Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-21 01:56 UTC

fmind / mlops-python-package

Production-shaped MLOps Python package and reference implementation of the MLOps Coding Course: MLflow, scikit-learn, Pydantic, Pandera, uv, and mise.

Python · Jupyter NotebookMIT★ 1,419 stars⑂ 199 forkssince Jun 2023View on GitHub ↗

KindCommand-line toolhow this is determined

#mlops #python #automation #data-science #machine-learning #mlflow #pandera #pydantic #machine-learning-operations #mise #scikit-learn #uv #mlops-course

fmind/mlops-python-package holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on AI Readiness (82/100) and lowest on Sustainability & Governance (48/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

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.

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 69 is calibrated to 81 on the published index scale (record calibration 2026-08-02).

Ownership

Médéric Hurier (Fmind)Personal account

310 followers170 public repossince Mar 2013Fmind.dev

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Metrics by category

Vitality

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

76Good · 21% of overall

Development activity

65Good

How it's scored

36/36	Push recency — last push 1 days ago
4.2/36	Commit cadence — 6/52 weeks with commits
15/18	Commit volume — 46 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	46
human_commit_share	0.9
days_since_last_push	1
active_weeks_last_year	6

Release discipline

92Excellent

How it's scored

27/27	Ships releases — 20 releases published
36/36	Release recency — latest release 8 days ago
19.8/27	Release cadence — a release every ~90.1 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	20
latest_release_tag	v7.0.1
releases_from_tags	no
days_since_latest_release	8
mean_days_between_releases	90.1

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?

75Good · 17% of overall

Popularity & adoption

77Good

How it's scored

51.1/60	Stars — 1,419 stars
19.1/25	Forks — 199 forks
6.4/15	Watchers — 15 watchers

Inputs used

forks	199
stars	1,419
watchers	15
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

72Good

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (MIT)
0/18	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
readme_badges	5
has_contributing	no
has_issue_template	no
has_code_of_conduct	yes
readme_badge_services	github.com, shields.io
has_pull_request_template	yes

Sustainability & Governance

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

48Weak · 23% of overall

Maintainer resilience (bus factor)

20At Risk

How it's scored

9/54	Bus factor — 1 contributor(s) cover half of all commits
0.9/22.5	Commit distribution — top contributor authored 96% of commits
6.8/13.5	Contributor breadth — 5 contributors
3/10	OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3

Inputs used

bus_factor	1
contributors_sampled	5
top_contributor_share	0.959

Issue & PR responsiveness

62Moderate

How it's scored

38.8/42	Issue resolution — 92% of issues closed
10.7/30	PR acceptance — 42/118 decided PRs merged
13/13	Newcomer PR acceptance — 1/1 first-time contributors' PRs merged in 30d
0/15	OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0

Inputs used

merged_prs	42
open_issues	2
closed_issues	24
prs_merged_7d	0
prs_decided_7d	0
prs_merged_30d	1
prs_decided_30d	1
issue_closed_ratio	0.923
closed_unmerged_prs	76
first_time_authors_30d	1
first_time_prs_merged_30d	1
first_time_prs_decided_30d	1

Ownership & stewardship

66Good

How it's scored

10/30	Ownership backing — personal (user) account
0/20	Verified domain — not applicable to user accounts
17.9/25	Owner reach — 310 followers of fmind
25/25	Track record — 170 public repos, account ~13 yr old

Inputs used

followers	310
owner_type	User
is_verified	—
owner_login	fmind
public_repos	170
account_age_days	4,961

Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

74Good · 19% of overall

Engineering practices

80Excellent

How it's scored

24/24	CI workflows — 3 workflow(s)
24/24	Tests present
16/16	Linter config — pyproject.toml ([tool.ruff])
0/9.6	Pre-commit hooks
0/6.4	.editorconfig
0/20	OpenSSF Scorecard: CI-Tests — no data

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	yes
has_precommit_config	no

Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

65Good

How it's scored

30/30	README
0/25	Documentation directory
15/15	Documentation / homepage site — https://fmind.github.io/mlops-python-package/
10/10	Repository description
10/10	Topics — 13 topics
0/10	Wiki

Inputs used

topics	mlops, python, automation, data-science, machine-learning, mlflow, pandera, pydantic, machine-learning-operations, mise, scikit-learn, uv, mlops-course
has_wiki	no
homepage	https://fmind.github.io/mlops-python-package/
docs_site	https://fmind.github.io/mlops-python-package/
has_readme	yes
has_docs_dir	no
has_description	yes

Security

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

72Good · 16% of overall

Security posture

65Good

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
3/7.5	Branch-Protection — branch protection is not maximal on development and all release branches
0/2.5	CI-Tests — no data
0/2.5	CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5	Code-Review — Found 0/30 approved changesets -- score normalized to 0
0.8/2.5	Contributors — project has 1 contributing companies or organizations -- score normalized to 3
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 1 issue activity found in the last 90 days -- score normalized to 10
5/5	Packaging — packaging workflow detected
5/5	Pinned-Dependencies — all dependencies are pinned
0/5	SAST — no SAST tool detected
5/5	Security-Policy — security policy file detected
0/7.5	Signed-Releases — no data
7.5/7.5	Token-Permissions — GitHub workflow tokens follow principle of least privilege
0/7.5	Vulnerabilities — 10 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	16
scorecard_version	v5.5.0
checks_inconclusive	2
scorecard_aggregate	6.4

Excluded from scoring (no data or not applicable): CI-Tests, 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
0/25	Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	11
affected_packages	2
assessed_packages	175
unassessed_packages	0
affected_by_severity	high 2
direct_affected_packages	0

Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 175 resolved dependencies against OSV. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

82Excellent · 4% of overall

Agent context & guidance

85Excellent

How it's scored

45/45	Agent instructions — AGENTS.md
0/15	Machine-readable docs (llms.txt)
40/40	Legible commit history — 79 of 90 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.878
agent_instruction_files	AGENTS.md
agent_instruction_max_bytes	5,561

Verify loop (build / test / typecheck)

83Excellent

How it's scored

18/18	One-command bootstrap — mise.toml
22/22	Automated tests
11/11	Lint / format config — pyproject.toml ([tool.ruff])
0/11	Static type checking
10/10	Reproducible environment — Dockerfile, lockfile
4/10	Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
8/8	Automated maintenance — 10 of the last 100 commits are automated dependency updates
10/10	OpenSSF Scorecard: Pinned-Dependencies — all dependencies are pinned

Inputs used

has_nix	no
has_tests	yes
lockfiles	uv.lock
has_dockerfile	yes
typed_language	no
bootstrap_files	mise.toml
has_devcontainer	no
has_linter_config	yes
typecheck_configs	—
agent_commit_share	0.02
toolchain_manifests	—
dependency_bot_commit_share	0.1

Code legibility for models

55Moderate

How it's scored

0/45	Type-checkable code — Python without a type-check config
55/55	Manageable file sizes — 0/47 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	13,764
source_files_sampled	47
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

1,419GitHub stars

5contributors

46commits, last 12 months

1days since last push

20releases

1bus factor

2open issues

PyPIpackage ecosystems

Data collection warnings

Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
Could not fetch pypi package 'bikes' from its registry

More detail

Star and fork history 0 ★ / 199 ⇿

0Stars

199Forks

17Releases

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 6Minor 10Patch 1

Each point covers 3 days.

OpenSSF Scorecard 6.4 / 10

6.4aggregate

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 · 2026-10-21 01:55 UTC

10	Binary-Artifacts	no binaries found in the repo
4	Branch-Protection	branch protection is not maximal on development and all release branches
n/a	CI-Tests	no pull request found
0	CII-Best-Practices	no effort to earn an OpenSSF best practices badge detected
0	Code-Review	Found 0/30 approved changesets -- score normalized to 0
3	Contributors	project has 1 contributing companies or organizations -- score normalized to 3
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 1 issue activity found in the last 90 days -- score normalized to 10
10	Packaging	packaging workflow detected
10	Pinned-Dependencies	all dependencies are pinned
0	SAST	no SAST tool detected
10	Security-Policy	security policy file detected
n/a	Signed-Releases	no releases found
10	Token-Permissions	GitHub workflow tokens follow principle of least privilege
0	Vulnerabilities	10 existing vulnerabilities detected

Direct dependencies 18

Registry	Package	Version constraint	Manifest
PyPI	loguru	`>=0.7.3`	pyproject.toml
PyPI	matplotlib	`>=3.11.0`	pyproject.toml
PyPI	mlflow	`>=3.16.1`	pyproject.toml
PyPI	numba	`>=0.61.0`	pyproject.toml
PyPI	numpy	`>=2.1.3`	pyproject.toml
PyPI	nvidia-ml-py	`>=13.615.71`	pyproject.toml
PyPI	omegaconf	`>=2.3.1`	pyproject.toml
PyPI	pandas	`>=2.3.3`	pyproject.toml
PyPI	pandera	`>=0.32.1`	pyproject.toml
PyPI	plyer	`>=2.1.0`	pyproject.toml
PyPI	psutil	`>=7.2.2`	pyproject.toml
PyPI	pyarrow	`>=19.0.1`	pyproject.toml
PyPI	pydantic-settings	`>=2.14.2`	pyproject.toml
PyPI	pydantic	`>=2.13.4`	pyproject.toml
PyPI	scikit-learn	`>=1.9.0`	pyproject.toml
PyPI	setuptools	`>=83.0.0`	pyproject.toml
PyPI	shap	`>=0.46.0`	pyproject.toml
PyPI	skops	`>=0.16.0`	pyproject.toml

All dependencies 175

Full resolved dependency set from the GitHub dependency graph: 19 direct and 156 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	loguru	`0.7.3`	direct
PyPI	matplotlib	`3.11.2`	direct
PyPI	mlflow	`3.16.1`	direct
PyPI	numba	`0.68.0`	direct
PyPI	numpy	`2.5.3`	direct
PyPI	nvidia-ml-py	`13.615.71`	direct
PyPI	omegaconf	`2.3.1`	direct
PyPI	pandas	`3.0.6`	direct
PyPI	pandera	`0.34.0`	direct
PyPI	plyer	`2.1.0`	direct
PyPI	psutil	`7.2.2`	direct
PyPI	pyarrow	`25.0.1`	direct
PyPI	pydantic	`2.13.5`	direct
PyPI	pydantic-settings	`2.15.0`	direct
PyPI	scikit-learn	`1.9.1`	direct
PyPI	setuptools	`84.0.0`	direct
PyPI	shap	`0.49.1`	direct
PyPI	shap	`0.52.0`	direct
PyPI	skops	`0.16.0`	direct
PyPI	aiohappyeyeballs	`2.7.1`	indirect
PyPI	aiohttp	`3.14.4`	indirect
PyPI	aiosignal	`1.4.0`	indirect
PyPI	alembic	`1.20.0`	indirect
PyPI	annotated-doc	`0.0.5`	indirect
PyPI	annotated-types	`0.8.0`	indirect
PyPI	antlr4-python3-runtime	`4.9.3`	indirect
PyPI	anyio	`4.15.1`	indirect
PyPI	appnope	`1.0.0`	indirect
PyPI	asttokens	`3.0.2`	indirect
PyPI	attrs	`26.1.0`	indirect
PyPI	bikes	`6.0.1`	indirect
PyPI	blinker	`1.9.0`	indirect
PyPI	boolean-py	`5.0`	indirect
PyPI	cachecontrol	`0.14.4`	indirect
PyPI	cachetools	`7.2.1`	indirect
PyPI	certifi	`2026.7.22`	indirect
PyPI	cffi	`2.1.1`	indirect
PyPI	charset-normalizer	`3.5.2`	indirect
PyPI	click	`8.5.0`	indirect
PyPI	cloudpickle	`3.1.2`	indirect
PyPI	colorama	`0.4.6`	indirect
PyPI	comm	`0.2.3`	indirect
PyPI	contourpy	`1.4.0`	indirect
PyPI	coverage	`7.16.2`	indirect
PyPI	cryptography	`50.0.2`	indirect
PyPI	cycler	`0.12.1`	indirect
PyPI	cyclonedx-python-lib	`11.12.0`	indirect
PyPI	databricks-sdk	`0.147.0`	indirect
PyPI	debugpy	`1.8.22`	indirect
PyPI	defusedxml	`0.7.1`	indirect
PyPI	docker	`7.2.0`	indirect
PyPI	execnet	`2.1.2`	indirect
PyPI	executing	`2.2.1`	indirect
PyPI	fastapi	`0.142.2`	indirect
PyPI	fastjsonschema	`2.22.2`	indirect
PyPI	filelock	`4.0.12`	indirect
PyPI	flask	`3.1.3`	indirect
PyPI	flask-cors	`6.0.5`	indirect
PyPI	fonttools	`4.66.1`	indirect
PyPI	frozenlist	`1.8.0`	indirect
PyPI	gitdb	`4.0.12`	indirect
PyPI	gitpython	`3.2.0`	indirect
PyPI	google-auth	`2.60.0`	indirect
PyPI	graphene	`3.4.3`	indirect
PyPI	graphql-core	`3.2.13`	indirect
PyPI	graphql-relay	`3.2.0`	indirect
PyPI	gunicorn	`26.2.0`	indirect
PyPI	h11	`0.16.0`	indirect
PyPI	huey	`3.4.0`	indirect
PyPI	idna	`3.20`	indirect
PyPI	importlib-metadata	`9.0.1`	indirect
PyPI	iniconfig	`2.3.0`	indirect
PyPI	ipykernel	`7.4.0`	indirect
PyPI	ipython	`9.17.1`	indirect
PyPI	ipython-pygments-lexers	`1.1.1`	indirect
PyPI	itsdangerous	`2.2.0`	indirect
PyPI	jedi	`0.20.0`	indirect
PyPI	jinja2	`3.1.6`	indirect
PyPI	joblib	`1.6.0`	indirect
PyPI	jsonschema	`4.26.0`	indirect
PyPI	jsonschema-specifications	`2025.9.1`	indirect
PyPI	jupyter-client	`8.10.0`	indirect
PyPI	jupyter-core	`5.9.1`	indirect
PyPI	kiwisolver	`1.5.1`	indirect
PyPI	lefthook	`2.1.17`	indirect
PyPI	license-expression	`30.4.4`	indirect
PyPI	llvmlite	`0.50.0`	indirect
PyPI	mako	`1.4.3`	indirect
PyPI	markdown-it-py	`4.2.0`	indirect
PyPI	markdown2	`2.5.5`	indirect
PyPI	markupsafe	`3.0.4`	indirect
PyPI	matplotlib-inline	`0.2.2`	indirect
PyPI	mdurl	`0.1.2`	indirect
PyPI	mlflow-skinny	`3.16.1`	indirect
PyPI	mlflow-tracing	`3.16.1`	indirect
PyPI	msgpack	`1.2.3`	indirect
PyPI	multidict	`6.9.1`	indirect
PyPI	mypy-extensions	`1.1.0`	indirect
PyPI	narwhals	`2.26.0`	indirect
PyPI	nbformat	`5.11.1`	indirect
PyPI	nest-asyncio2	`1.7.3`	indirect
PyPI	opentelemetry-api	`1.45.0`	indirect
PyPI	opentelemetry-proto	`1.45.0`	indirect
PyPI	opentelemetry-sdk	`1.45.0`	indirect
PyPI	opentelemetry-semantic-conventions	`0.66b0`	indirect
PyPI	packageurl-python	`0.17.6`	indirect
PyPI	packaging	`26.3`	indirect
PyPI	parso	`0.8.7`	indirect
PyPI	pdoc	`16.0.0`	indirect
PyPI	pexpect	`4.9.0`	indirect
PyPI	pillow	`12.3.0`	indirect
PyPI	pip	`26.2.1`	indirect
PyPI	pip-api	`0.0.35`	indirect
PyPI	pip-audit	`2.10.1`	indirect
PyPI	pip-requirements-parser	`32.0.1`	indirect
PyPI	platformdirs	`4.12.3`	indirect
PyPI	plotly	`7.1.0`	indirect
PyPI	pluggy	`1.6.0`	indirect
PyPI	prettytable	`3.18.0`	indirect
PyPI	prompt-toolkit	`3.0.53`	indirect
PyPI	propcache	`0.5.4`	indirect
PyPI	protobuf	`6.33.6`	indirect
PyPI	ptyprocess	`0.7.0`	indirect
PyPI	pure-eval	`0.2.4`	indirect
PyPI	py-serializable	`2.1.0`	indirect
PyPI	pyasn1	`0.6.4`	indirect
PyPI	pyasn1-modules	`0.4.2`	indirect
PyPI	pycparser	`3.0`	indirect
PyPI	pydantic-core	`2.46.5`	indirect
PyPI	pygments	`2.21.0`	indirect
PyPI	pyparsing	`3.3.3`	indirect
PyPI	pytest	`9.1.1`	indirect
PyPI	pytest-cov	`7.1.0`	indirect
PyPI	pytest-mock	`3.16.0`	indirect
PyPI	pytest-xdist	`3.8.0`	indirect
PyPI	python-dateutil	`2.9.0.post0`	indirect
PyPI	python-dotenv	`1.2.4`	indirect
PyPI	pywin32	`312`	indirect
PyPI	pyyaml	`6.0.3`	indirect
PyPI	pyzmq	`27.2.0`	indirect
PyPI	referencing	`0.37.0`	indirect
PyPI	requests	`2.34.2`	indirect
PyPI	rich	`15.0.0`	indirect
PyPI	rpds-py	`2026.9.1`	indirect
PyPI	ruff	`0.16.10`	indirect
PyPI	scipy	`1.18.1`	indirect
PyPI	six	`1.17.0`	indirect
PyPI	slicer	`0.0.8`	indirect
PyPI	smmap	`5.0.3`	indirect
PyPI	sortedcontainers	`2.4.0`	indirect
PyPI	sqlalchemy	`2.1.3`	indirect
PyPI	sqlparse	`0.6.0`	indirect
PyPI	stack-data	`0.6.3`	indirect
PyPI	starlette	`1.7.0`	indirect
PyPI	threadpoolctl	`3.7.0`	indirect
PyPI	tomli	`2.4.1`	indirect
PyPI	tomli-w	`1.2.0`	indirect
PyPI	tornado	`6.5.10`	indirect
PyPI	tqdm	`4.70.1`	indirect
PyPI	traitlets	`5.16.1`	indirect
PyPI	ty	`0.0.84`	indirect
PyPI	typeguard	`4.6.0`	indirect
PyPI	typing-extensions	`4.16.0`	indirect
PyPI	typing-inspect	`0.9.0`	indirect
PyPI	typing-inspection	`0.4.4`	indirect
PyPI	tzdata	`2026.5`	indirect
PyPI	urllib3	`2.8.0`	indirect
PyPI	uvicorn	`0.54.0`	indirect
PyPI	validate-pyproject	`0.26`	indirect
PyPI	waitress	`3.0.2`	indirect
PyPI	wcwidth	`0.9.2`	indirect
PyPI	werkzeug	`3.1.9`	indirect
PyPI	win32-setctime	`1.2.0`	indirect
PyPI	yarl	`1.25.1`	indirect
PyPI	zipp	`4.1.1`	indirect

Dependency advisories 2

This repository publishes no package the index resolves, so its own dependency graph was assessed — 175 packages, which also include development and test pins that never ship: 2 carry known advisories, of which 0 are direct.

Package	Version	Relation	Severity	Advisories	Fixed in
certifi	`2026.7.22`	indirect	high	2	`2026.10.19`
pillow	`12.3.0`	indirect	high	9	`13.0.0`

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.

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