Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-19 02:36 UTC

NVIDIA-NeMo / DataDesigner

🎨 NeMo Data Designer: Generate high-quality synthetic data from scratch or from seed data.

Python · MDXApache-2.0★ 2,237 stars⑂ 211 forkssince Oct 2025View on GitHub ↗
KindCommand-line toolLibraryPluginhow this is determined

NVIDIA-NeMo/DataDesigner holds a health index of 96 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (96/100) and lowest on Security (59/100). It was last updated today. 2 contributors account for most of its recent work.

96
overall / 100
Exceptional

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.

96
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 84 is calibrated to 96 on the published index scale (record calibration 2026-08-02).

Ownership

NVIDIA-NeMoOrganization · verified domain
1,885 followers28 public repossince May 2025

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIdata-designer0.9.2644,6717515 days ago
PyPIdata-designer-config0.9.2645,1655415 days ago
PyPIdata-designer-engine0.9.2643,9645415 days ago

Metrics by category

Vitality

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

95Exceptional · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
33.9/36Commit cadence49/52 weeks with commits
18/18Commit volume585 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year585
human_commit_share0.79
days_since_last_push0
active_weeks_last_year49
How it's scored
27/27Ships releases37 releases published
36/36Release recencylatest release 15 days ago
27/27Release cadencea release every ~15.4 days
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count37
latest_release_tagv0.9.2
releases_from_tagsno
days_since_latest_release15
mean_days_between_releases15.4

Community & Adoption

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

85Excellent · 17% of overall
How it's scored
54.3/60Stars2,237 stars
19.4/25Forks211 forks
5.6/15Watchers11 watchers
Inputs used
forks211
stars2,237
watchers11
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateyes

Sustainability & Governance

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

79Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
15.1/22.5Commit distributiontop contributor authored 33% of commits
13.5/13.5Contributor breadth23 contributors
10/10OpenSSF Scorecard: Contributorsproject has 4 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled23
top_contributor_share0.328
How it's scored
32.8/42Issue resolution78% of issues closed
26.2/30PR acceptance573/655 decided PRs merged
0/13Newcomer PR acceptance0/2 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Reviewall changesets reviewed
Inputs used
merged_prs573
open_issues59
closed_issues210
prs_merged_7d3
prs_decided_7d4
prs_merged_30d39
prs_decided_30d43
issue_closed_ratio0.781
closed_unmerged_prs82
first_time_authors_30d2
first_time_prs_merged_30d0
first_time_prs_decided_30d2
How it's scored
30/30Ownership backingorganization-owned
20/20Verified domain
23.5/25Owner reach1,885 followers of NVIDIA-NeMo
13.3/25Track record28 public repos, account ~1 yr old
Inputs used
followers1,885
owner_typeOrganization
is_verifiedyes
owner_loginNVIDIA-NeMo
public_repos28
account_age_days479

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable3 package(s) on pypi
35/35Publish recencylatest publish 15 days ago
20/20Version history75 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesdata-designer, data-designer-config, data-designer-engine
ecosystemspypi
any_deprecatedno
min_days_since_publish15

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall
How it's scored
24/24CI workflows19 workflow(s)
24/24Tests present
16/16Linter configpackages/data-designer-config/pyproject.toml ([tool.ruff]), packages/data-designer-engine/pyproject.toml ([tool.ruff]), packages/data-designer/pyproject.toml ([tool.ruff]), pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://docs.nvidia.com/nemo/datadesigner
10/10Repository description
10/10Topics11 topics
10/10Wiki
Inputs used
topicsagentic-ai, data-augmentation, data-generation, llm, mcp, multimodal, nemo, nvidia, synthetic-data, tool-use, sdg
has_wikiyes
homepagehttps://docs.nvidia.com/nemo/datadesigner
docs_sitehttps://docs.nvidia.com/nemo/datadesigner
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

59Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
6/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
2.5/2.5CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Reviewall changesets reviewed
2.5/2.5Contributorsproject has 4 contributing companies or organizations
0/10Dangerous-Workflowdangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
0/5Packagingno data
4/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 8
0/5SASTSAST tool is not run on all commits -- score normalized to 0
5/5Security-Policysecurity policy file detected
0/7.5Signed-ReleasesProject has not signed or included provenance with any releases.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities10 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5.2
Excluded from scoring (no data or not applicable): Packaging. Remaining weights renormalized.
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
13.2/25Indirect dependencies free of known advisories1 affected: cryptography 49.0.0 (moderate 5.9)
40/40No advisories left outstandingno advisory has been public longer than 90 days
Inputs used
sourceosv
advisories2
affected_packages1
assessed_packages89
unassessed_packages0
affected_by_severitymoderate 1
direct_affected_packages0
Matched the pypi:data-designer@0.9.2 runtime dependency closure — what installing the published package pulls in — 89 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.

88Excellent · 4% of overall
How it's scored
45/45Agent instructionsAGENTS.md, CLAUDE.md, fern/AGENTS.md
15/15Machine-readable docs (llms.txt)llms.txt served by the project website (https://docs.nvidia.com/llms.txt)
40/40Legible commit history79 of 79 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.nvidia.com/llms.txt
legible_history_share1
agent_instruction_filesAGENTS.md, CLAUDE.md, fern/AGENTS.md
agent_instruction_max_bytes4,075
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
11/11Lint / format configpackages/data-designer-config/pyproject.toml ([tool.ruff]), packages/data-designer-engine/pyproject.toml ([tool.ruff]), packages/data-designer/pyproject.toml ([tool.ruff]), pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environmentlockfile
10/10Demonstrated agent practice12 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance9 of the last 100 commits are automated dependency updates
8/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 8
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.12
toolchain_manifests
dependency_bot_commit_share0.09
How it's scored
0/45Type-checkable codePython without a type-check config
54.4/55Manageable file sizes7/661 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes216,289
source_files_sampled661
oversized_source_files7
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples, notebooks, recipes
Inputs used
example_dirsexamples, notebooks, recipes
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

2,237GitHub stars
23contributors
585commits, last 12 months
0days since last push
37releases
2bus factor
59open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'data-designer-workspace' from its registry
  • Could not fetch pypi package 'data-designer-e2e-tests' from its registry
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

More detail

Star and fork history 0 ★ / 211 ⇿
0Stars
211Forks
34Releases

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.

04080120160200240210252025-122026-042026-09
Major 0Minor 8Patch 26
OpenSSF Scorecard 5.2 / 10
5.2aggregate

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 · 2026-09-19 02:36 UTC

10Binary-Artifactsno binaries found in the repo
8Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 4 contributing companies or organizations
0Dangerous-Workflowdangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
8Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 8
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities10 existing vulnerabilities detected
Direct dependencies 15
RegistryPackageVersion constraintManifest
PyPIdata-designertests_e2e/pyproject.toml
PyPIhttpx>=0.27.2,<1packages/data-designer-config/pyproject.toml
PyPIjinja2>=3.1.6,<4packages/data-designer-config/pyproject.toml
PyPInumpy>=1.23.5,<3packages/data-designer-config/pyproject.toml
PyPIpandas>=2.3.3,<3packages/data-designer-config/pyproject.toml
PyPIidna>=3.18,<4packages/data-designer-config/pyproject.toml
PyPIpillow>=12.3.0,<13packages/data-designer-config/pyproject.toml
PyPIpyarrow>=24,<25packages/data-designer-config/pyproject.toml
PyPIpydantic>=2.9.2,<3packages/data-designer-config/pyproject.toml
PyPIpygments>=2.20,<3packages/data-designer-config/pyproject.toml
PyPIpython-json-logger>=3,<4packages/data-designer-config/pyproject.toml
PyPIpyyaml>=6.0.1,<7packages/data-designer-config/pyproject.toml
PyPIrequests>=2.33,<3packages/data-designer-config/pyproject.toml
PyPIrich>=13.7.1,<15packages/data-designer-config/pyproject.toml
PyPIurllib3>=2.7.0,<3packages/data-designer-config/pyproject.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

Dependency advisories 1

Installing pypi:data-designer@0.9.2 pulls in 89 packages, direct and transitive: 1 carry known advisories, of which 0 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
cryptography49.0.0indirectmoderate250.0.0

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 statisticsPyPI.