Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 02:18 UTC

microsoft / qdk-chemistry

A high-performance toolkit for quantum and classical chemistry calculations.

C++ · PythonMIT★ 73 stars⑂ 34 forkssince Oct 2025View on GitHub ↗

KindCommand-line toolLibraryhow this is determined

microsoft/qdk-chemistry holds a health index of 96 out of 100, placing it in the Exceptional band. It scores highest on Vitality (92/100) and lowest on Community & Adoption (64/100). It was last updated today. 5 contributors account for most of its recent work.

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.

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 84 is calibrated to 96 on the published index scale (record calibration 2026-08-02).

Ownership

MicrosoftOrganization · verified domain

129,840 followers8,307 public repossince Dec 2013

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
PyPI	qdk-chemistry	2.2.1	-	9	6 days ago

Metrics by category

Vitality

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

92Excellent · 21% of overall

Development activity

94Exceptional

How it's scored

36/36	Push recency — last push 0 days ago
29.8/36	Commit cadence — 43/52 weeks with commits
18/18	Commit volume — 319 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	319
human_commit_share	0.99
days_since_last_push	0
active_weeks_last_year	43

Release discipline

90Excellent

How it's scored

27/27	Ships releases — 8 releases published
36/36	Release recency — latest release 6 days ago
27/27	Release cadence — a release every ~32.8 days
0/10	OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.

Inputs used

releases_count	8
latest_release_tag	v2.2.1
releases_from_tags	no
days_since_latest_release	6
mean_days_between_releases	32.8

Community & Adoption

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

64Moderate · 17% of overall

Popularity & adoption

46Weak

How it's scored

30.1/60	Stars — 73 stars
12.7/25	Forks — 34 forks
2.7/15	Watchers — 4 watchers

Inputs used

forks	34
stars	73
watchers	4
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

85Excellent

How it's scored

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

Inputs used

has_readme	yes
has_license	yes
readme_badges	0
has_contributing	yes
has_issue_template	no
has_code_of_conduct	yes
readme_badge_services	—
has_pull_request_template	no

Sustainability & Governance

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

92Excellent · 23% of overall

Maintainer resilience (bus factor)

88Excellent

How it's scored

45.9/54	Bus factor — 5 contributor(s) cover half of all commits
18.4/22.5	Commit distribution — top contributor authored 18% of commits
13.5/13.5	Contributor breadth — 27 contributors
10/10	OpenSSF Scorecard: Contributors — project has 15 contributing companies or organizations

Inputs used

bus_factor	5
contributors_sampled	27
top_contributor_share	0.184

Issue & PR responsiveness

84Excellent

How it's scored

35.7/42	Issue resolution — 85% of issues closed
24.5/30	PR acceptance — 345/423 decided PRs merged
8.7/13	Newcomer PR acceptance — 2/3 first-time contributors' PRs merged in 30d
15/15	OpenSSF Scorecard: Code-Review — all changesets reviewed

Inputs used

merged_prs	345
open_issues	39
closed_issues	222
prs_merged_7d	3
prs_decided_7d	3
prs_merged_30d	37
prs_decided_30d	40
issue_closed_ratio	0.851
closed_unmerged_prs	78
first_time_authors_30d	3
first_time_prs_merged_30d	2
first_time_prs_decided_30d	3

Ownership & stewardship

100Exceptional

How it's scored

30/30	Ownership backing — organization-owned
20/20	Verified domain
25/25	Owner reach — 129,840 followers of microsoft
25/25	Track record — 8,307 public repos, account ~12 yr old

Inputs used

followers	129,840
owner_type	Organization
is_verified	yes
owner_login	microsoft
public_repos	8,307
account_age_days	4,663

Package maintenance

100Exceptional

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 6 days ago
20/20	Version history — 9 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	qdk-chemistry
ecosystems	pypi
any_deprecated	no
min_days_since_publish	6

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional

How it's scored

24/24	CI workflows — 4 workflow(s)
24/24	Tests present
16/16	Linter config — python/pyproject.toml ([tool.ruff]), ruff.toml
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

90Excellent

How it's scored

30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — https://microsoft.github.io/qdk-chemistry/
10/10	Repository description
10/10	Topics — 2 topics
0/10	Wiki

Inputs used

topics	quantum-chemistry, quantum-computing
has_wiki	no
homepage	https://microsoft.github.io/qdk-chemistry/
docs_site	https://microsoft.github.io/qdk-chemistry/
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?

74Good · 16% of overall

Security posture

67Good

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
3.8/7.5	Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5	CI-Tests — 30 out of 30 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
7.5/7.5	Code-Review — all changesets reviewed
2.5/2.5	Contributors — project has 15 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
0/5	Packaging — no data
3/5	Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 6
0/5	SAST — SAST tool is not run on all commits -- score normalized to 0
5/5	Security-Policy — security policy file detected
0/7.5	Signed-Releases — Project has not signed or included provenance with any releases.
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	17
scorecard_version	v5.5.0
checks_inconclusive	1
scorecard_aggregate	6.7

Excluded from scoring (no data or not applicable): Packaging. 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	43
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:qdk-chemistry@2.2.1 runtime dependency closure — what installing the published package pulls in — 43 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.

80Excellent · 4% of overall

Agent context & guidance

40Weak

How it's scored

0/45	Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15	Machine-readable docs (llms.txt)
40/40	Legible commit history — 96 of 99 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.97
agent_instruction_files	—
agent_instruction_max_bytes	—

Verify loop (build / test / typecheck)

96Exceptional

How it's scored

18/18	One-command bootstrap — docs/Makefile
22/22	Automated tests
11/11	Lint / format config — python/pyproject.toml ([tool.ruff]), ruff.toml
11/11	Static type checking — python/src/qdk_chemistry/py.typed
10/10	Reproducible environment — devcontainer, Dockerfile
10/10	Demonstrated agent practice — 60 of the last 100 commits agent-authored or agent-credited
8/8	Automated maintenance — 1 of the last 100 commits are automated dependency updates
6/10	OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 6

Inputs used

has_nix	no
has_tests	yes
lockfiles	—
has_dockerfile	yes
typed_language	yes
bootstrap_files	docs/Makefile
has_devcontainer	yes
has_linter_config	yes
typecheck_configs	python/src/qdk_chemistry/py.typed
agent_commit_share	0.6
toolchain_manifests	—
dependency_bot_commit_share	0.01

Code legibility for models

99Exceptional

How it's scored

45/45	Type-checkable code — C++ (statically typed)
53.9/55	Manageable file sizes — 18/935 source files over 60KB

Inputs used

primary_language	C++
largest_source_bytes	144,600
source_files_sampled	935
oversized_source_files	18

Machine-readable interfaces

100Exceptional

How it's scored

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

Inputs used

example_dirs	examples, notebooks, sample
has_mcp_signal	yes
api_schema_files	—
interfaces_expected_of	—

Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto). Remaining weights renormalized.

Key facts

73GitHub stars

27contributors

319commits, last 12 months

0days since last push

8releases

5bus factor

39open issues

PyPIpackage ecosystems

Data collection warnings

Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
First-time contributor figures cover 12 of 14 authors (cap 12)
pypi download statistics for 'qdk-chemistry' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 34 ⇿

0Stars

34Forks

6Releases

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 2Minor 2Patch 2

OpenSSF Scorecard 6.7 / 10

6.7aggregate

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 02:17 UTC

10	Binary-Artifacts	no binaries found in the repo
5	Branch-Protection	branch protection is not maximal on development and all release branches
10	CI-Tests	30 out of 30 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
10	Code-Review	all changesets reviewed
10	Contributors	project has 15 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
n/a	Packaging	packaging workflow not detected
6	Pinned-Dependencies	dependency not pinned by hash detected -- score normalized to 6
0	SAST	SAST tool is not run on all commits -- score normalized to 0
10	Security-Policy	security policy file detected
0	Signed-Releases	Project has not signed or included provenance with any releases.
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
10	Vulnerabilities	0 existing vulnerabilities detected

Direct dependencies 8

Registry	Package	Version constraint	Manifest
PyPI	numpy	`>=2.0.0,<2.4.0`	python/pyproject.toml
PyPI	scipy	`>=1.7.0`	python/pyproject.toml
PyPI	matplotlib	`>=3.10.0`	python/pyproject.toml
PyPI	argcomplete	`>=3.0.0`	python/pyproject.toml
PyPI	ruamel-yaml	`>=0.18.10`	python/pyproject.toml
PyPI	qdk	`>=1.31.0`	python/pyproject.toml
PyPI	pybind11-stubgen	`>=2.5.1`	python/pyproject.toml
PyPI	h5py	`>=3.0.0`	python/pyproject.toml

All dependencies 49

Full resolved dependency set from the GitHub dependency graph: 9 direct and 40 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	argcomplete	`—`	direct
PyPI	h5py	`—`	direct
PyPI	matplotlib	`—`	direct
PyPI	numpy	`—`	direct
PyPI	numpy	`2.3.5`	direct
PyPI	pybind11-stubgen	`—`	direct
PyPI	qdk	`—`	direct
PyPI	ruamel-yaml	`—`	direct
PyPI	scipy	`—`	direct
PyPI	altgraph	`0.17.5`	indirect
PyPI	auditwheel	`6.6.0`	indirect
PyPI	azure-ai-discovery	`1.0.0`	indirect
PyPI	azure-identity	`—`	indirect
PyPI	azure-storage-blob	`—`	indirect
PyPI	build	`1.5.0`	indirect
PyPI	colorama	`0.4.6`	indirect
PyPI	delocate	`0.13.0`	indirect
PyPI	fonttools	`4.62.1`	indirect
PyPI	gcovr	`—`	indirect
PyPI	geometric	`—`	indirect
PyPI	googleapis-common-protos	`1.75.0`	indirect
PyPI	grpcio	`1.80.0`	indirect
PyPI	ipykernel	`—`	indirect
PyPI	macholib	`1.16.4`	indirect
PyPI	mcp	`—`	indirect
PyPI	networkx	`—`	indirect
PyPI	opentelemetry-api	`1.42.1`	indirect
PyPI	opentelemetry-exporter-otlp-proto-common	`1.42.1`	indirect
PyPI	opentelemetry-exporter-otlp-proto-grpc	`1.42.1`	indirect
PyPI	opentelemetry-proto	`1.42.1`	indirect
PyPI	opentelemetry-sdk	`1.42.1`	indirect
PyPI	opentelemetry-semantic-conventions	`0.63b1`	indirect
PyPI	packaging	`26.2`	indirect
PyPI	pandas	`—`	indirect
PyPI	pathspec	`1.1.1`	indirect
PyPI	protobuf	`5.29.6`	indirect
PyPI	pybind11	`—`	indirect
PyPI	pybind11	`3.0.4`	indirect
PyPI	pyelftools	`0.32`	indirect
PyPI	pylatexenc	`2.10`	indirect
PyPI	pyproject-hooks	`1.2.0`	indirect
PyPI	pyscf	`—`	indirect
PyPI	qiskit	`—`	indirect
PyPI	qiskit-ibm-runtime	`—`	indirect
PyPI	requests	`—`	indirect
PyPI	scikit-build-core	`—`	indirect
PyPI	scikit-build-core	`0.12.2`	indirect
PyPI	setuptools	`—`	indirect
PyPI	urllib3	`2.7.0`	indirect

Dependency advisories 0

Installing pypi:qdk-chemistry@2.2.1 pulls in 43 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

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

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.

Software health index

Score profile

Ownership

Package ecosystems

Metrics by category

Key facts

Data collection warnings

More detail

Feedback

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