Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-24 04:59 UTC

cogilab / Random2

Implementation of "Brain-Inspired Warm-Up Training with Random Noise for Uncertainty Calibration" (Cheon and Paik, Nat. Mach. Intell., 2026)

PythonMIT★ 14 stars⑂ 5 forkssince Feb 2026archivedView on GitHub ↗

cogilab/Random2 holds a health index of 7 out of 100, placing it in the Critical band. It scores highest on Community & Adoption (36/100) and lowest on Security (1/100). The repository is archived, so no further maintenance is expected.

7
overall / 100
Critical

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.

7
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 24 is calibrated to 18 on the published index scale (record calibration 2026-08-02).

Ownership

1 follower15 public repossince Oct 2024

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIRandom2points to another repo — not scored1.0.2103,9583979 days agoroman

Metrics by category

Vitality

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

30At Risk · 21% of overall
How it's scored
3.6/36Push recencylast push 182 days ago
0.7/36Commit cadence1/52 weeks with commits
2.7/18Commit volume1 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year1
human_commit_share
days_since_last_push182
active_weeks_last_year1
How it's scored
27/27Ships releases1 releases published
16.2/36Release recencylatest release 182 days ago
12.6/27Release cadencecadence unknown (single release)
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count1
latest_release_tagv1.0.0
releases_from_tagsno
days_since_latest_release182
mean_days_between_releases

Community & Adoption

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

36Weak · 17% of overall
How it's scored
18.1/60Stars14 stars
5/25Forks5 forks
0/15Watchers0 watchers
Inputs used
forks5
stars14
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges1
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

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

27At Risk · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
0/22.5Commit distributiontop contributor authored 100% of commits
1.4/13.5Contributor breadth1 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
2.2/25Owner reach1 followers of cogilab
12.4/25Track record15 public repos, account ~1 yr old
Inputs used
followers1
owner_typeOrganization
is_verifiedno
owner_logincogilab
public_repos15
account_age_days668

Engineering Quality

Are baseline engineering and documentation practices in place?

27At Risk · 19% of overall
How it's scored
0/24CI workflows
0/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_cino
has_testsno
has_editorconfigno
has_linter_configno
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage sitehttps://doi.org/10.5281/zenodo.18734398
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics
has_wikiyes
homepagehttps://doi.org/10.5281/zenodo.18734398
docs_sitehttps://doi.org/10.5281/zenodo.18734398
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

1Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestspyproject.toml, requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

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.

11Critical · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit historyno data
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
0/18One-command bootstrap
0/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsno
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/51 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes28,452
source_files_sampled51
oversized_source_files0

Key facts

14GitHub stars
1contributors
1commits, last 12 months
182days since last push
1releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'Random2' points at a different repository (http://pypi.python.org/pypi/random2); excluded from ecosystem scoring
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 5 ⇿
0Stars
5Forks

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.

12345512026-052026-062026-07
Direct dependencies 11
RegistryPackageVersion constraintManifest
PyPInumpypyproject.toml
PyPImatplotlibpyproject.toml
PyPIpep8pyproject.toml
PyPItorchpyproject.toml
PyPItorchvisionpyproject.toml
PyPItorchaudiopyproject.toml
PyPIipykernelpyproject.toml
PyPIopencv-pythonpyproject.toml
PyPIlxmlpyproject.toml
PyPIscikit-learnpyproject.toml
PyPIwandb>=0.24.1pyproject.toml
All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

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 statisticsPyPI.