Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-08 14:21 UTC

a-r-j / ProteinWorkshop

Benchmarking framework for protein representation learning. Includes a large number of pre-training and downstream task datasets, models and training/task utilities. (ICLR 2024)

Python · Jupyter NotebookMIT★ 279 stars⑂ 22 forkssince May 2023View on GitHub ↗
KindCommand-line toolLibraryhow this is determined
#benchmark#dataset#deep-learning#lightning#pretraining#protein#protein-structure#pytorch

a-r-j/ProteinWorkshop holds a health index of 48 out of 100, placing it in the Weak band. It scores highest on Engineering Quality (84/100) and lowest on Vitality (25/100). It was last updated 498 days ago. 2 contributors account for most of its recent work.

48
overall / 100
Weak

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.

48
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 55 is calibrated to 57 on the published index scale (record calibration 2026-08-02).

Ownership

Arian JamasbPersonal account
238 followers34 public repossince Nov 2016University of Cambridge

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 publishTags
PyPIproteinworkshop0.2.5-7984 days agoproteinsdeep-learningdatasetsbenchmarks

Metrics by category

Vitality

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

25At Risk · 21% of overall

Development activity

1Critical
How it's scored
0/36Push recency — last push 498 days ago
0/36Commit cadence — 0/52 weeks with commits
0/18Commit volume — 0 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_year0
human_commit_share0.99
days_since_last_push498
active_weeks_last_year0

Release discipline

60Moderate
How it's scored
27/27Ships releases — 6 releases published
0/36Release recency — latest release 984 days ago
27/27Release cadence — a release every ~24.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count6
latest_release_tagv0.2.5
releases_from_tagsno
days_since_latest_release984
mean_days_between_releases24.6
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?

52Moderate · 17% of overall

Popularity & adoption

54Moderate
How it's scored
39.6/60Stars — 279 stars
11/25Forks — 22 forks
3.9/15Watchers — 6 watchers
Inputs used
forks22
stars279
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

50Moderate
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesbadge.fury.io, github.com, readthedocs.org, shields.io
has_pull_request_templateno

Sustainability & Governance

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

69Good · 23% of overall

Maintainer resilience (bus factor)

62Moderate
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
14.8/22.5Commit distribution — top contributor authored 34% of commits
12.2/13.5Contributor breadth — 9 contributors
10/10OpenSSF Scorecard: Contributors — project has 8 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled9
top_contributor_share0.343

Issue & PR responsiveness

82Excellent
How it's scored
32.7/42Issue resolution — 78% of issues closed
26.4/30PR acceptance — 67/76 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
12/15OpenSSF Scorecard: Code-Review — Found 9/11 approved changesets -- score normalized to 8
Inputs used
merged_prs67
open_issues6
closed_issues21
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.778
closed_unmerged_prs9
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.

Ownership & stewardship

63Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
17.1/25Owner reach — 238 followers of a-r-j
23.2/25Track record — 34 public repos, account ~9 yr old
Inputs used
followers238
owner_typeUser
is_verified—
owner_logina-r-j
public_repos34
account_age_days3,575
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

69Good
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
4/35Publish recency — latest publish 984 days ago
20/20Version history — 7 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesproteinworkshop
ecosystemspypi
any_deprecatedno
min_days_since_publish984

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall

Engineering practices

74Good
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 0 out of 11 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://proteins.sh/
10/10Repository description
10/10Topics — 8 topics
10/10Wiki
Inputs used
topicsbenchmark, dataset, deep-learning, lightning, pretraining, protein, protein-structure, pytorch
has_wikiyes
homepagehttps://proteins.sh/
docs_sitehttps://proteins.sh/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

41Weak · 16% of overall

Security posture

39Weak
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — 0 out of 11 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6/7.5Code-Review — Found 9/11 approved changesets -- score normalized to 8
2.5/2.5Contributors — project has 8 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
0/5Packaging — no data
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 — 197 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate3.9
Excluded from scoring (no data or not applicable): Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

51Moderate
How it's scored
13.2/35Direct dependencies free of known advisories — 1 affected: lightning 2.6.5 (high 7.8)
8.4/25Indirect dependencies free of known advisories — 2 affected: lxml 5.4.0 (high 7.5), protobuf 4.25.9 (high 7.5)
29.1/40No advisories left outstanding — 2 advisory-carrying package(s) unaddressed past 90 days; oldest published 227 days ago
Inputs used
sourceosv
advisories6
affected_packages3
assessed_packages234
unassessed_packages0
affected_by_severityhigh 3
direct_affected_packages1
Matched the pypi:proteinworkshop@0.2.5 runtime dependency closure — what installing the published package pulls in — 234 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.

58Moderate · 4% of overall

Agent context & guidance

23At Risk
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
22.6/40Legible commit history — 42 of 99 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.424
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

69Good
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
0/11Static type checking
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 1 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
lockfilespoetry.lock
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.01

Code legibility for models

55Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/130 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes42,094
source_files_sampled130
oversized_source_files0

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 — notebooks
Inputs used
example_dirsnotebooks
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

279GitHub stars
9contributors
0commits, last 12 months
498days since last push
6releases
2bus factor
6open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'proteinworkshop' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 22 ⇿
0Stars
22Forks
6Releases

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.

0481216202022023-082024-092025-11
Major 0Minor 1Patch 5

Each point covers 3 days.

OpenSSF Scorecard 3.9 / 10
3.9aggregate

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 · 2026-09-08 14:20 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
0CI-Tests0 out of 11 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
8Code-ReviewFound 9/11 approved changesets -- score normalized to 8
10Contributorsproject has 8 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
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
0Vulnerabilities197 existing vulnerabilities detected
Direct dependencies 32
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All dependencies 240

Full resolved dependency set from the GitHub dependency graph: 31 direct and 209 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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PyPIurllib32.2.1indirect
PyPIvirtualenv20.25.1indirect
PyPIwatermark2.4.3indirect
PyPIwcwidth0.2.13indirect
PyPIwebencodings0.5.1indirect
PyPIwin32-setctime1.1.0indirect
PyPIwrapt1.16.0indirect
PyPIxarray2024.2.0indirect
PyPIxmltodict0.13.0indirect
PyPIxyzservices2023.10.1indirect
PyPIyarl1.9.4indirect
PyPIzipp3.18.1indirect
PyPIzope-event5.0indirect
PyPIzope-interface6.2indirect
Dependency advisories 3

Installing pypi:proteinworkshop@0.2.5 pulls in 234 packages, direct and transitive: 3 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
lightning2.6.5directhigh22022.6.15
lxml5.4.0indirecthigh26.1.0
protobuf4.25.9indirecthigh26.33.5

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.

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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.