Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-09 23:21 UTC

elkins-lab / resonance-flow

JAX-native differentiable protein folding framework integrating experimental NMR constraints (RDCs) and biophysical "self-correction." Several Jupyter Notebooks visualize the concepts.

PythonMIT★ 1 star⑂ 0 forkssince May 2026View on GitHub ↗
KindLibraryhow this is determined
#bioinformatics#biophysics#differentiable-programming#jax#machine-learning#nmr-spectroscopy#protein-folding#structural-biology#computational-biophysics#flax#computational-structural-biology#residual-dipolar-coupling#protein-refinement#protein-structure-prediction

elkins-lab/resonance-flow holds a health index of 65 out of 100, placing it in the Good band. It scores highest on Engineering Quality (95/100) and lowest on Community & Adoption (24/100). It was last updated 6 days ago. A single contributor accounts for most of its recent work.

65
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.

65
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 60 is calibrated to 65 on the published index scale (record calibration 2026-08-02).

Ownership

Elkins LabOrganization
1 follower20 public repossince Jun 2026

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIresonance-flow0.1.3-371 days ago

Metrics by category

Vitality

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

77Good · 21% of overall

Development activity

62Moderate
How it's scored
36/36Push recency — last push 6 days ago
3.5/36Commit cadence — 5/52 weeks with commits
15.7/18Commit volume — 55 commits in the last year
7/10OpenSSF Scorecard: Maintained — 9 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 7
Inputs used
commits_last_year55
human_commit_share1
days_since_last_push6
active_weeks_last_year5

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 3 releases published
36/36Release recency — latest release 71 days ago
27/27Release cadence — a release every ~17.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count3
latest_release_tagv0.1.3
releases_from_tagsno
days_since_latest_release71
mean_days_between_releases17.1
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?

24At Risk · 17% of overall

Popularity & adoption

1Critical
How it's scored
0/60Stars — 1 stars
0/25Forks — 0 forks
0/15Watchers — 0 watchers
Inputs used
forks0
stars1
watchers0
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_badges10
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicescodecov.io, github.com, shields.io
has_pull_request_templateno

Sustainability & Governance

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

49Weak · 23% of overall

Maintainer resilience (bus factor)

10Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0/22.5Commit distribution — top contributor authored 100% of commits
1.4/13.5Contributor breadth — 1 contributors
0/10OpenSSF Scorecard: Contributors — project has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1

Issue & PR responsiveness

67Good
How it's scored
0/42Issue resolution — no issues or no data
30/30PR acceptance — 1/1 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs1
open_issues0
closed_issues0
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio—
closed_unmerged_prs0
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Issue resolution, Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

42Weak
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
2.2/25Owner reach — 1 followers of elkins-lab
10.1/25Track record — 20 public repos, account ~0 yr old
Inputs used
followers1
owner_typeOrganization
is_verifiedno
owner_loginelkins-lab
public_repos20
account_age_days89

Package maintenance

92Excellent
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 71 days ago
12/20Version history — 3 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesresonance-flow
ecosystemspypi
any_deprecatedno
min_days_since_publish71

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://elkins-lab.github.io/resonance-flow/
10/10Repository description
10/10Topics — 14 topics
10/10Wiki
Inputs used
topicsbioinformatics, biophysics, differentiable-programming, jax, machine-learning, nmr-spectroscopy, protein-folding, structural-biology, computational-biophysics, flax, computational-structural-biology, residual-dipolar-coupling, protein-refinement, protein-structure-prediction
has_wikiyes
homepagehttps://elkins-lab.github.io/resonance-flow/
docs_sitehttps://elkins-lab.github.io/resonance-flow/
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

Security posture

38Weak
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 — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
0/2.5Contributors — project has 0 contributing companies or organizations -- score normalized to 0
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
5.2/7.5Maintained — 9 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 7
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — no SAST tool detected
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
6/7.5Vulnerabilities — 2 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate3.8
Excluded from scoring (no data or not applicable): CI-Tests, 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
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages29
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:resonance-flow@0.1.3 runtime dependency closure — what installing the published package pulls in — 29 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.

54Moderate · 4% of overall

Agent context & guidance

40Weak
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 45 of 55 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.818
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

47Weak
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — lockfile
3.6/10Demonstrated agent practice — 1 of the last 55 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
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.018
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

55Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/11 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes17,435
source_files_sampled11
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 — 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

1GitHub stars
1contributors
55commits, last 12 months
6days since last push
3releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

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

More detail

OpenSSF Scorecard 3.8 / 10
3.8aggregate

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 · 2026-09-09 23:21 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
7Maintained9 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 7
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
8Vulnerabilities2 existing vulnerabilities detected
Direct dependencies 5
RegistryPackageVersion constraintManifest
PyPIjax—pyproject.toml
PyPIjaxlib—pyproject.toml
PyPIflax—pyproject.toml
PyPIoptax—pyproject.toml
PyPInumpy—pyproject.toml
All dependencies 73

Full resolved dependency set from the GitHub dependency graph: 5 direct and 68 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIflax0.12.7direct
PyPIjax0.10.2direct
PyPIjaxlib0.10.2direct
PyPInumpy2.5.0direct
PyPIoptax0.2.8direct
PyPIabsl-py2.4.0indirect
PyPIaiofiles25.1.0indirect
PyPIast-serialize0.5.0indirect
PyPIbabel2.18.0indirect
PyPIbackrefs7.0indirect
PyPIcertifi2026.6.17indirect
PyPIcharset-normalizer3.4.7indirect
PyPIclick8.4.2indirect
PyPIcolorama0.4.6indirect
PyPIcoverage7.14.3indirect
PyPIetils1.14.0indirect
PyPIfsspec2026.6.0indirect
PyPIghp-import2.1.0indirect
PyPIgriffelib2.1.0indirect
PyPIhumanize4.16.0indirect
PyPIidna3.18indirect
PyPIiniconfig2.3.0indirect
PyPIjinja23.1.6indirect
PyPIlibrt0.12.0indirect
PyPImarkdown3.10.2indirect
PyPImarkdown-it-py4.2.0indirect
PyPImarkupsafe3.0.3indirect
PyPImdurl0.1.2indirect
PyPImergedeep1.3.4indirect
PyPImkdocs1.6.1indirect
PyPImkdocs-autorefs1.4.4indirect
PyPImkdocs-get-deps0.2.2indirect
PyPImkdocs-material9.7.6indirect
PyPImkdocs-material-extensions1.3.1indirect
PyPImkdocstrings1.0.4indirect
PyPImkdocstrings-python2.0.5indirect
PyPIml-dtypes0.5.4indirect
PyPImsgpack1.2.1indirect
PyPImypy2.1.0indirect
PyPImypy-extensions1.1.0indirect
PyPInest-asyncio1.6.0indirect
PyPIopt-einsum3.4.0indirect
PyPIorbax-checkpoint0.12.1indirect
PyPIpackaging26.2indirect
PyPIpaginate0.5.7indirect
PyPIpathspec1.1.1indirect
PyPIplatformdirs4.10.0indirect
PyPIpluggy1.6.0indirect
PyPIprometheus-client0.25.0indirect
PyPIprotobuf7.35.1indirect
PyPIpsutil7.2.2indirect
PyPIpygments2.20.0indirect
PyPIpymdown-extensions11.0indirect
PyPIpytest9.1.1indirect
PyPIpytest-cov7.1.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpyyaml6.0.3indirect
PyPIpyyaml-env-tag1.1indirect
PyPIrequests2.34.2indirect
PyPIresonance-flow0.1.3indirect
PyPIrich15.0.0indirect
PyPIruff0.15.20indirect
PyPIscipy1.18.0indirect
PyPIsetuptools—indirect
PyPIsimplejson4.1.1indirect
PyPIsix1.17.0indirect
PyPItensorstore0.1.84indirect
PyPItreescope0.1.10indirect
PyPItyping-extensions4.15.0indirect
PyPIurllib32.7.0indirect
PyPIuvloop0.22.1indirect
PyPIwatchdog6.0.0indirect
PyPIzipp4.1.0indirect
Dependency advisories 0

Installing pypi:resonance-flow@0.1.3 pulls in 29 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

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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.