Public recordExcellent
Software health reportschema 0.27.0 · metrics 1.13.0 · 2026-07-30 22:15 UTC
mne-tools / mne-python
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
mne-tools/mne-python holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (96/100) and lowest on AI Readiness (64/100). It was last updated today. 3 contributors account for most of its recent work.
89
overall / 100Software health index
Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.
89
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe problems (abandoned, single-maintainer, no hygiene)
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.
Ownership
MNE tools for MEG and EEG data analysisOrganization
312 followers49 public repossince Jan 2011
This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.
Package ecosystems
| Registry | Package | Version | Downloads / mo | Versions | Last publish | Tags |
|---|---|---|---|---|---|---|
| PyPI | mne | 1.12.1 | 424,271 | 86 | 101 days ago | ecogeegmegbrainfnirsneuroimagingneuroscience |
Metrics by category
Vitality
Is the project alive — is code being written and are releases shipping?
93
Development activity
100Excellent
How it's scored
| 36/36 | Push recency — last push 0 days ago |
| 36/36 | Commit cadence — 52/52 weeks with commits |
| 18/18 | Commit volume — 452 commits in the last year |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 19 issue activity found in the last 90 days -- score normalized to 10 |
Inputs used
| commits_last_year | 452 |
| human_commit_share | 0.87 |
| days_since_last_push | 0 |
| active_weeks_last_year | 52 |
Release discipline
82Good
How it's scored
| 27/27 | Ships releases — 64 releases published |
| 27/36 | Release recency — latest release 101 days ago |
| 19.8/27 | Release cadence — a release every ~81.2 days |
| 0/10 | OpenSSF Scorecard: Signed-Releases — no data |
Inputs used
| releases_count | 64 |
| latest_release_tag | v1.12.1 |
| releases_from_tags | no |
| days_since_latest_release | 101 |
| mean_days_between_releases | 81.2 |
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?
93
Popularity & adoption
93Excellent
How it's scored
| 57.4/60 | Stars — 3,475 stars |
| 25/25 | Forks — 1,576 forks |
| 10.6/15 | Watchers — 83 watchers |
Inputs used
| forks | 1,576 |
| stars | 3,475 |
| watchers | 83 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community health
92Excellent
How it's scored
| 22.5/22.5 | README |
| 22.5/22.5 | License — recognized license (BSD-3-Clause) |
| 18/18 | CONTRIBUTING guide |
| 13.5/13.5 | Code of conduct |
| 0/7.2 | Issue template |
| 6.3/6.3 | PR template |
Inputs used
| has_readme | yes |
| has_license | yes |
| has_contributing | yes |
| has_issue_template | no |
| has_code_of_conduct | yes |
| has_pull_request_template | yes |
Ecosystem adoption (downloads)
94Excellent
How it's scored
| 75/80 | Monthly downloads — 424,271 downloads/month across pypi |
| 0/20 | Registry dependents — not reported by this ecosystem |
Inputs used
| packages | mne |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 424,271 |
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?
83
How it's scored
| 36/54 | Bus factor — 3 contributor(s) cover half of all commits |
| 16.3/22.5 | Commit distribution — top contributor authored 28% of commits |
| 13.5/13.5 | Contributor breadth — 97 contributors |
| 10/10 | OpenSSF Scorecard: Contributors — project has 61 contributing companies or organizations |
Inputs used
| bus_factor | 3 |
| contributors_sampled | 97 |
| top_contributor_share | 0.276 |
Issue & PR responsiveness
87Excellent
How it's scored
| 42.1/46.8 | Issue resolution — 90% of issues closed |
| 33.2/38.3 | PR acceptance — 7,630/8,788 decided PRs merged |
| 12/15 | OpenSSF Scorecard: Code-Review — Found 26/29 approved changesets -- score normalized to 8 |
Inputs used
| merged_prs | 7,630 |
| open_issues | 520 |
| closed_issues | 4,656 |
| issue_closed_ratio | 0.9 |
| closed_unmerged_prs | 1,158 |
Ownership & stewardship
72Good
How it's scored
| 30/30 | Ownership backing — organization-owned |
| 0/20 | Verified domain |
| 17.9/25 | Owner reach — 312 followers of mne-tools |
| 24.4/25 | Track record — 49 public repos, account ~15 yr old |
Inputs used
| followers | 312 |
| owner_type | Organization |
| is_verified | — |
| owner_login | mne-tools |
| public_repos | 49 |
| account_age_days | 5,664 |
Package maintenance
100Excellent
How it's scored
| 25/25 | Published & resolvable — 1 package(s) on pypi |
| 35/35 | Publish recency — latest publish 101 days ago |
| 20/20 | Version history — 86 published versions |
| 20/20 | Not deprecated — active, not deprecated or yanked |
Inputs used
| packages | mne |
| ecosystems | pypi |
| any_deprecated | no |
| min_days_since_publish | 101 |
Engineering Quality
Are baseline engineering and documentation practices in place?
96
Engineering practices
94Excellent
How it's scored
| 24/24 | CI workflows — 9 workflow(s) |
| 24/24 | Tests present |
| 16/16 | Linter config |
| 9.6/9.6 | Pre-commit hooks |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10 |
Inputs used
| has_ci | yes |
| has_tests | yes |
| has_editorconfig | no |
| has_linter_config | yes |
| has_precommit_config | yes |
Documentation
100Excellent
How it's scored
| 30/30 | README |
| 25/25 | Documentation directory |
| 15/15 | Documentation / homepage site — https://mne.tools |
| 10/10 | Repository description |
| 10/10 | Topics — 12 topics |
| 10/10 | Wiki |
Inputs used
| topics | python, neuroscience, electroencephalography, magnetoencephalography, electrocorticography, machine-learning, statistics, visualization, eeg, meg, ecog, neuroimaging |
| has_wiki | yes |
| homepage | https://mne.tools |
| has_readme | yes |
| has_docs_dir | yes |
| has_description | yes |
Security
Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?
82
Security posture
78Good
How it's scored
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — no data |
| 2.5/2.5 | CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10 |
| 1.8/2.5 | CII-Best-Practices — badge detected: Silver |
| 6/7.5 | Code-Review — Found 26/29 approved changesets -- score normalized to 8 |
| 2.5/2.5 | Contributors — project has 61 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | License — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 19 issue activity found in the last 90 days -- score normalized to 10 |
| 5/5 | Packaging — packaging workflow detected |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 4.5/5 | SAST — SAST tool detected but not run on all commits |
| 5/5 | Security-Policy — security policy file detected |
| 0/7.5 | Signed-Releases — no data |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
Inputs used
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 7.8 |
Excluded from scoring (no data or not applicable): branch_protection, signed_releases. Remaining weights renormalized.
Dependency advisories
100Excellent
How it's scored
| 35/35 | Direct dependencies free of known advisories — no direct dependency carries a known advisory |
| 25/25 | Indirect dependencies free of known advisories — no indirect dependency carries a known advisory |
| 0/40 | No advisories left outstanding — no advisory carries a publication date |
Inputs used
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 24 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:mne@1.12.1 runtime dependency closure — what installing the published package pulls in — 24 packages. Reachability is not analyzed.
AI Readiness
How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score.
64
Agent context & guidance
85Excellent
How it's scored
| 45/45 | Agent instructions — AGENTS.md, CLAUDE.md |
| 0/15 | Machine-readable docs (llms.txt) |
| 40/40 | Legible commit history — 78 of 87 human commits state their intent (structured subject or explanatory body) |
Inputs used
| has_llms_txt | no |
| legible_history_share | 0.897 |
| agent_instruction_files | AGENTS.md, CLAUDE.md |
| agent_instruction_max_bytes | 7,654 |
Verify loop (build / test / typecheck)
61Moderate
How it's scored
| 18/18 | One-command bootstrap — Makefile, doc/Makefile, tools/dev/Makefile |
| 22/22 | Automated tests |
| 11/11 | Lint / format config |
| 0/11 | Static type checking |
| 0/10 | Reproducible environment |
| 2/10 | Demonstrated agent practice — 1 of the last 100 commits agent-authored or agent-credited |
| 8/8 | Automated maintenance — 5 of the last 100 commits are automated dependency updates |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Inputs used
| has_nix | no |
| has_tests | yes |
| lockfiles | — |
| has_dockerfile | no |
| typed_language | no |
| bootstrap_files | Makefile, doc/Makefile, tools/dev/Makefile |
| has_devcontainer | no |
| has_linter_config | yes |
| typecheck_configs | — |
| agent_commit_share | 0.01 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0.05 |
Code legibility for models
52Moderate
How it's scored
| 0/45 | Type-checkable code — Python without a type-check config |
| 52/55 | Manageable file sizes — 50/908 source files over 60KB |
Inputs used
| primary_language | Python |
| largest_source_bytes | 219,130 |
| source_files_sampled | 908 |
| oversized_source_files | 50 |
Machine-readable interfaces
40At risk
How it's scored
| 0/40 | API schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP server |
| 40/40 | Runnable examples — examples, sample |
Inputs used
| example_dirs | examples, sample |
| has_mcp_signal | no |
| api_schema_files | — |
Key facts
3,475GitHub stars
97contributors
452commits, last 12 months
0days since last push
64releases
3bus factor
520open 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 ★ / 1,576 ⇿
0Stars
1,576Forks
51Releases
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.
Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.
Major 1Minor 18Patch 32
Each point covers 7 days.
OpenSSF Scorecard 7.8 / 10
7.8aggregate
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).
| 10 | Binary-Artifacts | no binaries found in the repo |
| n/a | Branch-Protection | internal 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 |
| 10 | CI-Tests | 30 out of 30 merged PRs checked by a CI test -- score normalized to 10 |
| 7 | CII-Best-Practices | badge detected: Silver |
| 8 | Code-Review | Found 26/29 approved changesets -- score normalized to 8 |
| 10 | Contributors | project has 61 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 19 issue activity found in the last 90 days -- score normalized to 10 |
| 10 | Packaging | packaging workflow detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 9 | SAST | SAST tool detected but not run on all commits |
| 10 | Security-Policy | security policy file detected |
| n/a | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
Direct dependencies 10
| Registry | Package | Version constraint | Manifest |
|---|---|---|---|
| PyPI | decorator | >= 5.1 | pyproject.toml |
| PyPI | jinja2 | >= 3.1 | pyproject.toml |
| PyPI | lazy_loader | >= 0.3 | pyproject.toml |
| PyPI | matplotlib | >= 3.9 | pyproject.toml |
| PyPI | numpy | >= 2.0, < 3 | pyproject.toml |
| PyPI | packaging | — | pyproject.toml |
| PyPI | pooch | >= 1.5 | pyproject.toml |
| PyPI | scipy | >= 1.14 | pyproject.toml |
| PyPI | tqdm | >= 4.66 | pyproject.toml |
| PyPI | typing-extensions | >= 4.15 | pyproject.toml |
All dependencies 64
Full resolved dependency set from the GitHub dependency graph: 8 direct and 56 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.
| Registry | Package | Version | Relation |
|---|---|---|---|
| PyPI | decorator | — | direct |
| PyPI | jinja2 | — | direct |
| PyPI | lazy-loader | — | direct |
| PyPI | matplotlib | — | direct |
| PyPI | numpy | — | direct |
| PyPI | pooch | — | direct |
| PyPI | scipy | — | direct |
| PyPI | tqdm | — | direct |
| PyPI | antio | — | indirect |
| PyPI | curryreader | — | indirect |
| PyPI | dipy | — | indirect |
| PyPI | docutils | — | indirect |
| PyPI | edfio | — | indirect |
| PyPI | filelock | — | indirect |
| PyPI | h5io | — | indirect |
| PyPI | h5py | — | indirect |
| PyPI | hatchling | — | indirect |
| PyPI | imageio | — | indirect |
| PyPI | imageio-ffmpeg | — | indirect |
| PyPI | intersphinx-registry | — | indirect |
| PyPI | ipython | — | indirect |
| PyPI | joblib | — | indirect |
| PyPI | memory-profiler | — | indirect |
| PyPI | mffpy | — | indirect |
| PyPI | mne-qt-browser | — | indirect |
| PyPI | nibabel | — | indirect |
| PyPI | nilearn | — | indirect |
| PyPI | nitime | — | indirect |
| PyPI | numba | — | indirect |
| PyPI | numpydoc | — | indirect |
| PyPI | openmeeg | — | indirect |
| PyPI | openneuro-py | — | indirect |
| PyPI | pandas | — | indirect |
| PyPI | pillow | — | indirect |
| PyPI | pip | — | indirect |
| PyPI | pydata-sphinx-theme | — | indirect |
| PyPI | pygments | — | indirect |
| PyPI | pyobjc-framework-cocoa | — | indirect |
| PyPI | pyqt6 | — | indirect |
| PyPI | pyqt6-qt6 | — | indirect |
| PyPI | pyside6 | — | indirect |
| PyPI | pytest | — | indirect |
| PyPI | pytest-cov | — | indirect |
| PyPI | pytest-qt | — | indirect |
| PyPI | pytest-timeout | — | indirect |
| PyPI | pytest-xdist | — | indirect |
| PyPI | python-picard | — | indirect |
| PyPI | pyvista | — | indirect |
| PyPI | pyvistaqt | — | indirect |
| PyPI | pyzmq | — | indirect |
| PyPI | qdarkstyle | — | indirect |
| PyPI | rcssmin | — | indirect |
| PyPI | refleak | — | indirect |
| PyPI | ruff | — | indirect |
| PyPI | scikit-learn | — | indirect |
| PyPI | seaborn | — | indirect |
| PyPI | selenium | — | indirect |
| PyPI | sphinx | — | indirect |
| PyPI | sphinx-gallery | — | indirect |
| PyPI | sphinxcontrib-bibtex | — | indirect |
| PyPI | sphinxcontrib-towncrier | — | indirect |
| PyPI | statsmodels | — | indirect |
| PyPI | trame-vuetify | — | indirect |
| PyPI | vtk | — | indirect |
Dependency advisories 0
Installing pypi:mne@1.12.1 pulls in 24 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
{
"data": {
"repo": {
"topics": [
"python",
"neuroscience",
"electroencephalography",
"magnetoencephalography",
"electrocorticography",
"machine-learning",
"statistics",
"visualization",
"eeg",
"meg",
"ecog",
"neuroimaging"
],
"is_fork": false,
"size_kb": 171078,
"has_wiki": true,
"homepage": "https://mne.tools",
"languages": {
"jq": 1060,
"CSS": 4483,
"Jinja": 25368,
"Shell": 32506,
"Python": 12056855,
"Makefile": 3401,
"JavaScript": 10004,
"Csound Document": 24999
},
"pushed_at": "2026-07-30T21:01:09Z",
"created_at": "2011-01-28T03:31:13Z",
"owner_type": "Organization",
"updated_at": "2026-07-30T21:01:41Z",
"description": "MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python",
"is_archived": false,
"is_disabled": false,
"license_spdx": "BSD-3-Clause",
"default_branch": "main",
"license_spdx_raw": "BSD-3-Clause",
"primary_language": "Python",
"significant_languages": [
"Python"
]
},
"owner": {
"blog": "http://mne.tools",
"name": "MNE tools for MEG and EEG data analysis",
"type": "Organization",
"login": "mne-tools",
"company": null,
"location": null,
"followers": 312,
"avatar_url": "https://avatars.githubusercontent.com/u/583875?v=4",
"created_at": "2011-01-26T02:24:38Z",
"is_verified": null,
"public_repos": 49,
"account_age_days": 5664
},
"license": {
"state": "standard",
"spdx_id": "BSD-3-Clause",
"raw_spdx": "BSD-3-Clause",
"file_present": true,
"scorecard_found": true,
"profile_has_license": true
},
"activity": {
"releases": [
{
"tag": "v1.12.1",
"kind": "patch",
"published_at": "2026-04-20T17:07:30Z"
},
{
"tag": "v1.12.0",
"kind": "minor",
"published_at": "2026-04-07T19:35:14Z"
},
{
"tag": "v1.11.0",
"kind": "minor",
"published_at": "2025-11-21T19:34:02Z"
},
{
"tag": "v1.10.2",
"kind": "patch",
"published_at": "2025-10-14T00:15:25Z"
},
{
"tag": "v1.10.1",
"kind": "patch",
"published_at": "2025-08-11T14:16:42Z"
},
{
"tag": "v1.10.0",
"kind": "minor",
"published_at": "2025-07-15T16:36:20Z"
},
{
"tag": "v1.9.0",
"kind": "minor",
"published_at": "2024-12-18T21:22:26Z"
},
{
"tag": "v1.8.0",
"kind": "minor",
"published_at": "2024-08-19T02:12:47Z"
},
{
"tag": "v1.7.1",
"kind": "patch",
"published_at": "2024-06-14T22:41:47Z"
},
{
"tag": "v1.7.0",
"kind": "minor",
"published_at": "2024-04-19T18:02:55Z"
},
{
"tag": "v1.6.1",
"kind": "patch",
"published_at": "2024-01-16T18:37:42Z"
},
{
"tag": "v1.6.0",
"kind": "minor",
"published_at": "2023-11-20T20:23:33Z"
},
{
"tag": "v1.5.1",
"kind": "patch",
"published_at": "2023-09-06T17:49:45Z"
},
{
"tag": "v1.5.0",
"kind": "minor",
"published_at": "2023-08-15T17:51:10Z"
},
{
"tag": "v1.4.2",
"kind": "patch",
"published_at": "2023-06-06T14:43:08Z"
},
{
"tag": "v1.4.1",
"kind": "patch",
"published_at": "2023-06-05T16:27:52Z"
},
{
"tag": "v1.4.0",
"kind": "minor",
"published_at": "2023-05-10T15:02:48Z"
},
{
"tag": "v1.3.1",
"kind": "patch",
"published_at": "2023-02-23T20:18:44Z"
},
{
"tag": "v1.3.0",
"kind": "minor",
"published_at": "2022-12-22T16:20:38Z"
},
{
"tag": "v1.2.3",
"kind": "patch",
"published_at": "2022-12-02T21:34:21Z"
},
{
"tag": "v1.2.2",
"kind": "patch",
"published_at": "2022-11-11T17:04:40Z"
},
{
"tag": "v1.2.1",
"kind": "patch",
"published_at": "2022-10-19T22:31:10Z"
},
{
"tag": "v1.2.0",
"kind": "minor",
"published_at": "2022-10-11T19:32:02Z"
},
{
"tag": "v1.1.1",
"kind": "patch",
"published_at": "2022-08-24T15:27:56Z"
},
{
"tag": "v1.1.0",
"kind": "minor",
"published_at": "2022-08-03T15:58:33Z"
},
{
"tag": "v1.0.3",
"kind": "patch",
"published_at": "2022-05-12T13:39:57Z"
},
{
"tag": "v1.0.2",
"kind": "patch",
"published_at": "2022-04-15T15:00:16Z"
},
{
"tag": "v1.0.0",
"kind": "major",
"published_at": "2022-03-21T14:31:33Z"
},
{
"tag": "v0.24.1",
"kind": "patch",
"published_at": "2021-12-01T22:25:51Z"
},
{
"tag": "v0.24.0",
"kind": "minor",
"published_at": "2021-11-03T15:12:33Z"
},
{
"tag": "v0.23.4",
"kind": "patch",
"published_at": "2021-09-10T16:09:34Z"
},
{
"tag": "v0.23.3",
"kind": "patch",
"published_at": "2021-08-30T15:35:55Z"
},
{
"tag": "v0.23.2",
"kind": "patch",
"published_at": "2021-08-26T13:47:28Z"
},
{
"tag": "v0.23.1",
"kind": "patch",
"published_at": "2021-08-24T14:56:39Z"
},
{
"tag": "v0.23.0",
"kind": "minor",
"published_at": "2021-04-27T18:57:09Z"
},
{
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],
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"note": null,
"notes": [],
"value": 92,
"inputs": {
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},
{
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}
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{
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{
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"note": null,
"notes": [],
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}
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{
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]
},
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{
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{
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]
},
{
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{
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{
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{
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{
"key": "track_record",
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"status": "partial",
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{
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{
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{
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],
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{
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{
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{
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"status": "met",
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},
{
"key": "fuzzing",
"name": "Fuzzing",
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"status": "missed",
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},
{
"key": "license",
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{
"key": "maintained",
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"points": 7.5,
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{
"key": "packaging",
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"points": 5,
"status": "met",
"details": [],
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},
{
"key": "pinned_dependencies",
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"points": 0,
"status": "missed",
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{
"key": "sast",
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"status": "partial",
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},
{
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"details": [],
"max_points": 5
},
{
"key": "signed_releases",
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"status": "excluded",
"details": [
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"code": "no_data",
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],
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{
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"points": 0,
"status": "missed",
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{
"key": "vulnerabilities",
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"status": "met",
"details": [],
"max_points": 7.5
}
]
},
{
"key": "dependency_advisories",
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"note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:mne@1.12.1 runtime dependency closure — what installing the published package pulls in — 24 packages. Reachability is not analyzed.",
"notes": [
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"code": "excluded_no_data",
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{
"code": "weights_renormalized",
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{
"code": "advisories_scope_published",
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},
{
"code": "advisories_reachability",
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],
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"inputs": {
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"affected_by_severity": "none",
"direct_affected_packages": 0
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"components": [
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"name": "Direct dependencies free of known advisories",
"detail": "no direct dependency carries a known advisory",
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],
"max_points": 35
},
{
"key": "indirect_dependencies_free_of_known_advisories",
"name": "Indirect dependencies free of known advisories",
"detail": "no indirect dependency carries a known advisory",
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"status": "met",
"details": [
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"code": "no_indirect_advisories",
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}
],
"max_points": 25
},
{
"key": "no_advisories_left_outstanding",
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"detail": "no advisory carries a publication date",
"points": 0,
"status": "excluded",
"details": [
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"code": "advisories_no_publication_date",
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],
"max_points": 40
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]
},
{
"key": "malicious_dependencies",
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"name": "Malicious dependencies",
"note": null,
"notes": [],
"value": 100,
"inputs": {
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"direct_malicious_packages": 0,
"withdrawn_malicious_packages": 0,
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},
"components": [
{
"key": "no_dependency_reported_as_a_malicious_package",
"name": "No dependency reported as a malicious package",
"detail": "no dependency is reported as a malicious package",
"points": 100,
"status": "met",
"details": [
{
"code": "no_malicious_dependencies",
"params": {}
}
],
"max_points": 100
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
"band": "excellent",
"name": "High-Risk Jurisdiction Exposure",
"note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
"notes": [
{
"code": "jurisdiction_evidence_limits",
"params": {}
}
],
"value": 100,
"inputs": {
"meaning": "self-published location evidence; not nationality or citizenship",
"red_flag": false,
"exposures": [],
"policy_countries": [
"Russia",
"Iran",
"North Korea"
],
"review_only_matches": 0,
"assessed_self_published_locations": 14
},
"components": [
{
"key": "policy_exposure_multiplier",
"name": "Policy exposure multiplier",
"detail": "no confirmed policy-scope location match",
"points": 100,
"status": "met",
"details": [
{
"code": "jurisdiction_no_match",
"params": {}
}
],
"max_points": 100
}
]
}
],
"description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
},
{
"key": "ai_readiness",
"band": "moderate",
"name": "AI Readiness",
"value": 64,
"weight": 0,
"metrics": [
{
"key": "ai_agent_context",
"band": "excellent",
"name": "Agent context & guidance",
"note": null,
"notes": [],
"value": 85,
"inputs": {
"has_llms_txt": false,
"legible_history_share": 0.897,
"agent_instruction_files": [
"AGENTS.md",
"CLAUDE.md"
],
"agent_instruction_max_bytes": 7654
},
"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": "AGENTS.md, CLAUDE.md",
"points": 45,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "AGENTS.md, CLAUDE.md"
}
}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
"name": "Legible commit history",
"detail": "78 of 87 human commits state their intent (structured subject or explanatory body)",
"points": 40,
"status": "met",
"details": [
{
"code": "legible_history",
"params": {
"legible": 78,
"sampled": 87
}
}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "moderate",
"name": "Verify loop (build / test / typecheck)",
"note": null,
"notes": [],
"value": 61,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [],
"has_dockerfile": false,
"typed_language": false,
"bootstrap_files": [
"Makefile",
"doc/Makefile",
"tools/dev/Makefile"
],
"has_devcontainer": false,
"has_linter_config": true,
"typecheck_configs": [],
"agent_commit_share": 0.01,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0.05
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": "Makefile, doc/Makefile, tools/dev/Makefile",
"points": 18,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "Makefile, doc/Makefile, tools/dev/Makefile"
}
}
],
"max_points": 18
},
{
"key": "automated_tests",
"name": "Automated tests",
"detail": null,
"points": 22,
"status": "met",
"details": [],
"max_points": 22
},
{
"key": "lint_format_config",
"name": "Lint / format config",
"detail": null,
"points": 11,
"status": "met",
"details": [],
"max_points": 11
},
{
"key": "static_type_checking",
"name": "Static type checking",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 11
},
{
"key": "reproducible_environment",
"name": "Reproducible environment",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
"name": "Demonstrated agent practice",
"detail": "1 of the last 100 commits agent-authored or agent-credited",
"points": 2,
"status": "partial",
"details": [
{
"code": "agent_authored_commits",
"params": {
"count": 1,
"sampled": 100
}
}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "5 of the last 100 commits are automated dependency updates",
"points": 8,
"status": "met",
"details": [
{
"code": "dependency_bot_commits",
"params": {
"count": 5,
"sampled": 100
}
}
],
"max_points": 8
},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
}
]
},
{
"key": "ai_code_legibility",
"band": "moderate",
"name": "Code legibility for models",
"note": null,
"notes": [],
"value": 52,
"inputs": {
"primary_language": "Python",
"largest_source_bytes": 219130,
"source_files_sampled": 908,
"oversized_source_files": 50
},
"components": [
{
"key": "type_checkable_code",
"name": "Type-checkable code",
"detail": "Python without a type-check config",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_typecheck_config_language",
"params": {
"language": "Python"
}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
"name": "Manageable file sizes",
"detail": "50/908 source files over 60KB",
"points": 52,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 908,
"oversized": 50
}
}
],
"max_points": 55
}
]
},
{
"key": "ai_interfaces",
"band": "at_risk",
"name": "Machine-readable interfaces",
"note": null,
"notes": [],
"value": 40,
"inputs": {
"example_dirs": [
"examples",
"sample"
],
"has_mcp_signal": false,
"api_schema_files": []
},
"components": [
{
"key": "api_schema_openapi_graphql_proto",
"name": "API schema (OpenAPI/GraphQL/proto)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 40
},
{
"key": "mcp_server",
"name": "MCP server",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 20
},
{
"key": "runnable_examples",
"name": "Runnable examples",
"detail": "examples, sample",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "examples, sample"
}
}
],
"max_points": 40
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
}
],
"metrics_version": "1.13.0"
},
"warnings": [
"Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token"
],
"report_type": "repository",
"generated_at": "2026-07-30T22:15:03.022222Z",
"schema_version": "0.27.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/m/mne-tools/mne-python.svg",
"full_name": "mne-tools/mne-python",
"license_state": "standard",
"license_spdx": "BSD-3-Clause"
}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 v1.13.0, schema v0.27.0 — full methodology · metrics wiki.
How one result sits in the wider record: aggregate statistics — PyPI.