Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-08 08:11 UTC

orq-ai / evaluatorq

Evaluation module for the orq platfrom in python, including Red Teaming and Agent Simulation

PythonMIT★ 21 stars⑂ 0 forkssince Jun 2026View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

orq-ai/evaluatorq holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on AI Readiness (93/100) and lowest on Security (49/100). It was last updated today. 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

orq.aiOrganization · verified domain
47 followers22 public repossince Oct 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIevaluatorq1.37.05,743680 days ago

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
22.2/36Commit cadence — 32/52 weeks with commits
18/18Commit volume — 1,615 commits in the last year
0/10OpenSSF Scorecard: Maintained — project was created within the last 90 days. Please review its contents carefully
Inputs used
commits_last_year1,615
human_commit_share0.97
days_since_last_push0
active_weeks_last_year32

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 55 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~0.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count55
latest_release_tagv1.38.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases0.9
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?

49Weak · 17% of overall
How it's scored
21.1/60Stars — 21 stars
0/25Forks — 0 forks
0/15Watchers — 0 watchers
Inputs used
forks0
stars21
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno
How it's scored
50.1/80Monthly downloads — 5,743 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesevaluatorq
dependents—
ecosystemspypi
total_downloads—
monthly_downloads5,743
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?

72Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
5.7/22.5Commit distribution — top contributor authored 75% of commits
10.8/13.5Contributor breadth — 8 contributors
10/10OpenSSF Scorecard: Contributors — project has 4 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled8
top_contributor_share0.747
How it's scored
33.6/42Issue resolution — 80% of issues closed
28.2/30PR acceptance — 177/188 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
9/15OpenSSF Scorecard: Code-Review — Found 13/21 approved changesets -- score normalized to 6
Inputs used
merged_prs177
open_issues1
closed_issues4
prs_merged_7d12
prs_decided_7d13
prs_merged_30d51
prs_decided_30d55
issue_closed_ratio0.8
closed_unmerged_prs11
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
20/20Verified domain
12.1/25Owner reach — 47 followers of orq-ai
19.7/25Track record — 22 public repos, account ~4 yr old
Inputs used
followers47
owner_typeOrganization
is_verifiedyes
owner_loginorq-ai
public_repos22
account_age_days1,784

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 — 68 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesevaluatorq
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall
How it's scored
24/24CI workflows — 7 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 23 out of 23 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 site — https://orq-ai.github.io/evaluatorq/
10/10Repository description
10/10Topics — 3 topics
10/10Wiki
Inputs used
topicsagentic-ai, evaluation, orq
has_wikiyes
homepagehttps://orq-ai.github.io/evaluatorq/
docs_sitehttps://orq-ai.github.io/evaluatorq/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

49Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
3/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 23 out of 23 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
4.5/7.5Code-Review — Found 13/21 approved changesets -- score normalized to 6
2.5/2.5Contributors — project has 4 contributing companies or organizations
0/10Dangerous-Workflow — 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 — project was created within the last 90 days. Please review its contents carefully
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
0/7.5Vulnerabilities — 11 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate3.6
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
advisories13
affected_packages5
assessed_packages288
unassessed_packages0
affected_by_severitycritical 2, high 2, moderate 1
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 288 resolved dependencies against OSV. 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.

93Exceptional · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md, CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://orq-ai.github.io/evaluatorq/llms.txt)
40/40Legible commit history — 93 of 97 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://orq-ai.github.io/evaluatorq/llms.txt
legible_history_share0.959
agent_instruction_filesAGENTS.md, CLAUDE.md
agent_instruction_max_bytes29,693
How it's scored
18/18One-command bootstrap — examples/agent_simulation/webinar_demo/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — src/evaluatorq/py.typed, src/py.typed
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 66 of the last 100 commits agent-authored or agent-credited
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
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesexamples/agent_simulation/webinar_demo/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configssrc/evaluatorq/py.typed, src/py.typed
agent_commit_share0.66
toolchain_manifests—
dependency_bot_commit_share0.03
How it's scored
27/45Type-checkable code — Python with type-check config (src/evaluatorq/py.typed, src/py.typed)
53.6/55Manageable file sizes — 18/715 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes514,536
source_files_sampled715
oversized_source_files18
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

21GitHub stars
8contributors
1,615commits, last 12 months
0days since last push
55releases
1bus factor
1open 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:evaluatorq@1.37.0; advisories assessed against the repository dependency graph instead

More detail

OpenSSF Scorecard 3.6 / 10
3.6aggregate

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 · 2026-09-08 08:10 UTC

10Binary-Artifactsno binaries found in the repo
4Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests23 out of 23 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 13/21 approved changesets -- score normalized to 6
10Contributorsproject has 4 contributing companies or organizations
0Dangerous-Workflowdangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintainedproject was created within the last 90 days. Please review its contents carefully
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
0Vulnerabilities11 existing vulnerabilities detected
Direct dependencies 7
RegistryPackageVersion constraintManifest
PyPIpydantic>=2.0pyproject.toml
PyPIhttpx>=0.28.1pyproject.toml
PyPIrich>=14.2.0pyproject.toml
PyPIloguru>=0.6.0pyproject.toml
PyPItyper>=0.12.0pyproject.toml
PyPIopenai>=1.92.0,<4.0.0pyproject.toml
PyPIorq-ai-sdk>=4.12pyproject.toml
All dependencies 288

Full resolved dependency set from the GitHub dependency graph: 15 direct and 273 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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PyPItokenizers0.23.1indirect
PyPItoml0.10.2indirect
PyPItomli2.0.2indirect
PyPItomli-w1.1.0indirect
PyPItornado6.5.8indirect
PyPItqdm4.67.3indirect
PyPItruststore0.10.4indirect
PyPItypes-requests2.33.0.20260408indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2025.3indirect
PyPIurllib32.7.0indirect
PyPIuuid-utils0.14.0indirect
PyPIuv0.11.21indirect
PyPIuvicorn0.44.0indirect
PyPIuvicorn0.46.0indirect
PyPIuvloop0.22.1indirect
PyPIvl-convert-python1.9.0.post1indirect
PyPIwatchdog6.0.0indirect
PyPIwatchfiles1.1.1indirect
PyPIwatchfiles1.2.0indirect
PyPIwcwidth0.8.2indirect
PyPIwebencodings0.5.1indirect
PyPIwebsocket-client1.9.0indirect
PyPIwebsockets15.0.1indirect
PyPIwebsockets16.0indirect
PyPIwin32-setctime1.2.0indirect
PyPIxxhash3.6.0indirect
PyPIyarl1.24.2indirect
PyPIzipp3.23.0indirect
PyPIzstandard0.25.0indirect
Dependency advisories 5

This repository publishes no package the index resolves, so its own dependency graph was assessed — 288 packages, which also include development and test pins that never ship: 5 carry known advisories, of which 0 are direct.

PackageVersionRelationSeverityAdvisoriesFixed in
chromadb1.1.1indirectcritical5—
gitpython3.1.58indirectcritical43.1.59
click8.3.1indirecthigh18.3.3
json-repair0.25.2indirecthigh10.60.1
requests2.32.4indirectmoderate22.33.0

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

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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.

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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.