Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 15:57 UTC

plasma-umass / scalene

Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals

Python · JavaScriptApache-2.0★ 13,507 stars⑂ 437 forkssince Dec 2019View on GitHub ↗
KindCommand-line toolLibraryhow this is determined
#python#profiling#performance-analysis#cpu-profiling#profiler#python-profilers#gpu-programming#scalene#profiles-memory#performance-cpu#cpu#memory-allocation#gpu#memory-consumption

plasma-umass/scalene holds a health index of 90 out of 100, placing it in the Excellent band. It scores highest on AI Readiness (90/100) and lowest on Security (59/100). It was last updated 20 days ago. A single contributor accounts for most of its recent work.

90
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.

90
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 76 is calibrated to 90 on the published index scale (record calibration 2026-08-02).

Ownership

PLASMA @ UMassOrganization
343 followers111 public repossince Jun 2012

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIscalene2.3.0387,476190127 days agoperformanceprofileroptimizationcpugpumemoryllm

Metrics by category

Vitality

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

82Excellent · 21% of overall

Development activity

77Good
How it's scored
28.8/36Push recency — last push 20 days ago
20.1/36Commit cadence — 29/52 weeks with commits
18/18Commit volume — 232 commits in the last year
10/10OpenSSF Scorecard: Maintained — 29 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year232
human_commit_share1
days_since_last_push20
active_weeks_last_year29

Release discipline

90Excellent
How it's scored
27/27Ships releases — 90 releases published
27/36Release recency — latest release 127 days ago
27/27Release cadence — a release every ~25.7 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count90
latest_release_tagv2.3.0
releases_from_tagsno
days_since_latest_release127
mean_days_between_releases25.7
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?

83Excellent · 17% of overall

Popularity & adoption

93Exceptional
How it's scored
60/60Stars — 13,507 stars
22/25Forks — 437 forks
10.7/15Watchers — 85 watchers
Inputs used
forks437
stars13,507
watchers85
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

65Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
0/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges7
has_contributingno
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

93Exceptional
How it's scored
74.5/80Monthly downloads — 387,476 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesscalene
dependents—
ecosystemspypi
total_downloads—
monthly_downloads387,476
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?

67Good · 23% of overall

Maintainer resilience (bus factor)

36Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
4/22.5Commit distribution — top contributor authored 82% of commits
13.5/13.5Contributor breadth — 53 contributors
10/10OpenSSF Scorecard: Contributors — project has 8 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled53
top_contributor_share0.824

Issue & PR responsiveness

70Good
How it's scored
30/42Issue resolution — 71% of issues closed
26.9/30PR acceptance — 438/489 decided PRs merged
13/13Newcomer PR acceptance — 1/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — Found 1/30 approved changesets -- score normalized to 0
Inputs used
merged_prs438
open_issues151
closed_issues377
prs_merged_7d0
prs_decided_7d0
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio0.714
closed_unmerged_prs51
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1

Ownership & stewardship

73Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
18.2/25Owner reach — 343 followers of plasma-umass
25/25Track record — 111 public repos, account ~14 yr old
Inputs used
followers343
owner_typeOrganization
is_verifiedno
owner_loginplasma-umass
public_repos111
account_age_days5,200

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 127 days ago
20/20Version history — 190 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesscalene
ecosystemspypi
any_deprecatedno
min_days_since_publish127

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall

Engineering practices

84Excellent
How it's scored
24/24CI workflows — 6 workflow(s)
24/24Tests present
16/16Linter config — ruff.toml
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 28 out of 28 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

85Excellent
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 14 topics
10/10Wiki
Inputs used
topicspython, profiling, performance-analysis, cpu-profiling, profiler, python-profilers, gpu-programming, scalene, profiles-memory, performance-cpu, cpu, memory-allocation, gpu, memory-consumption
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

59Moderate · 16% of overall

Security posture

49Weak
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
2.5/2.5CI-Tests — 28 out of 28 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
0/7.5Code-Review — Found 1/30 approved changesets -- score normalized to 0
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
7.5/7.5Maintained — 29 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
5/5SAST — SAST tool is run on all commits
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 — 21 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.9
Excluded from scoring (no data or not applicable): Packaging, 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_packages16
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:scalene@2.3.0 runtime dependency closure — what installing the published package pulls in — 16 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.

90Excellent · 4% of overall

Agent context & guidance

99Exceptional
How it's scored
45/45Agent instructions — CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://code.visualstudio.com/llms.txt)
38.9/40Legible commit history — 73 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://code.visualstudio.com/llms.txt
legible_history_share0.73
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes23,506

Verify loop (build / test / typecheck)

82Excellent
How it's scored
18/18One-command bootstrap — GNUmakefile, Makefile
22/22Automated tests
11/11Lint / format config — ruff.toml
11/11Static type checking — mypy.ini, pyrightconfig.json, scalene/scalene-gui/tsconfig.json
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 48 of the last 100 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
lockfilesPipfile.lock, package-lock.json
has_dockerfileno
typed_languageno
bootstrap_filesGNUmakefile, Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsmypy.ini, pyrightconfig.json, scalene/scalene-gui/tsconfig.json
agent_commit_share0.48
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

81Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (mypy.ini, pyrightconfig.json, scalene/scalene-gui/tsconfig.json)
53.6/55Manageable file sizes — 9/352 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes1,142,293
source_files_sampled352
oversized_source_files9

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

13,507GitHub stars
53contributors
232commits, last 12 months
20days since last push
90releases
1bus factor
151open 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 ★ / 437 ⇿
0Stars
437Forks
90Releases

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.

0100200300400500423352020-012023-052026-09
Major 1Minor 5Patch 70

Each point covers 7 days.

OpenSSF Scorecard 4.9 / 10
4.9aggregate

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 · 2026-09-17 15:57 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
10CI-Tests28 out of 28 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 1/30 approved changesets -- score normalized to 0
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
10Maintained29 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
10SASTSAST tool is run on all commits
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities21 existing vulnerabilities detected
Direct dependencies 10
RegistryPackageVersion constraintManifest
PyPIrich>=10.7.0pyproject.toml
PyPIcloudpickle>=2.2.1pyproject.toml
PyPInvidia-ml-py>=12.555.43pyproject.toml
PyPIJinja2>=3.0.3pyproject.toml
PyPIpsutil>=5.9.2pyproject.toml
PyPInumpy>=1.24.0,!=1.27pyproject.toml
PyPInumpy>=2.3.4pyproject.toml
PyPIastunparse>=1.6.3pyproject.toml
PyPIpydantic>=2.6pyproject.toml
PyPIpyyaml>=6.0pyproject.toml
All dependencies 268

Full resolved dependency set from the GitHub dependency graph: 14 direct and 254 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIjinja2—direct
PyPInumpy—direct
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PyPInvidia-ml-py12.535.77direct
PyPIpsutil—direct
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PyPIpydantic—direct
PyPIpyyaml—direct
PyPIrich—direct
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npmvega-interpreter1.2.0indirect
npmvega-label1.3.1indirect
npmvega-lite5.23.0indirect
npmvega-loader4.5.3indirect
npmvega-parser6.6.0indirect
npmvega-projection1.6.2indirect
npmvega-regression1.3.1indirect
npmvega-runtime6.2.1indirect
npmvega-scale7.4.2indirect
npmvega-scenegraph4.13.1indirect
npmvega-schema-url-parser2.2.0indirect
npmvega-selections5.6.0indirect
npmvega-statistics1.9.0indirect
npmvega-themes2.15.0indirect
npmvega-time2.1.3indirect
npmvega-tooltip0.35.2indirect
npmvega-transforms4.12.1indirect
npmvega-typings1.5.0indirect
npmvega-util1.17.3indirect
npmvega-view5.16.0indirect
npmvega-view-transforms4.6.1indirect
npmvega-voronoi4.2.4indirect
npmvega-wordcloud4.1.6indirect
npmwebidl-conversions3.0.1indirect
npmwhatwg-url5.0.0indirect
npmwrap-ansi7.0.0indirect
npmy18n5.0.8indirect
npmyargs17.7.2indirect
npmyargs-parser21.1.1indirect
PyPIcython—indirect
PyPIhypothesis—indirect
PyPIimportlib-metadata—indirect
PyPIimportlib-resources—indirect
PyPIiniconfig2.0.0indirect
PyPIipython—indirect
PyPIjaraco-text—indirect
PyPIlxml—indirect
PyPImarkdown-it-py3.0.0indirect
PyPImdurl0.1.2indirect
PyPImore-itertools—indirect
PyPIordered-set—indirect
PyPIpackaging—indirect
PyPIpackaging23.1indirect
PyPIplatformdirs—indirect
PyPIpluggy1.2.0indirect
PyPIpygments2.16.1indirect
PyPIpyperf—indirect
PyPIpyperf2.6.1indirect
PyPIpytest7.4.0indirect
PyPIpytest-asyncio—indirect
PyPIsetuptools—indirect
PyPIsetuptools-scm—indirect
PyPItomli—indirect
PyPIwheel—indirect
PyPIwheel0.41.1indirect
Dependency advisories 0

Installing pypi:scalene@2.3.0 pulls in 16 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.

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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.