Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-14 12:02 UTC

olivierfriard / BORIS

Behavioral Observation Research Interactive Software

PythonGPL-3.0★ 253 stars⑂ 57 forkssince Jan 2014View on GitHub ↗
KindCommand-line toolLibraryDesktop applicationhow this is determined

olivierfriard/BORIS holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Vitality (93/100) and lowest on AI Readiness (36/100). It was last updated today. 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

Olivier FriardPersonal account
63 followers40 public repossince Jan 2014University of Torino

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 publish
PyPIboris-behav-obs10.0.12,6352140 days ago

Metrics by category

Vitality

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

93Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
24.9/36Commit cadence — 36/52 weeks with commits
18/18Commit volume — 357 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year357
human_commit_share1
days_since_last_push0
active_weeks_last_year36

Release discipline

98Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 42 days ago
27/27Release cadence — a release every ~14.1 days
8/10OpenSSF Scorecard: Signed-Releases — 5 out of the last 5 releases have a total of 5 signed artifacts.
Inputs used
releases_count100
latest_release_tagv9.15.0
releases_from_tagsno
days_since_latest_release42
mean_days_between_releases14.1

Community & Adoption

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

56Moderate · 17% of overall
How it's scored
39/60Stars — 253 stars
14.6/25Forks — 57 forks
6.5/15Watchers — 16 watchers
Inputs used
forks57
stars253
watchers16
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (GPL-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_badges10
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno
How it's scored
45.6/80Monthly downloads — 2,635 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesboris-behav-obs
dependents—
ecosystemspypi
total_downloads—
monthly_downloads2,635
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
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.2/22.5Commit distribution — top contributor authored 99% of commits
13.5/13.5Contributor breadth — 15 contributors
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled15
top_contributor_share0.991
How it's scored
33.5/42Issue resolution — 80% of issues closed
17.2/30PR acceptance — 23/40 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_prs23
open_issues177
closed_issues695
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.797
closed_unmerged_prs17
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
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
13/25Owner reach — 63 followers of olivierfriard
23.7/25Track record — 40 public repos, account ~12 yr old
Inputs used
followers63
owner_typeUser
is_verified—
owner_loginolivierfriard
public_repos40
account_age_days4,641
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 0 days ago
20/20Version history — 214 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesboris-behav-obs
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

78Good · 19% of overall
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-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_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://www.boris.unito.it
10/10Repository description
10/10Topics — 5 topics
10/10Wiki
Inputs used
topicsvideo-coding, behavior-analysis, animal-behavior, animal-behaviour, ethology
has_wikiyes
homepagehttps://www.boris.unito.it
docs_sitehttps://www.boris.unito.it
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

57Moderate · 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 — no data
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
1.5/2.5Contributors — project has 2 contributing companies or organizations -- score normalized to 6
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 8 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 — no SAST tool detected
0/5Security-Policy — security policy file not detected
6/7.5Signed-Releases — 5 out of the last 5 releases have a total of 5 signed artifacts.
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_aggregate5.1
Excluded from scoring (no data or not applicable): CI-Tests, Packaging. Remaining weights renormalized.
How it's scored
19.9/35Direct dependencies free of known advisories — 1 affected: tablib 3.8.0 (moderate 5.4)
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
40/40No advisories left outstanding — no advisory has been public longer than 90 days
Inputs used
sourceosv
advisories2
affected_packages1
assessed_packages12
unassessed_packages3
affected_by_severitymoderate 1
direct_affected_packages1
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories. Remaining weights renormalized. Matched 12 resolved dependencies against OSV. 3 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

36Weak · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
4.3/40Legible commit history — 8 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.08
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — justfile, tests/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
0/10Reproducible environment
4/10Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
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_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesjustfile, tests/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.02
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
51.6/55Manageable file sizes — 9/147 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes675,008
source_files_sampled147
oversized_source_files9

Key facts

253GitHub stars
15contributors
357commits, last 12 months
0days since last push
100releases
1bus factor
177open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:boris-behav-obs@10.0.1; advisories assessed against the repository dependency graph instead

More detail

Star and fork history 0 ★ / 57 ⇿
0Stars
57Forks
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.

01020304050605722016-062021-082026-09
Major 1Minor 4Patch 81

Each point covers 10 days.

OpenSSF Scorecard 5.1 / 10
5.1aggregate

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 · 2026-10-14 12:02 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 8 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
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
8Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 14
RegistryPackageVersion constraintManifest
PyPIexifread==3.5.1pyproject.toml
PyPInumpy==2.3.2pyproject.toml
PyPImatplotlib==3.10.5pyproject.toml
PyPIpandas==2.3.2pyproject.toml
PyPItablib==3.8.0pyproject.toml
PyPIpyreadr==0.5.3pyproject.toml
PyPIpyside6==6.10pyproject.toml
PyPIpyobjc-framework-Cocoa—pyproject.toml
PyPIhachoir==3.3.0pyproject.toml
PyPIscipy==1.16.1pyproject.toml
PyPIscikit-learn==1.7.1pyproject.toml
PyPIpyqtgraph>=0.14.0pyproject.toml
PyPIpytest-cov==7.1.0pyproject.toml
PyPIpytest==9.1.1pyproject.toml
All dependencies 15

Full resolved dependency set from the GitHub dependency graph: 13 direct and 2 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIexifread3.5.1direct
PyPIhachoir3.3.0direct
PyPImatplotlib3.10.5direct
PyPInumpy2.3.2direct
PyPIpandas2.3.2direct
PyPIpyqtgraph—direct
PyPIpyreadr0.5.3direct
PyPIpyside66.10direct
PyPIpytest9.1.1direct
PyPIpytest-cov7.1.0direct
PyPIscikit-learn1.7.1direct
PyPIscipy1.16.1direct
PyPItablib3.8.0direct
PyPIrpy2—indirect
PyPIsetuptools—indirect
Dependency advisories 1

This repository publishes no package the index resolves, so its own dependency graph was assessed — 12 packages, which also include development and test pins that never ship: 1 carry known advisories, of which 1 are direct. 3 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
tablib3.8.0directmoderate23.10.0

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.