Public record
Software health reportschema 0.26.0 · metrics 2.10.0 · 2026-07-22 05:14 UTC

Whatsonyourmind / modelforge

Bulge-tier Excel financial model factory — every cell live-formulated, every number traceable

PythonCustom license★ 1 star⑂ 0 forkssince Apr 2026View on GitHub ↗
KindLibraryMCP serverCommand-line toolhow this is determined
#credit-risk#excel#financial-modeling#llm-tools#mcp#openpyxl#ai-agents#audit-trail#data-lineage#dcf#lbo#mcp-server#valuation#yaml-to-excel

Whatsonyourmind/modelforge holds a health index of 53 out of 100, placing it in the Moderate band. It scores highest on Vitality (75/100) and lowest on Community & Adoption (21/100). It was last updated 22 days ago. A single contributor accounts for most of its recent work.

53
overall / 100
Moderate

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.

53
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 52 is calibrated to 53 on the published index scale (record calibration 2026-08-02).

Ownership

LukaPersonal account
12 followers20 public repossince Dec 2025

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
PyPImodelforge-finance0.12.0571729 days ago

Metrics by category

Vitality

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

75Good · 21% of overall

Development activity

62Moderate
How it's scored
28.8/36Push recency — last push 22 days ago
5.5/36Commit cadence — 8/52 weeks with commits
18/18Commit volume — 134 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_year134
human_commit_share1
days_since_last_push22
active_weeks_last_year8

Release discipline

94Exceptional
How it's scored
27/27Ships releases — 5 releases published
36/36Release recency — latest release 29 days ago
27/27Release cadence — a release every ~9.8 days
4/10OpenSSF Scorecard: Signed-Releases — 2 out of the last 5 releases have a total of 4 signed artifacts.
Inputs used
releases_count5
latest_release_tagv0.12.0
releases_from_tagsno
days_since_latest_release29
mean_days_between_releases9.8

Community & Adoption

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

21At Risk · 17% of overall

Popularity & adoption

1Critical
How it's scored
0/60Stars — 1 stars
0/25Forks — 0 forks
0/15Watchers — 0 watchers
Inputs used
forks0
stars1
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonbelow_threshold

Community health

44Weak
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_badges—
has_contributingno
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?

34At Risk · 23% of overall

Maintainer resilience (bus factor)

15Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.3/22.5Commit distribution — top contributor authored 98% of commits
2.7/13.5Contributor breadth — 2 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.985

Issue & PR responsiveness

1Critical
How it's scored
0/42Issue resolution — no issues or no data
0/30PR acceptance — no decided pull requests or no data
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_prs0
open_issues0
closed_issues0
prs_merged_7d—
prs_decided_7d—
prs_merged_30d—
prs_decided_30d—
issue_closed_ratio—
closed_unmerged_prs0
first_time_authors_30d—
first_time_prs_merged_30d—
first_time_prs_decided_30d—
Excluded from scoring (no data or not applicable): Issue resolution, PR acceptance, Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

36Weak
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
8/25Owner reach — 12 followers of Whatsonyourmind
10.9/25Track record — 20 public repos, account ~0 yr old
Inputs used
followers12
owner_typeUser
is_verified—
owner_loginWhatsonyourmind
public_repos20
account_age_days226
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 29 days ago
20/20Version history — 7 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmodelforge-finance
ecosystemspypi
any_deprecatedno
min_days_since_publish29

Engineering Quality

Are baseline engineering and documentation practices in place?

72Good · 19% of overall

Engineering practices

60Moderate
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
0/16Linter config
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_configno
has_precommit_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://pypi.org/project/modelforge-finance/
10/10Repository description
10/10Topics — 14 topics
0/10Wiki
Inputs used
topicscredit-risk, excel, financial-modeling, llm-tools, mcp, openpyxl, ai-agents, audit-trail, data-lineage, dcf, lbo, mcp-server, valuation, yaml-to-excel
has_wikino
homepagehttps://pypi.org/project/modelforge-finance/
docs_sitehttps://pypi.org/project/modelforge-finance/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

55Moderate · 16% of overall

Security posture

44Weak
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
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no 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 — no SAST tool detected
5/5Security-Policy — security policy file detected
3/7.5Signed-Releases — 2 out of the last 5 releases have a total of 4 signed artifacts.
6.8/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 10 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.4
Excluded from scoring (no data or not applicable): CI-Tests, Packaging. 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_packages25
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:modelforge-finance@0.12.0 runtime dependency closure — what installing the published package pulls in — 25 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.

65Good · 4% of overall

Agent context & guidance

55Moderate
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt present
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_url—
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

53Moderate
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
11/11Static type checking — web/collab-worker/tsconfig.json
10/10Reproducible environment — Dockerfile, lockfile
10/10Demonstrated agent practice — 68 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
lockfilespackage-lock.json
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configsweb/collab-worker/tsconfig.json
agent_commit_share0.68
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (web/collab-worker/tsconfig.json)
54.6/55Manageable file sizes — 3/452 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes100,966
source_files_sampled452
oversized_source_files3

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
20/20MCP server
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalyes
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto). Remaining weights renormalized.

Key facts

1GitHub stars
2contributors
134commits, last 12 months
22days since last push
5releases
1bus factor
0open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 4.4 / 10
4.4aggregate

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 · 2026-07-22 05:14 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
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update 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
0SASTno SAST tool detected
10Security-Policysecurity policy file detected
4Signed-Releases2 out of the last 5 releases have a total of 4 signed artifacts.
9Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities10 existing vulnerabilities detected
Direct dependencies 8
RegistryPackageVersion constraintManifest
PyPIpydantic>=2.6pyproject.toml
PyPIopenpyxl>=3.1.2pyproject.toml
PyPIpyyaml>=6.0.1pyproject.toml
PyPIrich>=13.7pyproject.toml
PyPIclick>=8.1pyproject.toml
PyPIformulas>=1.2.7pyproject.toml
PyPIreportlab>=4.0pyproject.toml
PyPInumpy>=1.26pyproject.toml
All dependencies 190

Full resolved dependency set from the GitHub dependency graph: 8 direct and 182 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIformulas—direct
PyPInumpy—direct
PyPIopenpyxl—direct
PyPIpydantic—direct
PyPIpyyaml—direct
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PyPIrich—direct
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Dependency advisories 0

Installing pypi:modelforge-finance@0.12.0 pulls in 25 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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Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.26.0 — full methodology · metrics wiki.

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