Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-02 05:10 UTC

jupyterlab / jupyter-ai

An open source extension that connects AI agents to computational notebooks in JupyterLab.

Python · TypeScriptBSD-3-Clause★ 4,388 stars⑂ 531 forkssince Feb 2023View on GitHub ↗
KindPluginhow this is determined
#jupyter#jupyterlab#jupyterlab-extension#acp#agents

jupyterlab/jupyter-ai holds a health index of 95 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (92/100) and lowest on AI Readiness (66/100). It was last updated 3 days ago. 3 contributors account for most of its recent work.

95
overall / 100
Exceptional

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.

95
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 82 is calibrated to 95 on the published index scale (record calibration 2026-08-02).

Ownership

JupyterLabOrganization · verified domain
1,607 followers67 public repossince Oct 2016

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIjupyter_ai3.1.3-11819 days agojupyterjupyter-aijupyterlabjupyterlab-extension

Metrics by category

Vitality

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

83Excellent · 21% of overall

Development activity

79Good
How it's scored
36/36Push recency — last push 3 days ago
15.2/36Commit cadence — 22/52 weeks with commits
18/18Commit volume — 109 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 23 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year109
human_commit_share0.73
days_since_last_push3
active_weeks_last_year22

Release discipline

90Excellent
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 5 days ago
27/27Release cadence — a release every ~6.3 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count100
latest_release_tagv3.2.0rc0
releases_from_tagsno
days_since_latest_release5
mean_days_between_releases6.3

Community & Adoption

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

82Excellent · 17% of overall

Popularity & adoption

91Excellent
How it's scored
59.1/60Stars — 4,388 stars
22.7/25Forks — 531 forks
9.3/15Watchers — 48 watchers
Inputs used
forks531
stars4,388
watchers48
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

72Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (BSD-3-Clause)
0/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingno
has_issue_templateno
has_code_of_conductyes
readme_badge_services—
has_pull_request_templateyes

Sustainability & Governance

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

83Excellent · 23% of overall

Maintainer resilience (bus factor)

74Good
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
14.5/22.5Commit distribution — top contributor authored 36% of commits
13.5/13.5Contributor breadth — 62 contributors
10/10OpenSSF Scorecard: Contributors — project has 39 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled62
top_contributor_share0.356

Issue & PR responsiveness

65Good
How it's scored
27/42Issue resolution — 64% of issues closed
27/30PR acceptance — 718/798 decided PRs merged
9.8/13Newcomer PR acceptance — 3/4 first-time contributors' PRs merged in 30d
1.5/15OpenSSF Scorecard: Code-Review — Found 3/29 approved changesets -- score normalized to 1
Inputs used
merged_prs718
open_issues287
closed_issues515
prs_merged_7d2
prs_decided_7d3
prs_merged_30d9
prs_decided_30d11
issue_closed_ratio0.642
closed_unmerged_prs80
first_time_authors_30d4
first_time_prs_merged_30d3
first_time_prs_decided_30d4

Ownership & stewardship

98Exceptional
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
23.1/25Owner reach — 1,607 followers of jupyterlab
25/25Track record — 67 public repos, account ~9 yr old
Inputs used
followers1,607
owner_typeOrganization
is_verifiedyes
owner_loginjupyterlab
public_repos67
account_age_days3,611

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 19 days ago
20/20Version history — 118 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesjupyter_ai
ecosystemspypi
any_deprecatedno
min_days_since_publish19

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 8 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 10 out of 10 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://jupyter-ai.readthedocs.io/
10/10Repository description
10/10Topics — 5 topics
0/10Wiki
Inputs used
topicsjupyter, jupyterlab, jupyterlab-extension, acp, agents
has_wikino
homepagehttps://jupyter-ai.readthedocs.io/
docs_sitehttps://jupyter-ai.readthedocs.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

69Good · 16% of overall

Security posture

61Moderate
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 — 10 out of 10 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 3/29 approved changesets -- score normalized to 1
2.5/2.5Contributors — project has 39 contributing companies or organizations
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
7.5/7.5Maintained — 30 commit(s) and 23 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
3/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 6
5/5SAST — SAST tool is run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
6/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
4.5/7.5Vulnerabilities — 4 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.1
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging. 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_packages164
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:jupyter_ai@3.1.3 runtime dependency closure — what installing the published package pulls in — 164 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.

66Good · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 70 of 73 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.959
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes8,881

Verify loop (build / test / typecheck)

67Good
How it's scored
18/18One-command bootstrap — docs/Makefile, noxfile.py
22/22Automated tests
11/11Lint / format config
0/11Static type checking
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
6/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 6
Inputs used
has_nixno
has_testsyes
lockfilesyarn.lock
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile, noxfile.py
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
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 — 0/18 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes27,379
source_files_sampled18
oversized_source_files0

Machine-readable interfaces

33At Risk
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
20/20MCP server
0/40Runnable examples
Inputs used
example_dirs—
has_mcp_signalyes
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto). Remaining weights renormalized.

Key facts

4,388GitHub stars
62contributors
109commits, last 12 months
3days since last push
100releases
3bus factor
287open issues
npm, PyPIpackage ecosystems

Data collection warnings

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

More detail

Star and fork history 0 ★ / 531 ⇿
0Stars
531Forks
100Releases

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.

0100200300400500600527392023-022024-112026-08
Major 1Minor 47Patch 30

Each point covers 4 days.

OpenSSF Scorecard 6.1 / 10
6.1aggregate

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 · 2026-09-02 05:10 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-Tests10 out of 10 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 3/29 approved changesets -- score normalized to 1
10Contributorsproject has 39 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 23 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
6Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 6
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
8Token-Permissionsdetected GitHub workflow tokens with excessive permissions
6Vulnerabilities4 existing vulnerabilities detected
Direct dependencies 9
RegistryPackageVersion constraintManifest
PyPIjupyterlab_chat>=0.25.0rc0,<0.26.0pyproject.toml
PyPIjupyter_ai_router>=0.1.0rc0,<0.2.0pyproject.toml
PyPIjupyter_ai_persona_manager>=0.2.0rc0,<0.3.0pyproject.toml
PyPIjupyter_ai_chat_commands>=0.0.4,<0.1.0pyproject.toml
PyPIjupyter_ai_acp_client>=0.3.0rc0,<0.4.0pyproject.toml
PyPIjupyter_server_mcp>=0.3.0rc0,<0.4.0pyproject.toml
PyPIjupyter_ai_tools>=0.7.0rc0,<0.8.0pyproject.toml
PyPIjupyterlab_commands_toolkit>=0.2.0rc0,<0.3.0pyproject.toml
PyPIjupyter_live_content>=0.1.1,<0.2.0pyproject.toml
All dependencies 428

Full resolved dependency set from the GitHub dependency graph: 9 direct and 419 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIjupyter-ai-acp-client—direct
PyPIjupyter-ai-chat-commands—direct
PyPIjupyter-ai-persona-manager—direct
PyPIjupyter-ai-router—direct
PyPIjupyter-ai-tools—direct
PyPIjupyter-live-content—direct
PyPIjupyter-server-mcp—direct
PyPIjupyterlab-chat—direct
PyPIjupyterlab-commands-toolkit—direct
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npmd3-polygon3.0.1indirect
npmd3-quadtree3.0.1indirect
npmd3-random3.0.1indirect
npmd3-sankey0.12.3indirect
npmd3-scale4.0.2indirect
npmd3-scale-chromatic3.1.0indirect
npmd3-selection3.0.0indirect
npmd3-shape1.3.7indirect
npmd3-shape3.2.0indirect
npmd3-time3.1.0indirect
npmd3-time-format4.1.0indirect
npmd3-timer3.0.1indirect
npmd3-transition3.0.1indirect
npmd3-zoom3.0.0indirect
npmdagre-d3-es7.0.14indirect
npmdayjs1.11.23indirect
npmdebug2.6.9indirect
npmdeepmerge4.3.1indirect
npmdelaunator5.1.0indirect
npmdom-serializer2.0.0indirect
npmdomelementtype2.3.0indirect
npmdomhandler5.0.3indirect
npmdompurify3.4.14indirect
npmdomutils3.2.2indirect
npmelkjs0.9.3indirect
npmemoji-regex8.0.0indirect
npmentities4.5.0indirect
npmenv-paths2.2.1indirect
npmes-toolkit1.51.0indirect
npmescalade3.2.0indirect
npmescape-string-regexp4.0.0indirect
npmexenv-es61.1.1indirect
npmexponential-backoff3.1.3indirect
npmfast-deep-equal3.1.3indirect
npmfast-uri3.1.6indirect
npmfastdom1.0.12indirect
npmfdir6.5.0indirect
npmfree-style3.1.0indirect
npmfs-extra10.1.0indirect
npmfsevents2.3.2indirect
npmget-caller-file2.0.5indirect
npmgithub-slugger2.0.0indirect
npmgraceful-fs4.2.11indirect
npmhachure-fill0.5.2indirect
npmhtmlparser28.0.2indirect
npmiconv-lite0.6.3indirect
npmimport-meta-resolve4.2.0indirect
npminherits2.0.3indirect
npminherits2.0.4indirect
npminternmap1.0.1indirect
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npmis-fullwidth-code-point3.0.0indirect
npmis-plain-object5.1.0indirect
npmisarray1.0.0indirect
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npmisomorphic.js0.2.5indirect
npmjs-tokens4.0.0indirect
npmjson-schema-compare0.2.2indirect
npmjson-schema-merge-allof0.8.1indirect
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npmjson-stringify-pretty-compact4.0.0indirect
npmjson52.2.3indirect
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npmkatex0.16.47indirect
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npmlib00.2.117indirect
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npmlodash.escape4.0.1indirect
npmlodash.mergewith4.6.2indirect
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npmmarkdown-to-jsx7.7.17indirect
npmmarked16.4.2indirect
npmmarked17.0.6indirect
npmmarked-gfm-heading-id4.1.4indirect
npmmarked-mangle1.1.13indirect
npmmermaid11.17.2indirect
npmminimist1.2.8indirect
npmminipass7.1.3indirect
npmminizlib3.1.0indirect
npmms2.0.0indirect
npmnanoid3.3.18indirect
npmnode-fetch2.7.0indirect
npmnode-gyp13.0.2indirect
npmnopt10.0.1indirect
npmobject-assign4.1.1indirect
npmpackage-manager-detector1.8.0indirect
npmparse-srcset1.0.2indirect
npmpath0.12.7indirect
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npmpath-data-parser0.1.0indirect
npmpicocolors1.1.1indirect
npmpicomatch4.0.7indirect
npmplaywright1.62.1indirect
npmplaywright-core1.62.1indirect
npmpoints-on-curve0.2.0indirect
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npmpostcss8.5.26indirect
npmproc-log7.0.0indirect
npmprocess0.11.10indirect
npmprocess-nextick-args2.0.1indirect
npmprop-types15.8.1indirect
npmquerystringify2.2.0indirect
npmreact18.3.1indirect
npmreact-dom18.3.1indirect
npmreact-is16.13.1indirect
npmreact-is18.3.1indirect
npmreadable-stream2.3.8indirect
npmregexp-match-indices1.0.2indirect
npmregexp-tree0.1.27indirect
npmrequire-directory2.1.1indirect
npmrequire-from-string2.0.2indirect
npmrequires-port1.0.0indirect
npmrobust-predicates3.0.3indirect
npmroughjs4.6.6indirect
npmrw1.3.3indirect
npmsafe-buffer5.1.2indirect
npmsafer-buffer2.1.2indirect
npmsanitize-html2.12.1indirect
npmscheduler0.23.2indirect
npmsemver7.8.5indirect
npmsource-map-js1.2.1indirect
npmstrictdom1.0.1indirect
npmstring-width4.2.3indirect
npmstring_decoder1.1.1indirect
npmstrip-ansi6.0.1indirect
npmstyle-mod4.1.3indirect
npmstylis4.4.0indirect
npmtabbable5.3.3indirect
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npmtinyexec1.3.0indirect
npmtinyglobby0.2.17indirect
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npmts-dedent2.3.0indirect
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npmtypestyle2.4.0indirect
npmundici8.10.0indirect
npmuniversalify2.0.1indirect
npmurl-parse1.5.10indirect
npmutil0.10.4indirect
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npmuuid14.0.2indirect
npmvalidate.io-array1.0.6indirect
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npmvalidate.io-number1.0.3indirect
npmvega5.33.1indirect
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npmvega-crossfilter4.1.4indirect
npmvega-dataflow5.7.8indirect
npmvega-encode4.10.3indirect
npmvega-event-selector3.0.1indirect
npmvega-expression5.1.2indirect
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npmvega-force4.2.3indirect
npmvega-format1.1.4indirect
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npmvega-hierarchy4.1.4indirect
npmvega-label1.3.2indirect
npmvega-lite5.23.0indirect
npmvega-loader4.5.4indirect
npmvega-parser6.6.1indirect
npmvega-projection1.6.3indirect
npmvega-regression1.3.2indirect
npmvega-runtime6.2.2indirect
npmvega-scale7.4.3indirect
npmvega-scenegraph4.13.2indirect
npmvega-selections5.6.3indirect
npmvega-statistics1.9.0indirect
npmvega-time2.1.4indirect
npmvega-transforms4.12.2indirect
npmvega-typings1.5.1indirect
npmvega-util1.17.4indirect
npmvega-view5.16.1indirect
npmvega-view-transforms4.6.2indirect
npmvega-voronoi4.2.5indirect
npmvega-wordcloud4.1.7indirect
npmvscode-jsonrpc8.2.1indirect
npmvscode-jsonrpc9.0.1indirect
npmvscode-languageserver-protocol3.18.2indirect
npmvscode-languageserver-types3.18.0indirect
npmvscode-ws-jsonrpc1.0.2indirect
npmw3c-keyname2.2.8indirect
npmwebidl-conversions3.0.1indirect
npmwhatwg-url5.0.0indirect
npmwhich7.0.0indirect
npmwrap-ansi7.0.0indirect
npmws8.21.3indirect
npmy-protocols1.0.7indirect
npmy18n5.0.8indirect
npmyallist5.0.0indirect
npmyargs17.7.3indirect
npmyargs-parser21.1.1indirect
npmyjs13.6.32indirect
PyPIhatchling—indirect
PyPIjupyter-ai-jupyternaut—indirect
PyPIjupyter-ai-litellm—indirect
PyPIjupyter-ai-magic-commands—indirect
PyPIjupyter-collaboration—indirect
PyPIjupyter-server-documents—indirect
PyPIjupyterlab-notebook-awareness—indirect
Dependency advisories 0

Installing pypi:jupyter_ai@3.1.3 pulls in 164 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

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93jupyterlite/jupyterliteWasm powered Jupyter running in the browser 💡Exceptional93jupyterlab/jupyter-collaborationA Jupyter Server Extension Providing Support for Y DocumentsExceptional91jupyterlab/jupyter-chatAn extension to add a chat panel to JupyterLabExcellent89materialsproject/crystaltoolkitCrystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials Project website.Excellent89jupyterhub/jupyter-server-proxyJupyter notebook server extension to proxy web services.Excellent87mito-ds/mitoJupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and SpreadsheetExcellent

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.