Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-21 00:17 UTC

SigmaHQ / pySigma

Python library to parse and convert Sigma rules into queries (and whatever else you could imagine)

PythonLGPL-2.1★ 601 stars⑂ 160 forkssince Jun 2021View on GitHub ↗

SigmaHQ/pySigma holds a health index of 34 out of 100, placing it in the At Risk band. It scores highest on Vitality (96/100) and lowest on Security (42/100). It was last updated 2 days ago. A single contributor accounts for most of its recent work.

overall / 100

At Risk

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 74 is calibrated to 88 on the published index scale (record calibration 2026-08-02). High-Risk Jurisdiction Policy applies a 75% multiplier to weighted overall health and gives it an At Risk ceiling of 34.

Ownership

SigmaOrganization

981 followers31 public repossince Mar 2021

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	pySigma	2.0.1	385,306	104	9 days ago

Metrics by category

Vitality

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

96Exceptional · 21% of overall

Development activity

93Exceptional

How it's scored

36/36	Push recency — last push 2 days ago
29.1/36	Commit cadence — 42/52 weeks with commits
18/18	Commit volume — 502 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	502
human_commit_share	0.97
days_since_last_push	2
active_weeks_last_year	42

Release discipline

100Exceptional

How it's scored

27/27	Ships releases — 100 releases published
36/36	Release recency — latest release 9 days ago
27/27	Release cadence — a release every ~23.4 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	100
latest_release_tag	v2.0.1
releases_from_tags	no
days_since_latest_release	9
mean_days_between_releases	23.4

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?

69Good · 17% of overall

Popularity & adoption

71Good

How it's scored

45.1/60	Stars — 601 stars
18.3/25	Forks — 160 forks
7.2/15	Watchers — 21 watchers

Inputs used

forks	160
stars	601
watchers	21
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 (LGPL-2.1)
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	3
has_contributing	no
has_issue_template	no
has_code_of_conduct	no
readme_badge_services	github.com, shields.io
has_pull_request_template	no

Ecosystem adoption (downloads)

93Exceptional

How it's scored

74.5/80	Monthly downloads — 385,306 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem

Inputs used

packages	pySigma
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	385,306
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?

70Good · 23% of overall

Maintainer resilience (bus factor)

39Weak

How it's scored

9/54	Bus factor — 1 contributor(s) cover half of all commits
6.7/22.5	Commit distribution — top contributor authored 70% of commits
13.5/13.5	Contributor breadth — 68 contributors
10/10	OpenSSF Scorecard: Contributors — project has 17 contributing companies or organizations

Inputs used

bus_factor	1
contributors_sampled	68
top_contributor_share	0.704

Issue & PR responsiveness

78Good

How it's scored

41.4/42	Issue resolution — 99% of issues closed
26.9/30	PR acceptance — 343/383 decided PRs merged
8.7/13	Newcomer PR acceptance — 4/6 first-time contributors' PRs merged in 30d
1.5/15	OpenSSF Scorecard: Code-Review — Found 2/15 approved changesets -- score normalized to 1

Inputs used

merged_prs	343
open_issues	2
closed_issues	137
prs_merged_7d	0
prs_decided_7d	0
prs_merged_30d	36
prs_decided_30d	40
issue_closed_ratio	0.986
closed_unmerged_prs	40
first_time_authors_30d	6
first_time_prs_merged_30d	4
first_time_prs_decided_30d	6

Ownership & stewardship

74Good

How it's scored

30/30	Ownership backing — organization-owned
0/20	Verified domain
21.5/25	Owner reach — 981 followers of SigmaHQ
22.2/25	Track record — 31 public repos, account ~5 yr old

Inputs used

followers	981
owner_type	Organization
is_verified	no
owner_login	SigmaHQ
public_repos	31
account_age_days	2,059

Package maintenance

100Exceptional

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 9 days ago
20/20	Version history — 104 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	pySigma
ecosystems	pypi
any_deprecated	no
min_days_since_publish	9

Engineering Quality

Are baseline engineering and documentation practices in place?

86Excellent · 19% of overall

Engineering practices

94Exceptional

How it's scored

24/24	CI workflows — 3 workflow(s)
24/24	Tests present
16/16	Linter config — pyproject.toml ([tool.black])
9.6/9.6	Pre-commit hooks
0/6.4	.editorconfig
20/20	OpenSSF Scorecard: CI-Tests — 8 out of 8 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

75Good

How it's scored

30/30	README
25/25	Documentation directory
0/15	Documentation / homepage site
10/10	Repository description
0/10	Topics
10/10	Wiki

Inputs used

topics	—
has_wiki	yes
homepage	—
docs_site	—
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?

42Weak · 16% of overall

Security posture

34At Risk

Jurisdiction risk alert

High-risk jurisdiction exposure detected

1 contributor organization profilesIran

Based on self-published public profile locations. This governance signal does not determine nationality, citizenship, intent, sanctions status, or individual trustworthiness.

How jurisdiction exposure is assessed

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
0/7.5	Branch-Protection — no data
2.5/2.5	CI-Tests — 8 out of 8 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 2/15 approved changesets -- score normalized to 1
2.5/2.5	Contributors — project has 17 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 5 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
5/5	Security-Policy — security policy file detected
0/7.5	Signed-Releases — no data
0/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
6/7.5	Vulnerabilities — 2 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	16
scorecard_version	v5.5.0
checks_inconclusive	2
scorecard_aggregate	6.3
high_risk_jurisdiction_cap	34
high_risk_jurisdiction_multiplier	75
security_posture_after_multiplier	47
security_posture_before_jurisdiction	63

Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized. High-Risk Jurisdiction Policy applies a 75% multiplier and gives Security posture an At risk ceiling of 34.

Dependency advisories

74Good

How it's scored

26.6/35	Direct dependencies free of known advisories — 1 affected: diskcache 5.6.3 (moderate)
10.2/25	Indirect dependencies free of known advisories — 1 affected: certifi 2026.10.18 (high 7.4)
37.2/40	No advisories left outstanding — 1 advisory-carrying package(s) unaddressed past 90 days; oldest published 251 days ago

Inputs used

source	osv
advisories	4
affected_packages	2
assessed_packages	16
unassessed_packages	0
affected_by_severity	high 1, moderate 1
direct_affected_packages	1

Matched the pypi:pySigma@2.0.1 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.

84Excellent · 4% of overall

Agent context & guidance

83Excellent

How it's scored

45/45	Agent instructions — .github/copilot-instructions.md, .github/instructions/docs.md, .github/instructions/sigma.md, .github/instructions/tests.md
0/15	Machine-readable docs (llms.txt)
37.9/40	Legible commit history — 69 of 97 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.711
agent_instruction_files	.github/copilot-instructions.md, .github/instructions/docs.md, .github/instructions/sigma.md, .github/instructions/tests.md
agent_instruction_max_bytes	3,187

Verify loop (build / test / typecheck)

87Excellent

How it's scored

18/18	One-command bootstrap — docs/Makefile
22/22	Automated tests
11/11	Lint / format config — pyproject.toml ([tool.black])
11/11	Static type checking — mypy.ini, sigma/py.typed
10/10	Reproducible environment — lockfile
10/10	Demonstrated agent practice — 27 of the last 100 commits agent-authored or agent-credited
5/8	Automated maintenance — dependency automation configured, none observed in the sampled commits
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	poetry.lock
has_dockerfile	no
typed_language	no
bootstrap_files	docs/Makefile
has_devcontainer	no
has_linter_config	yes
typecheck_configs	mypy.ini, sigma/py.typed
agent_commit_share	0.27
toolchain_manifests	—
dependency_bot_commit_share	0

Code legibility for models

80Excellent

How it's scored

27/45	Type-checkable code — Python with type-check config (mypy.ini, sigma/py.typed)
53/55	Manageable file sizes — 4/108 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	140,470
source_files_sampled	108
oversized_source_files	4

Key facts

601GitHub stars

68contributors

502commits, last 12 months

2days since last push

100releases

1bus factor

2open 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 ★ / 160 ⇿

0Stars

160Forks

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.

Major 2Minor 13Patch 83

Each point covers 5 days.

OpenSSF Scorecard 6.3 / 10

6.3aggregate

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 00:16 UTC

10	Binary-Artifacts	no binaries found in the repo
n/a	Branch-Protection	internal 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
10	CI-Tests	8 out of 8 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 2/15 approved changesets -- score normalized to 1
10	Contributors	project has 17 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 5 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
10	Security-Policy	security policy file detected
n/a	Signed-Releases	no releases found
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
8	Vulnerabilities	2 existing vulnerabilities detected

Direct dependencies 10

Registry	Package	Version constraint	Manifest
PyPI	packaging	`^26.3`	pyproject.toml
PyPI	pyparsing	`^3.2.5`	pyproject.toml
PyPI	pyyaml	`^6.0.3`	pyproject.toml
PyPI	requests	`^2.32.5`	pyproject.toml
PyPI	jinja2	`^3.1.6`	pyproject.toml
PyPI	types-pyyaml	`^6.0.12.20260815`	pyproject.toml
PyPI	diskcache	`^5.6.3`	pyproject.toml
PyPI	diskcache-stubs	`>=5.6.3`	pyproject.toml
PyPI	jq	`^1.12`	pyproject.toml
PyPI	google-re2	`>=1.0`	pyproject.toml

All dependencies 64

Full resolved dependency set from the GitHub dependency graph: 10 direct and 54 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	diskcache	`5.6.3`	direct
PyPI	diskcache-stubs	`5.6.3.6.20240818`	direct
PyPI	google-re2	`1.1.20251105`	direct
PyPI	jinja2	`3.1.6`	direct
PyPI	jq	`1.12.0`	direct
PyPI	packaging	`26.3`	direct
PyPI	pyparsing	`3.3.3`	direct
PyPI	pyyaml	`6.0.3`	direct
PyPI	requests	`2.34.2`	direct
PyPI	types-pyyaml	`6.0.12.20260906`	direct
PyPI	alabaster	`1.0.0`	indirect
PyPI	ast-serialize	`0.12.1`	indirect
PyPI	astroid	`4.3.3`	indirect
PyPI	babel	`2.18.0`	indirect
PyPI	black	`26.5.1`	indirect
PyPI	certifi	`2026.7.22`	indirect
PyPI	cfgv	`3.5.0`	indirect
PyPI	charset-normalizer	`3.5.2`	indirect
PyPI	click	`8.5.0`	indirect
PyPI	colorama	`0.4.6`	indirect
PyPI	coverage	`7.16.2`	indirect
PyPI	defusedxml	`0.7.1`	indirect
PyPI	dill	`0.4.1`	indirect
PyPI	distlib	`0.4.3`	indirect
PyPI	docutils	`0.21.2`	indirect
PyPI	filelock	`4.0.10`	indirect
PyPI	identify	`2.6.20`	indirect
PyPI	idna	`3.20`	indirect
PyPI	imagesize	`2.0.1`	indirect
PyPI	iniconfig	`2.3.0`	indirect
PyPI	isort	`9.0.2`	indirect
PyPI	librt	`0.16.0`	indirect
PyPI	markupsafe	`3.0.4`	indirect
PyPI	mccabe	`0.7.0`	indirect
PyPI	mypy	`2.4.0`	indirect
PyPI	mypy-extensions	`1.1.0`	indirect
PyPI	nodeenv	`1.11.0`	indirect
PyPI	pathspec	`1.1.1`	indirect
PyPI	pip	`26.2.1`	indirect
PyPI	platformdirs	`4.12.3`	indirect
PyPI	pluggy	`1.6.0`	indirect
PyPI	pre-commit	`4.6.2`	indirect
PyPI	pygments	`2.21.0`	indirect
PyPI	pylint	`4.1.2`	indirect
PyPI	pytest	`9.1.1`	indirect
PyPI	pytest-cov	`7.1.0`	indirect
PyPI	pytest-mypy	`1.0.1`	indirect
PyPI	python-discovery	`1.6.1`	indirect
PyPI	pytokens	`0.4.1`	indirect
PyPI	roman-numerals	`4.1.0`	indirect
PyPI	roman-numerals-py	`4.1.0`	indirect
PyPI	snowballstemmer	`3.1.1`	indirect
PyPI	sphinx	`8.2.3`	indirect
PyPI	sphinxcontrib-applehelp	`2.0.0`	indirect
PyPI	sphinxcontrib-devhelp	`2.0.0`	indirect
PyPI	sphinxcontrib-htmlhelp	`2.1.0`	indirect
PyPI	sphinxcontrib-jsmath	`1.0.1`	indirect
PyPI	sphinxcontrib-qthelp	`2.0.0`	indirect
PyPI	sphinxcontrib-serializinghtml	`2.0.0`	indirect
PyPI	tomlkit	`0.15.1`	indirect
PyPI	types-requests	`2.33.0.20260906`	indirect
PyPI	typing-extensions	`4.16.0`	indirect
PyPI	urllib3	`2.8.0`	indirect
PyPI	virtualenv	`21.14.5`	indirect

Dependency advisories 2

Installing pypi:pySigma@2.0.1 pulls in 16 packages, direct and transitive: 2 carry known advisories, of which 1 are direct dependencies.

Package	Version	Relation	Severity	Advisories	Fixed in
certifi	`2026.10.18`	indirect	high	2	`2026.10.19`
diskcache	`5.6.3`	direct	moderate	2	—

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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