Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-09 11:04 UTC

silx-kit / fabio

I/O library for images produced by 2D X-ray detector

PythonCustom license★ 66 stars⑂ 55 forkssince Mar 2013View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

silx-kit/fabio holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (95/100) and lowest on Security (40/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

80
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.

80
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 68 is calibrated to 80 on the published index scale (record calibration 2026-08-02).

Ownership

37 followers18 public repossince Sep 2015

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIfabio2026.6.075,1003181 days ago

Metrics by category

Vitality

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

77Good · 21% of overall
How it's scored
36/36Push recency — last push 1 days ago
10.4/36Commit cadence — 15/52 weeks with commits
18/18Commit volume — 206 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year206
human_commit_share0.99
days_since_last_push1
active_weeks_last_year15
How it's scored
27/27Ships releases — 26 releases published
36/36Release recency — latest release 81 days ago
12.6/27Release cadence — a release every ~142.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count26
latest_release_tagv2026.06
releases_from_tagsno
days_since_latest_release81
mean_days_between_releases142.8

Community & Adoption

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

54Moderate · 17% of overall
How it's scored
29.4/60Stars — 66 stars
14.4/25Forks — 55 forks
2.7/15Watchers — 4 watchers
Inputs used
forks55
stars66
watchers4
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
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
How it's scored
65/80Monthly downloads — 75,100 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesfabio
dependents—
ecosystemspypi
total_downloads—
monthly_downloads75,100
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
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
6.2/22.5Commit distribution — top contributor authored 72% of commits
13.5/13.5Contributor breadth — 36 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled36
top_contributor_share0.725
How it's scored
37.9/42Issue resolution — 90% of issues closed
27.4/30PR acceptance — 322/352 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs322
open_issues30
closed_issues280
prs_merged_7d0
prs_decided_7d0
prs_merged_30d3
prs_decided_30d3
issue_closed_ratio0.903
closed_unmerged_prs30
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
11.4/25Owner reach — 37 followers of silx-kit
21.3/25Track record — 18 public repos, account ~10 yr old
Inputs used
followers37
owner_typeOrganization
is_verifiedno
owner_loginsilx-kit
public_repos18
account_age_days3,999

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 81 days ago
20/20Version history — 31 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesfabio
ecosystemspypi
any_deprecatedno
min_days_since_publish81

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — http://www.silx.org/doc/fabio/latest/
10/10Repository description
10/10Topics — 3 topics
10/10Wiki
Inputs used
topicspython, detector, science
has_wikiyes
homepage—
docs_sitehttp://www.silx.org/doc/fabio/latest/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

40Weak · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsci/requirements_appveyor.txt, ci/requirements_gh.txt, ci/requirements_rtd.txt, pyproject.toml, requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configyes

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_packages6
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:fabio@2026.6.0 runtime dependency closure — what installing the published package pulls in — 6 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.

53Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
11.9/40Legible commit history — 22 of 99 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.222
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — doc/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 1 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesdoc/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.01
How it's scored
0/45Type-checkable code — Python without a type-check config
54.6/55Manageable file sizes — 1/150 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes68,039
source_files_sampled150
oversized_source_files1
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 — notebooks
Inputs used
example_dirsnotebooks
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

66GitHub stars
36contributors
206commits, last 12 months
1days since last push
26releases
1bus factor
30open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'fabio' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • pypi download figures for fabio carried forward from the previous scan (stats endpoint unavailable this scan)
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/09 11:01:50 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 55 ⇿
0Stars
55Forks
25Releases

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.

01020304050605422013-112020-032026-06
Major 0Minor 15Patch 5

Each point covers 12 days.

Direct dependencies 6
RegistryPackageVersion constraintManifest
PyPInumpy—pyproject.toml
PyPIh5py—pyproject.toml
PyPIhdf5plugin—pyproject.toml
PyPIlxml—pyproject.toml
PyPIpillow—pyproject.toml
PyPIfilelock—pyproject.toml
All dependencies 23

Full resolved dependency set from the GitHub dependency graph: 6 direct and 17 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIfilelock—direct
PyPIh5py—direct
PyPIhdf5plugin—direct
PyPIlxml—direct
PyPInumpy—direct
PyPIpillow—direct
PyPIbuild—indirect
PyPIcython—indirect
PyPImatplotlib—indirect
PyPImeson—indirect
PyPImeson-python—indirect
PyPInbsphinx—indirect
PyPIninja—indirect
PyPIpydata-sphinx-theme—indirect
PyPIpyproject-metadata—indirect
PyPIpyqt6—indirect
PyPIpyside6—indirect
PyPIqtpy—indirect
PyPIsphinx—indirect
PyPIsphinx-rtd-theme—indirect
PyPIsphinxcontrib-programoutput—indirect
PyPItomli—indirect
PyPIwheel—indirect
Dependency advisories 0

Installing pypi:fabio@2026.6.0 pulls in 6 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.