Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-15 19:20 UTC

PrincetonUniversity / PsyNeuLink

A block modeling system for cognitive neuroscience

Python · HTMLApache-2.0★ 116 stars⑂ 36 forkssince Jun 2016View on GitHub ↗

PrincetonUniversity/PsyNeuLink holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on Vitality (93/100) and lowest on Security (57/100). It was last updated today. 2 contributors account for most of its recent work.

89
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.

89
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 75 is calibrated to 89 on the published index scale (record calibration 2026-08-02).

Ownership

414 followers334 public repossince Jul 2012

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIpsyneulink0.20.2.05,688820 days agocognitivemodeling

Metrics by category

Vitality

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

93Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
35.3/36Commit cadence — 51/52 weeks with commits
18/18Commit volume — 588 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year588
human_commit_share0.87
days_since_last_push0
active_weeks_last_year51
How it's scored
27/27Ships releases — 67 releases published
36/36Release recency — latest release 0 days ago
19.8/27Release cadence — a release every ~106.2 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count67
latest_release_tagv0.20.2.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases106.2

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

61Moderate · 17% of overall
How it's scored
33.4/60Stars — 116 stars
12.9/25Forks — 36 forks
4.3/15Watchers — 7 watchers
Inputs used
forks36
stars116
watchers7
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
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
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno
How it's scored
50.1/80Monthly downloads — 5,688 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagespsyneulink
dependents—
ecosystemspypi
total_downloads—
monthly_downloads5,688
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
25.2/54Bus factor — 2 contributor(s) cover half of all commits
15.4/22.5Commit distribution — top contributor authored 32% of commits
13.5/13.5Contributor breadth — 23 contributors
10/10OpenSSF Scorecard: Contributors — project has 7 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled23
top_contributor_share0.316
How it's scored
25.1/42Issue resolution — 60% of issues closed
24.6/30PR acceptance — 2,860/3,483 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
1.5/15OpenSSF Scorecard: Code-Review — Found 2/12 approved changesets -- score normalized to 1
Inputs used
merged_prs2,860
open_issues60
closed_issues89
prs_merged_7d6
prs_decided_7d6
prs_merged_30d13
prs_decided_30d14
issue_closed_ratio0.597
closed_unmerged_prs623
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
0/20Verified domain
18.8/25Owner reach — 414 followers of PrincetonUniversity
25/25Track record — 334 public repos, account ~14 yr old
Inputs used
followers414
owner_typeOrganization
is_verifiedno
owner_loginPrincetonUniversity
public_repos334
account_age_days5,187

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 — 82 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespsyneulink
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 — 6 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff]), setup.cfg ([pycodestyle])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 20 out of 20 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://psyneulink.org
10/10Repository description
10/10Topics — 3 topics
10/10Wiki
Inputs used
topicscognitive-science, neuroscience, modeling-tools
has_wikiyes
homepagehttps://psyneulink.org
docs_sitehttps://psyneulink.org
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

57Moderate · 16% of overall
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 — 20 out of 20 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
0.8/7.5Code-Review — Found 2/12 approved changesets -- score normalized to 1
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
7.5/7.5Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
4.5/5SAST — SAST tool detected but not run on all commits
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
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_aggregate5.7
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging. 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.

62Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
39.8/40Legible commit history — 65 of 87 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.747
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff]), setup.cfg ([pycodestyle])
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice — 10 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 13 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
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.1
toolchain_manifests—
dependency_bot_commit_share0.13
How it's scored
0/45Type-checkable code — Python without a type-check config
47.9/55Manageable file sizes — 58/448 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes901,826
source_files_sampled448
oversized_source_files58
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — psyneulink/core/rpc/graph.proto
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_filespsyneulink/core/rpc/graph.proto
interfaces_expected_of—
Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

116GitHub stars
23contributors
588commits, last 12 months
0days since last push
67releases
2bus factor
60open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'psyneulink' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • pypi download figures for psyneulink carried forward from the previous scan (stats endpoint unavailable this scan)
  • deps.dev does not index pypi:psyneulink@0.20.2.0; advisories assessed against the repository dependency graph instead
  • No resolved dependencies carried a version and a supported ecosystem

More detail

Star and fork history 0 ★ / 36 ⇿
0Stars
36Forks
62Releases

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.

01325383622017-122022-042026-08
Major 0Minor 0Patch 0

Each point covers 8 days.

OpenSSF Scorecard 5.7 / 10
5.7aggregate

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 · 2026-09-15 19:18 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-Tests20 out of 20 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 2/12 approved changesets -- score normalized to 1
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
10Maintained30 commit(s) and 2 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
9SASTSAST tool detected but not run on all commits
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 40

Full resolved dependency set from the GitHub dependency graph: 0 direct and 40 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIbeartype—indirect
PyPIcmaes—indirect
PyPIdask—indirect
PyPIdask-jobqueue—indirect
PyPIdill—indirect
PyPIdistributed—indirect
PyPIfastkde—indirect
PyPIgraph-scheduler—indirect
PyPIgraphviz—indirect
PyPIgrpcio—indirect
PyPIjupyter—indirect
PyPIleabra-psyneulink—indirect
PyPIllvmlite—indirect
PyPImatplotlib—indirect
PyPInetworkx—indirect
PyPInumpy—indirect
PyPIoptuna—indirect
PyPIpackaging—indirect
PyPIpandas—indirect
PyPIpillow—indirect
PyPIpint—indirect
PyPIprotobuf—indirect
PyPIpsutil—indirect
PyPIpsyneulink-sphinx-theme—indirect
PyPIpycuda—indirect
PyPIpytest—indirect
PyPIpytest-benchmark—indirect
PyPIpytest-cov—indirect
PyPIpytest-forked—indirect
PyPIpytest-helpers-namespace—indirect
PyPIpytest-profiling—indirect
PyPIpytest-pycodestyle—indirect
PyPIpytest-ruff—indirect
PyPIpytest-xdist—indirect
PyPIrich—indirect
PyPIscipy—indirect
PyPIsphinx—indirect
PyPIsphinx-autodoc-typehints—indirect
PyPIstandard-imghdr—indirect
PyPItoposort—indirect
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies carried a version and a supported ecosystem

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 statistics — PyPI.