct
PyPIorjson—direct
PyPIorso—direct
PyPIpsutil—direct
PyPIpyarrow—direct
PyPIrequests—direct
PyPIblack—indirect
PyPIcassandra-driver—indirect
PyPIcoverage—indirect
PyPIduckdb1.4.*indirect
PyPIduckdb-engine—indirect
PyPIfastavro—indirect
PyPIfreezegun—indirect
PyPIfuzzy1.2.2indirect
PyPIgoogle-cloud-bigquery-storage—indirect
PyPIgoogle-cloud-firestore—indirect
PyPIgoogle-cloud-storage—indirect
PyPIhypothesis—indirect
PyPIisort—indirect
PyPIjellyfish—indirect
PyPIminio—indirect
PyPImypy—indirect
PyPIopenpyxl—indirect
PyPIpandas—indirect
PyPIpg8000—indirect
PyPIpolars—indirect
PyPIprotobuf—indirect
PyPIpsycopg2-binary—indirect
PyPIpycln—indirect
PyPIpydantic2.11.10indirect
PyPIpyiceberg—indirect
PyPIpymemcache—indirect
PyPIpymongo—indirect
PyPIpymysql—indirect
PyPIpyodbc—indirect
PyPIpysimdjson—indirect
PyPIpytest—indirect
PyPIpytest-xdist—indirect
PyPIredis—indirect
PyPIrich—indirect
PyPIsetuptools-rust—indirect
PyPIsqlalchemy—indirect
PyPIsqlalchemy-bigquery—indirect
PyPIsqlalchemy-cockroachdb—indirect
PyPIsqloxide—indirect
PyPItravers0.1.11indirect
PyPItypes-orjson—indirect
PyPItypes-pyyaml—indirect
PyPItypes-requests—indirect
PyPIvalkey—indirect
PyPIvortex-data—indirect
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.

Related records

Inspected repositories sharing catalogue tags or ecosystems with mabel-dev/opteryx. Read them side by side →

98trinodb/trinoOfficial repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)Exceptional99apache/datafusionApache DataFusion SQL Query EngineExceptional98Eventual-Inc/DaftHigh-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scaleExceptional98pulumi/pulumiPulumi - Infrastructure as Code in any programming language 🚀Exceptional100pingcap/tidbTiDB is built for agentic workloads that grow unpredictably, with ACID guarantees and native support for transactions, analytics, and vector search. No data silos. No noisy neighbors. No infrastructure ceiling.Exceptional98valkey-io/valkey-glideAn open source Valkey client library that supports Valkey, and Redis open source 6.2, 7.0 and 7.2. Valkey GLIDE is designed for reliability, optimized performance, and high-availability, for Valkey and Redis OSS based applications. GLIDE is a multi language client library, written in Rust with programming language bindings, such as Java and PythonExceptional

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 v0.14.0 — full methodology · metrics wiki.

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