Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-05 17:08 UTC

Project-AgML / AgML

AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.

PythonApache-2.0★ 332 stars⑂ 52 forkssince Oct 2021View on GitHub ↗

Project-AgML/AgML holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Engineering Quality (86/100) and lowest on Security (50/100). It was last updated 22 days ago. A single contributor accounts for most of its recent work.

78
overall / 100
Good

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.

78
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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

AgMLOrganization
85 followers6 public repossince Dec 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIagml0.8.03153056 days ago

Metrics by category

Vitality

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

68Good · 21% of overall
How it's scored
28.8/36Push recencylast push 22 days ago
5.5/36Commit cadence8/52 weeks with commits
14/18Commit volume35 commits in the last year
10/10OpenSSF Scorecard: Maintained12 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year35
human_commit_share1
days_since_last_push22
active_weeks_last_year8
How it's scored
27/27Ships releases28 releases published
36/36Release recencylatest release 56 days ago
12.6/27Release cadencea release every ~121.3 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count28
latest_release_tagv0.8.0
releases_from_tagsno
days_since_latest_release56
mean_days_between_releases121.3
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?

73Good · 17% of overall
How it's scored
40.9/60Stars332 stars
14.2/25Forks52 forks
6.7/15Watchers17 watchers
Inputs used
forks52
stars332
watchers17
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

61Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
4.2/22.5Commit distributiontop contributor authored 82% of commits
13.5/13.5Contributor breadth14 contributors
6/10OpenSSF Scorecard: Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled14
top_contributor_share0.815
How it's scored
28/42Issue resolution67% of issues closed
28.9/30PR acceptance51/53 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/7 approved changesets -- score normalized to 0
Inputs used
merged_prs51
open_issues10
closed_issues20
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.667
closed_unmerged_prs2
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 backingorganization-owned
0/20Verified domain
13.9/25Owner reach85 followers of Project-AgML
15.5/25Track record6 public repos, account ~4 yr old
Inputs used
followers85
owner_typeOrganization
is_verifiedno
owner_loginProject-AgML
public_repos6
account_age_days1,709

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 56 days ago
20/20Version history30 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesagml
ecosystemspypi
any_deprecatedno
min_days_since_publish56

Engineering Quality

Are baseline engineering and documentation practices in place?

86Excellent · 19% of overall
How it's scored
24/24CI workflows2 workflow(s)
24/24Tests present
16/16Linter configruff.toml
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests6 out of 6 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

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://project-agml.github.io
10/10Repository description
10/10Topics9 topics
0/10Wiki
Inputs used
topicsdeep-learning, agriculture, pytorch, dataset, image-classification, object-detection, semantic-segmentation, computer-vision, synthetic-data
has_wikino
homepagehttps://project-agml.github.io
docs_sitehttps://project-agml.github.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

50Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2.5/2.5CI-Tests6 out of 6 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
0/7.5Code-ReviewFound 0/7 approved changesets -- score normalized to 0
1.5/2.5Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained12 commit(s) and 0 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
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities33 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate3.7
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages58
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:agml@0.8.0 runtime dependency closure — what installing the published package pulls in — 58 packages. 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.

59Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
11.2/40Legible commit history21 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.21
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
11/11Lint / format configruff.toml
11/11Static type checkingconfig/mypy.ini
10/10Reproducible environmentlockfile
0/10Demonstrated agent practiceno agent-authored commits among the last 100
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
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configsconfig/mypy.ini
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
27/45Type-checkable codePython with type-check config (config/mypy.ini)
53.7/55Manageable file sizes3/125 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes86,964
source_files_sampled125
oversized_source_files3
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples, 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

332GitHub stars
14contributors
35commits, last 12 months
22days since last push
28releases
1bus factor
10open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 52 ⇿
0Stars
52Forks
25Releases

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.

010203040504842022-012024-052026-08
Major 0Minor 6Patch 19

Each point covers 5 days.

OpenSSF Scorecard 3.7 / 10
3.7aggregate

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 · 2026-09-05 17:07 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-Tests6 out of 6 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/7 approved changesets -- score normalized to 0
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
10Licenselicense file detected
10Maintained12 commit(s) and 0 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
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
0Vulnerabilities33 existing vulnerabilities detected
Direct dependencies 11
RegistryPackageVersion constraintManifest
PyPImatplotlib>=3.7.5pyproject.toml
PyPInumpy>=1.24.4pyproject.toml
PyPIopencv-python>=4.10.0.84pyproject.toml
PyPIopencv-python-headless>=4.10.0.84pyproject.toml
PyPIrequests>=2.0.0pyproject.toml
PyPItqdm>=4.67.0pyproject.toml
PyPIpyyaml>=6.0.2pyproject.toml
PyPIalbumentations>=1.4.18pyproject.toml
PyPIdict2xml>=1.7.6pyproject.toml
PyPIrich>=14.0.0pyproject.toml
PyPIdatasetspyproject.toml
All dependencies 90

Full resolved dependency set from the GitHub dependency graph: 11 direct and 79 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIalbumentations2.0.5direct
PyPIdatasets5.0.0direct
PyPIdict2xml1.7.6direct
PyPImatplotlib3.10.1direct
PyPInumpy2.2.4direct
PyPIopencv-python4.11.0.86direct
PyPIopencv-python-headless4.11.0.86direct
PyPIpyyaml6.0.2direct
PyPIrequests2.32.3direct
PyPIrich14.0.0direct
PyPItqdm4.67.1direct
PyPIagml0.8.0indirect
PyPIaiohappyeyeballs2.7.1indirect
PyPIaiohttp3.14.1indirect
PyPIaiosignal1.4.0indirect
PyPIalbucore0.0.23indirect
PyPIannotated-types0.7.0indirect
PyPIanyio4.14.1indirect
PyPIasync-timeout5.0.1indirect
PyPIattrs25.3.0indirect
PyPIboto31.37.31indirect
PyPIbotocore1.37.31indirect
PyPIcertifi2025.1.31indirect
PyPIcharset-normalizer3.4.1indirect
PyPIclick8.4.2indirect
PyPIcolorama0.4.6indirect
PyPIcontourpy1.3.1indirect
PyPIcoverage7.8.0indirect
PyPIcycler0.12.1indirect
PyPIdill0.4.1indirect
PyPIexceptiongroup1.2.2indirect
PyPIfilelock3.29.7indirect
PyPIfonttools4.57.0indirect
PyPIfrozenlist1.8.0indirect
PyPIfsspec2026.4.0indirect
PyPIh110.16.0indirect
PyPIhf-xet1.5.1indirect
PyPIhttpcore1.0.9indirect
PyPIhttpx0.28.1indirect
PyPIhuggingface-hub1.23.0indirect
PyPIidna3.10indirect
PyPIimageio2.37.0indirect
PyPIiniconfig2.1.0indirect
PyPIinterrogate1.7.0indirect
PyPIjmespath1.0.1indirect
PyPIjoblib1.4.2indirect
PyPIkiwisolver1.4.8indirect
PyPIlazy-loader0.4indirect
PyPImarkdown-it-py3.0.0indirect
PyPImdurl0.1.2indirect
PyPImultidict6.7.1indirect
PyPImultiprocess0.70.19indirect
PyPImypy1.15.0indirect
PyPImypy-extensions1.0.0indirect
PyPInetworkx3.4.2indirect
PyPIpackaging24.2indirect
PyPIpandas2.2.3indirect
PyPIpillow11.1.0indirect
PyPIpluggy1.5.0indirect
PyPIpropcache0.5.2indirect
PyPIpy1.11.0indirect
PyPIpyarrow25.0.0indirect
PyPIpydantic2.11.3indirect
PyPIpydantic-core2.33.1indirect
PyPIpygments2.19.1indirect
PyPIpyparsing3.2.3indirect
PyPIpytest8.3.5indirect
PyPIpytest-cov6.1.1indirect
PyPIpytest-order1.3.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpytz2025.2indirect
PyPIruff0.11.5indirect
PyPIs3transfer0.11.4indirect
PyPIscikit-image0.25.2indirect
PyPIscikit-learn1.6.1indirect
PyPIscipy1.15.2indirect
PyPIshapely2.1.0indirect
PyPIsimsimd6.2.1indirect
PyPIsix1.17.0indirect
PyPIstringzilla3.12.3indirect
PyPItabulate0.9.0indirect
PyPIthreadpoolctl3.6.0indirect
PyPItifffile2025.3.30indirect
PyPItomli2.2.1indirect
PyPItyping-extensions4.13.2indirect
PyPItyping-inspection0.4.0indirect
PyPItzdata2025.2indirect
PyPIurllib32.4.0indirect
PyPIxxhash3.8.1indirect
PyPIyarl1.24.2indirect
Dependency advisories 0

Installing pypi:agml@0.8.0 pulls in 58 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

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 statisticsPyPI.