Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-14 05:59 UTC

CompOmics / ms2pip

MS²PIP: Fast and accurate peptide spectrum prediction for multiple fragmentation methods, instruments, and labeling techniques.

HTML · Jupyter NotebookApache-2.0★ 50 stars⑂ 19 forkssince Mar 2017View on GitHub ↗

KindCommand-line toolLibraryhow this is determined

#machine-learning #mass-spectrometry #peptide-identification #proteomics #spectrum-prediction #peptide-spectrum

CompOmics/ms2pip holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Engineering Quality (86/100) and lowest on AI Readiness (50/100). It was last updated 62 days ago. A single contributor accounts for most of its recent work.

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.

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)

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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

Computational Omics and Systems Biology GroupOrganization

132 followers117 public repossince Mar 2015

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	ms2pip	4.2.0	23,703	38	62 days ago	ms2pip proteomics peptide-fragmentation peak-intensity-prediction spectrum-prediction machine-learning spectral-library fasta2speclib

Metrics by category

Vitality

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

65Good · 21% of overall

Development activity

53Moderate

How it's scored

18/36	Push recency — last push 62 days ago
8.3/36	Commit cadence — 12/52 weeks with commits
17/18	Commit volume — 78 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 19 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	78
human_commit_share	1
days_since_last_push	62
active_weeks_last_year	12

Release discipline

84Excellent

How it's scored

27/27	Ships releases — 26 releases published
36/36	Release recency — latest release 62 days ago
12.6/27	Release cadence — a release every ~139.8 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	26
latest_release_tag	v4.2.0
releases_from_tags	no
days_since_latest_release	62
mean_days_between_releases	139.8

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?

54Moderate · 17% of overall

Popularity & adoption

45Weak

How it's scored

27.4/60	Stars — 50 stars
10.5/25	Forks — 19 forks
6.7/15	Watchers — 17 watchers

Inputs used

forks	19
stars	50
watchers	17
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

50Moderate

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (Apache-2.0)
0/18	CONTRIBUTING guide
0/13.5	Code of conduct
0/7.2	Issue template
0/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	8
has_contributing	no
has_issue_template	no
has_code_of_conduct	no
readme_badge_services	shields.io
has_pull_request_template	no

Ecosystem adoption (downloads)

73Good

How it's scored

58.3/80	Monthly downloads — 23,703 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem

Inputs used

packages	ms2pip
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	23,703
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?

68Good · 23% of overall

Maintainer resilience (bus factor)

40Weak

How it's scored

9/54	Bus factor — 1 contributor(s) cover half of all commits
7.2/22.5	Commit distribution — top contributor authored 68% of commits
13.5/13.5	Contributor breadth — 11 contributors
10/10	OpenSSF Scorecard: Contributors — project has 5 contributing companies or organizations

Inputs used

bus_factor	1
contributors_sampled	11
top_contributor_share	0.682

Issue & PR responsiveness

75Good

How it's scored

40/42	Issue resolution — 95% of issues closed
23.6/30	PR acceptance — 144/183 decided PRs merged
0/13	Newcomer PR acceptance — no first-time contributor's PR decided in 30d
1.5/15	OpenSSF Scorecard: Code-Review — Found 1/10 approved changesets -- score normalized to 1

Inputs used

merged_prs	144
open_issues	4
closed_issues	82
prs_merged_7d	0
prs_decided_7d	0
prs_merged_30d	0
prs_decided_30d	0
issue_closed_ratio	0.953
closed_unmerged_prs	39
first_time_authors_30d	0
first_time_prs_merged_30d	0
first_time_prs_decided_30d	0

Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

70Good

How it's scored

30/30	Ownership backing — organization-owned
0/20	Verified domain
15.3/25	Owner reach — 132 followers of CompOmics
25/25	Track record — 117 public repos, account ~11 yr old

Inputs used

followers	132
owner_type	Organization
is_verified	no
owner_login	CompOmics
public_repos	117
account_age_days	4,191

Package maintenance

100Exceptional

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 62 days ago
20/20	Version history — 38 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	ms2pip
ecosystems	pypi
any_deprecated	no
min_days_since_publish	62

Engineering Quality

Are baseline engineering and documentation practices in place?

86Excellent · 19% of overall

Engineering practices

84Excellent

How it's scored

24/24	CI workflows — 2 workflow(s)
24/24	Tests present
16/16	Linter config — pyproject.toml ([tool.ruff], [tool.black])
0/9.6	Pre-commit hooks
0/6.4	.editorconfig
20/20	OpenSSF Scorecard: CI-Tests — 9 out of 9 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	no

Documentation

90Excellent

How it's scored

30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — https://ms2pip.readthedocs.io
10/10	Repository description
10/10	Topics — 6 topics
0/10	Wiki

Inputs used

topics	machine-learning, mass-spectrometry, peptide-identification, proteomics, spectrum-prediction, peptide-spectrum
has_wiki	no
homepage	https://ms2pip.readthedocs.io
docs_site	https://ms2pip.readthedocs.io
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?

64Moderate · 16% of overall

Security posture

55Moderate

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
6/7.5	Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5	CI-Tests — 9 out of 9 merged PRs checked by a CI test -- score normalized to 10
0/2.5	CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5	Code-Review — Found 1/10 approved changesets -- score normalized to 1
2.5/2.5	Contributors — project has 5 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 — 19 commit(s) and 4 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
0/5	SAST — SAST tool is not run on all commits -- score normalized to 0
0/5	Security-Policy — security policy file not detected
0/7.5	Signed-Releases — no data
0/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
1.5/7.5	Vulnerabilities — 8 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	17
scorecard_version	v5.5.0
checks_inconclusive	1
scorecard_aggregate	5.5

Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional

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	29
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:ms2pip@4.2.0 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.

50Moderate · 4% of overall

Agent context & guidance

32At Risk

How it's scored

0/45	Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15	Machine-readable docs (llms.txt)
31.5/40	Legible commit history — 59 of 100 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.59
agent_instruction_files	—
agent_instruction_max_bytes	—

Verify loop (build / test / typecheck)

43Weak

How it's scored

0/18	One-command bootstrap
22/22	Automated tests
11/11	Lint / format config — pyproject.toml ([tool.ruff], [tool.black])
0/11	Static type checking
10/10	Reproducible environment — lockfile
0/10	Demonstrated agent practice — no agent-authored commits among the last 100
0/8	Automated maintenance — no automated dependency updates observed
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	uv.lock
has_dockerfile	no
typed_language	no
bootstrap_files	—
has_devcontainer	no
has_linter_config	yes
typecheck_configs	—
agent_commit_share	0
toolchain_manifests	—
dependency_bot_commit_share	0

Code legibility for models

55Moderate

How it's scored

0/45	Type-checkable code — HTML without a type-check config
55/55	Manageable file sizes — 0/25 source files over 60KB

Inputs used

primary_language	HTML
largest_source_bytes	28,997
source_files_sampled	25
oversized_source_files	0

Machine-readable interfaces

100Exceptional

How it's scored

0/40	API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20	MCP server — not applicable to this kind of software
40/40	Runnable examples — notebooks

Inputs used

example_dirs	notebooks
has_mcp_signal	no
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

50GitHub stars

11contributors

78commits, last 12 months

62days since last push

26releases

1bus factor

4open 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 ★ / 19 ⇿

0Stars

19Forks

23Releases

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.

Major 1Minor 10Patch 7

Each point covers 8 days.

OpenSSF Scorecard 5.5 / 10

5.5aggregate

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 · 2026-09-14 05:58 UTC

10	Binary-Artifacts	no binaries found in the repo
8	Branch-Protection	branch protection is not maximal on development and all release branches
10	CI-Tests	9 out of 9 merged PRs checked by a CI test -- score normalized to 10
0	CII-Best-Practices	no effort to earn an OpenSSF best practices badge detected
1	Code-Review	Found 1/10 approved changesets -- score normalized to 1
10	Contributors	project has 5 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	19 commit(s) and 4 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
0	SAST	SAST tool is not run on all commits -- score normalized to 0
0	Security-Policy	security policy file not detected
n/a	Signed-Releases	no releases found
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
2	Vulnerabilities	8 existing vulnerabilities detected

Direct dependencies 12

Registry	Package	Version constraint	Manifest
PyPI	numpy	`>=1.25,<3`	pyproject.toml
PyPI	pandas	`>=1,<3`	pyproject.toml
PyPI	pyteomics	`>=3.5,<5`	pyproject.toml
PyPI	sqlalchemy	`>=1.4,<3`	pyproject.toml
PyPI	click	`>=7,<9`	pyproject.toml
PyPI	xgboost	`>=1.3,<3`	pyproject.toml
PyPI	lxml	`>=4,<7`	pyproject.toml
PyPI	rich	`>=13,<16`	pyproject.toml
PyPI	pydantic	`>=2,<3`	pyproject.toml
PyPI	werkzeug	`>=2,<4`	pyproject.toml
PyPI	psm_utils	`>=1.5,<4`	pyproject.toml
PyPI	ms2rescore-rs	`>=0.5.0,<2`	pyproject.toml

All dependencies 176

Full resolved dependency set from the GitHub dependency graph: 15 direct and 161 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	click	`8.4.2`	direct
PyPI	lxml	`6.1.1`	direct
PyPI	ms2rescore-rs	`0.5.0`	direct
PyPI	numpy	`1.15.4`	direct
PyPI	numpy	`2.4.6`	direct
PyPI	pandas	`0.23.4`	direct
PyPI	pandas	`2.3.3`	direct
PyPI	psm-utils	`1.5.5`	direct
PyPI	pydantic	`2.13.4`	direct
PyPI	pyteomics	`3.5.1`	direct
PyPI	pyteomics	`4.7.5`	direct
PyPI	rich	`14.3.4`	direct
PyPI	sqlalchemy	`2.0.51`	direct
PyPI	werkzeug	`3.1.8`	direct
PyPI	xgboost	`2.1.4`	direct
PyPI	aiohappyeyeballs	`2.7.1`	indirect
PyPI	aiohttp	`3.14.1`	indirect
PyPI	aiosignal	`1.4.0`	indirect
PyPI	alabaster	`1.0.0`	indirect
PyPI	annotated-types	`0.7.0`	indirect
PyPI	anyio	`4.14.1`	indirect
PyPI	appnope	`0.1.4`	indirect
PyPI	asttokens	`3.0.1`	indirect
PyPI	attrs	`26.1.0`	indirect
PyPI	babel	`2.18.0`	indirect
PyPI	biopython	`1.72`	indirect
PyPI	certifi	`2026.6.17`	indirect
PyPI	cffi	`2.1.0`	indirect
PyPI	charset-normalizer	`3.4.9`	indirect
PyPI	colorama	`0.4.6`	indirect
PyPI	comm	`0.2.3`	indirect
PyPI	commonmark	`0.9.2`	indirect
PyPI	contourpy	`1.3.3`	indirect
PyPI	cuda-bindings	`13.3.1`	indirect
PyPI	cuda-pathfinder	`1.5.6`	indirect
PyPI	cuda-toolkit	`13.0.3.0`	indirect
PyPI	cycler	`0.12.1`	indirect
PyPI	cython	`0.29.2`	indirect
PyPI	debugpy	`1.8.21`	indirect
PyPI	decorator	`5.3.1`	indirect
PyPI	deeplc	`4.0.0b1`	indirect
PyPI	docutils	`0.22.4`	indirect
PyPI	executing	`2.2.1`	indirect
PyPI	fastobo	`0.14.1`	indirect
PyPI	filelock	`3.29.7`	indirect
PyPI	fonttools	`4.63.0`	indirect
PyPI	frozenlist	`1.8.0`	indirect
PyPI	fsspec	`2026.6.0`	indirect
PyPI	greenlet	`3.5.3`	indirect
PyPI	h11	`0.16.0`	indirect
PyPI	idna	`3.18`	indirect
PyPI	im2deep	`2.0.0`	indirect
PyPI	imagesize	`2.0.0`	indirect
PyPI	iniconfig	`2.3.0`	indirect
PyPI	ipykernel	`7.3.0`	indirect
PyPI	ipython	`9.15.0`	indirect
PyPI	ipython-pygments-lexers	`1.1.1`	indirect
PyPI	ipywidgets	`8.1.8`	indirect
PyPI	jedi	`0.20.0`	indirect
PyPI	jinja2	`3.1.6`	indirect
PyPI	joblib	`1.5.3`	indirect
PyPI	jupyter-client	`8.9.1`	indirect
PyPI	jupyter-core	`5.9.1`	indirect
PyPI	jupyterlab-widgets	`3.0.16`	indirect
PyPI	kiwisolver	`1.5.0`	indirect
PyPI	lark	`1.3.1`	indirect
PyPI	lightning	`2.6.5`	indirect
PyPI	lightning-utilities	`0.15.3`	indirect
PyPI	llvmlite	`0.48.0`	indirect
PyPI	markdown-it-py	`4.2.0`	indirect
PyPI	markupsafe	`3.0.3`	indirect
PyPI	matplotlib	`3.0.2`	indirect
PyPI	matplotlib	`3.11.0`	indirect
PyPI	matplotlib-inline	`0.2.2`	indirect
PyPI	mdurl	`0.1.2`	indirect
PyPI	ml-dtypes	`0.5.4`	indirect
PyPI	mpmath	`1.3.0`	indirect
PyPI	multidict	`6.7.1`	indirect
PyPI	narwhals	`2.23.0`	indirect
PyPI	nest-asyncio2	`1.7.2`	indirect
PyPI	networkx	`3.6.1`	indirect
PyPI	numba	`0.66.0`	indirect
PyPI	numpydoc	`1.10.0`	indirect
PyPI	nvidia-cublas	`13.1.1.3`	indirect
PyPI	nvidia-cuda-cupti	`13.0.85`	indirect
PyPI	nvidia-cuda-nvrtc	`13.0.88`	indirect
PyPI	nvidia-cuda-runtime	`13.0.96`	indirect
PyPI	nvidia-cudnn-cu13	`9.20.0.48`	indirect
PyPI	nvidia-cufft	`12.0.0.61`	indirect
PyPI	nvidia-cufile	`1.15.1.6`	indirect
PyPI	nvidia-curand	`10.4.0.35`	indirect
PyPI	nvidia-cusolver	`12.0.4.66`	indirect
PyPI	nvidia-cusparse	`12.6.3.3`	indirect
PyPI	nvidia-cusparselt-cu13	`0.8.1`	indirect
PyPI	nvidia-nccl-cu12	`2.30.7`	indirect
PyPI	nvidia-nccl-cu13	`2.29.7`	indirect
PyPI	nvidia-nvjitlink	`13.3.33`	indirect
PyPI	nvidia-nvshmem-cu13	`3.4.5`	indirect
PyPI	nvidia-nvtx	`13.0.85`	indirect
PyPI	onnx	`1.22.0`	indirect
PyPI	onnx2torch	`1.5.15`	indirect
PyPI	packaging	`26.2`	indirect
PyPI	parso	`0.8.7`	indirect
PyPI	pexpect	`4.9.0`	indirect
PyPI	pillow	`12.3.0`	indirect
PyPI	pip	`26.1.2`	indirect
PyPI	platformdirs	`4.10.0`	indirect
PyPI	pluggy	`1.6.0`	indirect
PyPI	prompt-toolkit	`3.0.52`	indirect
PyPI	propcache	`0.5.2`	indirect
PyPI	protobuf	`7.35.1`	indirect
PyPI	psims	`1.3.6`	indirect
PyPI	psutil	`7.2.2`	indirect
PyPI	ptyprocess	`0.7.0`	indirect
PyPI	pure-eval	`0.2.3`	indirect
PyPI	pyarrow	`25.0.0`	indirect
PyPI	pycparser	`3.0`	indirect
PyPI	pydantic-core	`2.46.4`	indirect
PyPI	pygments	`2.20.0`	indirect
PyPI	pyparsing	`3.3.2`	indirect
PyPI	pytest	`9.1.1`	indirect
PyPI	python-dateutil	`2.9.0.post0`	indirect
PyPI	pytorch-lightning	`2.6.5`	indirect
PyPI	pytz	`2026.2`	indirect
PyPI	pyyaml	`6.0.3`	indirect
PyPI	pyzmq	`27.1.0`	indirect
PyPI	recommonmark	`0.7.1`	indirect
PyPI	requests	`2.34.2`	indirect
PyPI	roman-numerals	`4.1.0`	indirect
PyPI	ruff	`0.15.21`	indirect
PyPI	scikit-learn	`1.9.0`	indirect
PyPI	scipy	`1.1.0`	indirect
PyPI	scipy	`1.17.1`	indirect
PyPI	scipy	`1.18.0`	indirect
PyPI	semver	`3.0.4`	indirect
PyPI	setuptools	`83.0.0`	indirect
PyPI	six	`1.17.0`	indirect
PyPI	snowballstemmer	`3.1.1`	indirect
PyPI	spectrum-utils	`0.5.0`	indirect
PyPI	sphinx	`9.0.4`	indirect
PyPI	sphinx	`9.1.0`	indirect
PyPI	sphinx-autobuild	`2025.8.25`	indirect
PyPI	sphinx-automodapi	`0.22.0`	indirect
PyPI	sphinx-click	`6.2.0`	indirect
PyPI	sphinx-rtd-theme	`3.1.0`	indirect
PyPI	sphinxcontrib-applehelp	`2.0.0`	indirect
PyPI	sphinxcontrib-devhelp	`2.0.0`	indirect
PyPI	sphinxcontrib-htmlhelp	`2.1.0`	indirect
PyPI	sphinxcontrib-jquery	`4.1`	indirect
PyPI	sphinxcontrib-jsmath	`1.0.1`	indirect
PyPI	sphinxcontrib-qthelp	`2.0.0`	indirect
PyPI	sphinxcontrib-serializinghtml	`2.0.0`	indirect
PyPI	stack-data	`0.6.3`	indirect
PyPI	starlette	`1.3.1`	indirect
PyPI	sympy	`1.14.0`	indirect
PyPI	tables	`3.4.4`	indirect
PyPI	threadpoolctl	`3.6.0`	indirect
PyPI	toml	`0.10.2`	indirect
PyPI	torch	`2.13.0`	indirect
PyPI	torchmetrics	`1.9.0`	indirect
PyPI	torchvision	`0.28.0`	indirect
PyPI	tornado	`6.5.7`	indirect
PyPI	tqdm	`4.68.4`	indirect
PyPI	traitlets	`5.15.1`	indirect
PyPI	triton	`3.7.1`	indirect
PyPI	ty	`0.0.58`	indirect
PyPI	typing-extensions	`4.16.0`	indirect
PyPI	typing-inspection	`0.4.2`	indirect
PyPI	tzdata	`2026.3`	indirect
PyPI	urllib3	`2.7.0`	indirect
PyPI	uvicorn	`0.51.0`	indirect
PyPI	watchfiles	`1.2.0`	indirect
PyPI	wcwidth	`0.8.2`	indirect
PyPI	websockets	`16.1`	indirect
PyPI	widgetsnbextension	`4.0.15`	indirect
PyPI	yarl	`1.24.2`	indirect

Dependency advisories 0

Installing pypi:ms2pip@4.2.0 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.

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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.

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