Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-18 19:07 UTC

Fabric-Development / fabric

The next-generation framework for building desktop widgets using Python

PythonAGPL-3.0★ 1,390 stars⑂ 46 forkssince Jan 2024View on GitHub ↗
KindCommand-line toolhow this is determined

Fabric-Development/fabric holds a health index of 73 out of 100, placing it in the Good band. It scores highest on Engineering Quality (74/100) and lowest on Security (56/100). It was last updated 22 days ago. A single contributor accounts for most of its recent work.

73
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.

73
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 64 is calibrated to 73 on the published index scale (record calibration 2026-08-02).

Ownership

Fabric DevelopmentOrganization
44 followers7 public repossince Oct 2023

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIfabricpoints to another repo — not scored3.2.37,402,988130195 days ago

Metrics by category

Vitality

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

71Good · 21% of overall
How it's scored
28.8/36Push recency — last push 22 days ago
9.7/36Commit cadence — 14/52 weeks with commits
13.3/18Commit volume — 29 commits in the last year
10/10OpenSSF Scorecard: Maintained — 20 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year29
human_commit_share1
days_since_last_push22
active_weeks_last_year14
How it's scored
27/27Ships releases — 3 releases published
36/36Release recency — latest release 46 days ago
12.6/27Release cadence — a release every ~330 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count3
latest_release_tagv0.0.3-pre-alpha
releases_from_tagsno
days_since_latest_release46
mean_days_between_releases330
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?

61Moderate · 17% of overall
How it's scored
51/60Stars — 1,390 stars
13.8/25Forks — 46 forks
6.2/15Watchers — 14 watchers
Inputs used
forks46
stars1,390
watchers14
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (AGPL-3.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno

Sustainability & Governance

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

57Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
5.7/22.5Commit distribution — top contributor authored 75% of commits
13.5/13.5Contributor breadth — 30 contributors
10/10OpenSSF Scorecard: Contributors — project has 25 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled30
top_contributor_share0.747
How it's scored
40.6/42Issue resolution — 97% of issues closed
22.5/30PR acceptance — 75/100 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
7.5/15OpenSSF Scorecard: Code-Review — Found 17/30 approved changesets -- score normalized to 5
Inputs used
merged_prs75
open_issues2
closed_issues58
prs_merged_7d0
prs_decided_7d0
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio0.967
closed_unmerged_prs25
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
11.9/25Owner reach — 44 followers of Fabric-Development
12.7/25Track record — 7 public repos, account ~3 yr old
Inputs used
followers44
owner_typeOrganization
is_verifiedno
owner_loginFabric-Development
public_repos7
account_age_days1,111

Engineering Quality

Are baseline engineering and documentation practices in place?

74Good · 19% of overall
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — ruff.toml
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 0 out of 18 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://wiki.ffpy.org
10/10Repository description
10/10Topics — 8 topics
0/10Wiki
Inputs used
topicsdesktop-widgets, gtk, gtk3, status-bar, wayland, widget, widgets, x11
has_wikino
homepagehttps://wiki.ffpy.org
docs_sitehttps://wiki.ffpy.org
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

56Moderate · 16% of overall
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
0/2.5CI-Tests — 0 out of 18 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
3.8/7.5Code-Review — Found 17/30 approved changesets -- score normalized to 5
2.5/2.5Contributors — project has 25 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 20 commit(s) and 0 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
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
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
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.5
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_packages10
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:fabric@3.2.3 runtime dependency closure — what installing the published package pulls in — 10 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.

68Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 97 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.97
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — ruff.toml
11/11Static type checking — fabric/py.typed
10/10Reproducible environment — Nix
0/10Demonstrated agent practice — no agent-authored commits among the last 100
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_nixyes
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsfabric/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (fabric/py.typed)
55/55Manageable file sizes — 0/79 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes35,073
source_files_sampled79
oversized_source_files0
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 — examples
Inputs used
example_dirsexamples
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

1,390GitHub stars
30contributors
29commits, last 12 months
22days since last push
3releases
1bus factor
2open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'fabric' points at a different repository (https://github.com/fabric/fabric); excluded from ecosystem scoring

More detail

Star and fork history 0 ★ / 46 ⇿
0Stars
46Forks
3Releases

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.

010203040504432024-012025-052026-09
Major 0Minor 0Patch 2

Each point covers 3 days.

OpenSSF Scorecard 4.5 / 10
4.5aggregate

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 · 2026-10-18 19:07 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
0CI-Tests0 out of 18 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
5Code-ReviewFound 17/30 approved changesets -- score normalized to 5
10Contributorsproject has 25 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained20 commit(s) and 0 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
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 4
RegistryPackageVersion constraintManifest
PyPIclick—pyproject.toml
PyPIloguru—pyproject.toml
PyPIpycairo—pyproject.toml
PyPIPyGObject==3.50.0pyproject.toml
All dependencies 11

Full resolved dependency set from the GitHub dependency graph: 4 direct and 7 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIclick—direct
PyPIloguru—direct
PyPIpycairo—direct
PyPIpygobject3.50.0direct
PyPIpsutil—indirect
PyPIpygobject-stubs—indirect
PyPIrecommonmark—indirect
PyPIsphinx8.1.3indirect
PyPIsphinx-markdown-builder0.6.7indirect
PyPIsphinx-toolbox3.8.1indirect
PyPIsphinxawesome-theme—indirect
Dependency advisories 0

Installing pypi:fabric@3.2.3 pulls in 10 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.

The message is kept through sign-in.

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.