Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-10 08:13 UTC

databricks-industry-solutions / many-model-forecasting

Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.

Python · Jupyter NotebookCustom license★ 103 stars⑂ 45 forkssince Oct 2022View on GitHub ↗
KindCommand-line toolLibraryhow this is determined
#forecasting#xindustry

databricks-industry-solutions/many-model-forecasting holds a health index of 73 out of 100, placing it in the Good band. It scores highest on Vitality (92/100) and lowest on Engineering Quality (53/100). It was last updated today. 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

Databricks Industry SolutionsOrganization
862 followers224 public repossince May 2022

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIskill-test0.4.01442136 days ago

Metrics by category

Vitality

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

92Excellent · 21% of overall

Development activity

86Excellent
How it's scored
36/36Push recency — last push 0 days ago
22.2/36Commit cadence — 32/52 weeks with commits
18/18Commit volume — 312 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_year312
human_commit_share0.84
days_since_last_push0
active_weeks_last_year32

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 9 releases published
36/36Release recency — latest release 60 days ago
27/27Release cadence — a release every ~18.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count9
latest_release_tagv0.1.7
releases_from_tagsno
days_since_latest_release60
mean_days_between_releases18.6
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?

57Moderate · 17% of overall

Popularity & adoption

50Moderate
How it's scored
32.6/60Stars — 103 stars
13.7/25Forks — 45 forks
3.9/15Watchers — 6 watchers
Inputs used
forks45
stars103
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

64Moderate
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
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?

56Moderate · 23% of overall

Maintainer resilience (bus factor)

29At Risk
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
4.4/22.5Commit distribution — top contributor authored 80% of commits
9.5/13.5Contributor breadth — 7 contributors
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled7
top_contributor_share0.803

Issue & PR responsiveness

67Good
How it's scored
30.5/42Issue resolution — 73% of issues closed
28.2/30PR acceptance — 184/196 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/3 approved changesets -- score normalized to 0
Inputs used
merged_prs184
open_issues3
closed_issues8
prs_merged_7d0
prs_decided_7d0
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio0.727
closed_unmerged_prs12
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

73Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
21.1/25Owner reach — 862 followers of databricks-industry-solutions
21.6/25Track record — 224 public repos, account ~4 yr old
Inputs used
followers862
owner_typeOrganization
is_verifiedno
owner_logindatabricks-industry-solutions
public_repos224
account_age_days1,563

Package maintenance

61Moderate
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
4/35Publish recency — latest publish 2,136 days ago
12/20Version history — 4 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesskill-test
ecosystemspypi
any_deprecatedno
min_days_since_publish2,136

Engineering Quality

Are baseline engineering and documentation practices in place?

53Moderate · 19% of overall

Engineering practices

48Weak
How it's scored
24/24CI workflows — 5 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 0 out of 14 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

60Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 2 topics
10/10Wiki
Inputs used
topicsforecasting, xindustry
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

61Moderate · 16% of overall

Security posture

51Moderate
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 14 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
0/7.5Code-Review — Found 0/3 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
2.2/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
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
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
6/7.5Vulnerabilities — 2 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): 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_packages8
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:skill-test@0.4.0 runtime dependency closure — what installing the published package pulls in — 8 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.

69Good · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.databricks.com/llms.txt)
40/40Legible commit history — 79 of 84 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.databricks.com/llms.txt
legible_history_share0.94
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes1,714

Verify loop (build / test / typecheck)

40Weak
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — devcontainer, Dockerfile, lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 16 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontaineryes
has_linter_configno
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.16

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
54.4/55Manageable file sizes — 1/99 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes63,634
source_files_sampled99
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 — examples, notebooks
Inputs used
example_dirsexamples, notebooks
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

103GitHub stars
7contributors
312commits, last 12 months
0days since last push
9releases
1bus factor
3open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'mmf_sa' from its registry

More detail

Star and fork history 0 ★ / 45 ⇿
0Stars
45Forks
9Releases

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.

010203040504432023-022024-112026-08
Major 1Minor 1Patch 7

Each point covers 4 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 · 2026-09-10 08:13 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
0CI-Tests0 out of 14 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/3 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
9Licenselicense file detected
10Maintained30 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
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
8Vulnerabilities2 existing vulnerabilities detected
Direct dependencies 6
RegistryPackageVersion constraintManifest
PyPIomegaconf==2.3.0pyproject.toml
PyPIsktime==0.40.1pyproject.toml
PyPImlflow>=3.15.0pyproject.toml
PyPIpyyaml>=6.0skills/.test/pyproject.toml
PyPImlflow>=3.12.0skills/.test/pyproject.toml
PyPIpython-dotenv>=1.2.2skills/.test/pyproject.toml
All dependencies 183

Full resolved dependency set from the GitHub dependency graph: 6 direct and 177 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIomegaconf2.3.0direct
PyPIpython-dotenv1.2.2direct
PyPIpyyaml6.0.3direct
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PyPIaiohappyeyeballs2.6.2indirect
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PyPIaiosignal1.4.0indirect
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PyPIcachetools6.2.6indirect
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PyPIcharset-normalizer3.4.4indirect
PyPIchronos-forecasting2.2.2indirect
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PyPIpandas—indirect
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PyPIpy4j0.10.9.9indirect
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PyPIpydantic2.12.5indirect
PyPIpydantic-core2.41.5indirect
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PyPIpyparsing3.3.2indirect
PyPIpyspark3.4.4indirect
PyPIpytest9.0.3indirect
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PyPIs3transfer0.16.0indirect
PyPIscikit-learn1.7.2indirect
PyPIscikit-learn1.8.0indirect
PyPIscipy—indirect
PyPIscipy1.15.3indirect
PyPIscipy1.17.0indirect
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PyPIsix1.17.0indirect
PyPIskill-test0.1.0indirect
PyPIskops0.13.0indirect
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PyPItimesfm—indirect
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PyPItzdata2025.3indirect
PyPIurllib32.7.0indirect
PyPIutilsforecast0.2.15indirect
PyPIuuid-utils0.14.0indirect
PyPIuvicorn0.40.0indirect
PyPIwaitress3.0.2indirect
PyPIwcwidth0.5.3indirect
PyPIwebsockets16.0indirect
PyPIwerkzeug3.1.6indirect
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Dependency advisories 0

Installing pypi:skill-test@0.4.0 pulls in 8 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

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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.