Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-10 05:23 UTC

givasile / effector

Effector - a Python package for global and regional effect methods

Jupyter NotebookMIT★ 121 stars⑂ 2 forkssince Mar 2022View on GitHub ↗
KindLibraryhow this is determined
#explainability#iml#xai

givasile/effector holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Engineering Quality (90/100) and lowest on Community & Adoption (53/100). It was last updated 29 days ago. A single contributor accounts for most of its recent work.

78
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.

78
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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

Vasilis GkolemisPersonal account
21 followers34 public repossince Jun 2016"Athena" Research and Innovation Center

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIeffector0.5.03,0135056 days agoexplainabilityinterpretabilitymachine-learningdeep-learningregional-xaifeature-effect

Metrics by category

Vitality

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

70Good · 21% of overall

Development activity

56Moderate
How it's scored
28.8/36Push recency — last push 29 days ago
2.8/36Commit cadence — 4/52 weeks with commits
14.2/18Commit volume — 37 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year37
human_commit_share1
days_since_last_push29
active_weeks_last_year4

Release discipline

92Excellent
How it's scored
27/27Ships releases — 7 releases published
36/36Release recency — latest release 56 days ago
19.8/27Release cadence — a release every ~85.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count7
latest_release_tagv0.5.0
releases_from_tagsno
days_since_latest_release56
mean_days_between_releases85.5
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?

53Moderate · 17% of overall

Popularity & adoption

35Weak
How it's scored
33.7/60Stars — 121 stars
0/25Forks — 2 forks
1.7/15Watchers — 3 watchers
Inputs used
forks2
stars121
watchers3
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesbadge.fury.io, codecov.io, github.com, shields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

58Moderate
How it's scored
46.4/80Monthly downloads — 3,013 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageseffector
dependents—
ecosystemspypi
total_downloads—
monthly_downloads3,013
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?

58Moderate · 23% of overall

Maintainer resilience (bus factor)

22At Risk
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
1.6/22.5Commit distribution — top contributor authored 93% of commits
8.1/13.5Contributor breadth — 6 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled6
top_contributor_share0.928

Issue & PR responsiveness

71Good
How it's scored
42/42Issue resolution — 100% of issues closed
20.2/30PR acceptance — 41/61 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs41
open_issues0
closed_issues3
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio1
closed_unmerged_prs20
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.

Ownership & stewardship

54Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
9.7/25Owner reach — 21 followers of givasile
23.2/25Track record — 34 public repos, account ~10 yr old
Inputs used
followers21
owner_typeUser
is_verified—
owner_logingivasile
public_repos34
account_age_days3,736
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 56 days ago
20/20Version history — 50 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageseffector
ecosystemspypi
any_deprecatedno
min_days_since_publish56

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall

Engineering practices

84Excellent
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 29 out of 29 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://xai-effector.github.io/
10/10Repository description
10/10Topics — 3 topics
10/10Wiki
Inputs used
topicsexplainability, iml, xai
has_wikiyes
homepagehttps://xai-effector.github.io/
docs_sitehttps://xai-effector.github.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

65Good · 16% of overall

Security posture

56Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.5/2.5CI-Tests — 29 out of 29 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
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
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
0/7.5Vulnerabilities — 17 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.6
Excluded from scoring (no data or not applicable): Branch-Protection, 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_packages13
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:effector@0.5.0 runtime dependency closure — what installing the published package pulls in — 13 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.

58Moderate · 4% of overall

Agent context & guidance

28At Risk
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
27.7/40Legible commit history — 52 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.52
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

66Good
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Jupyter Notebook without a type-check config
54.3/55Manageable file sizes — 1/74 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes76,354
source_files_sampled74
oversized_source_files1

Machine-readable interfaces

100Exceptional
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

121GitHub stars
6contributors
37commits, last 12 months
29days since last push
7releases
1bus factor
0open 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 ★ / 2 ⇿
0Stars
2Forks
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.

111222212024-052024-112025-05
Major 0Minor 0Patch 3
OpenSSF Scorecard 5.6 / 10
5.6aggregate

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 · 2026-09-10 05:22 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal 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
10CI-Tests29 out of 29 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow 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
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
0Vulnerabilities17 existing vulnerabilities detected
Direct dependencies 4
RegistryPackageVersion constraintManifest
PyPImatplotlib—pyproject.toml
PyPInumpy—pyproject.toml
PyPIscipy—pyproject.toml
PyPItqdm—pyproject.toml
All dependencies 275

Full resolved dependency set from the GitHub dependency graph: 13 direct and 262 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPImatplotlib—direct
PyPImatplotlib3.10.9direct
PyPImatplotlib3.11.0direct
PyPInumpy—direct
PyPInumpy2.2.6direct
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PyPIscipy—direct
PyPIscipy1.15.3direct
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PyPItqdm—direct
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PyPItyper0.25.1indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2026.2indirect
PyPIucimlrepo—indirect
PyPIucimlrepo0.0.7indirect
PyPIuri-template1.3.0indirect
PyPIurllib32.7.0indirect
PyPIwatchdog6.0.0indirect
PyPIwcwidth0.8.2indirect
PyPIwebcolors25.10.0indirect
PyPIwebencodings0.5.1indirect
PyPIwebsocket-client1.9.0indirect
PyPIwheel0.47.0indirect
PyPIwidgetsnbextension4.0.15indirect
PyPIwrapt2.2.2indirect
PyPIxmltodict1.0.4indirect
Dependency advisories 0

Installing pypi:effector@0.5.0 pulls in 13 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.

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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.

How one result sits in the wider record: aggregate statistics — PyPI.