Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-19 02:38 UTC

OpenDCAI / DataFlow

[SIGMOD'27] Easy Data Preparation with latest LLMs-based Operators and Pipelines.

PythonApache-2.0★ 8,186 stars⑂ 1,146 forkssince Oct 2024View on GitHub ↗
KindCommand-line toolhow this is determined

OpenDCAI/DataFlow holds a health index of 88 out of 100, placing it in the Excellent band. It scores highest on Vitality (91/100) and lowest on AI Readiness (46/100). It was last updated 5 days ago. 4 contributors account for most of its recent work.

88
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.

88
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 74 is calibrated to 88 on the published index scale (record calibration 2026-08-02).

Ownership

OpenDCAIOrganization
1,747 followers42 public repossince Aug 2024

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIopen-dataflowpoints to another repo — not scored1.0.101,16814176 days agoaiartificial-intelligence

Metrics by category

Vitality

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

91Excellent · 21% of overall
How it's scored
36/36Push recency — last push 5 days ago
27/36Commit cadence — 39/52 weeks with commits
18/18Commit volume — 350 commits in the last year
10/10OpenSSF Scorecard: Maintained — 23 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year350
human_commit_share1
days_since_last_push5
active_weeks_last_year39
How it's scored
27/27Ships releases — 12 releases published
27/36Release recency — latest release 176 days ago
27/27Release cadence — a release every ~29.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count12
latest_release_tagv1.0.10
releases_from_tagsno
days_since_latest_release176
mean_days_between_releases29.6
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?

77Good · 17% of overall

Popularity & adoption

100Exceptional
How it's scored
60/60Stars — 8,186 stars
25/25Forks — 1,146 forks
15/15Watchers — 561 watchers
Inputs used
forks1,146
stars8,186
watchers561
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges22
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?

78Good · 23% of overall
How it's scored
43.2/54Bus factor — 4 contributor(s) cover half of all commits
17.6/22.5Commit distribution — top contributor authored 22% of commits
13.5/13.5Contributor breadth — 54 contributors
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Inputs used
bus_factor4
contributors_sampled54
top_contributor_share0.218
How it's scored
36.8/42Issue resolution — 88% of issues closed
25.9/30PR acceptance — 385/446 decided PRs merged
9.3/13Newcomer PR acceptance — 5/7 first-time contributors' PRs merged in 30d
7.5/15OpenSSF Scorecard: Code-Review — Found 15/30 approved changesets -- score normalized to 5
Inputs used
merged_prs385
open_issues9
closed_issues63
prs_merged_7d0
prs_decided_7d0
prs_merged_30d6
prs_decided_30d8
issue_closed_ratio0.875
closed_unmerged_prs61
first_time_authors_30d3
first_time_prs_merged_30d5
first_time_prs_decided_30d7
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
23.3/25Owner reach — 1,747 followers of OpenDCAI
16.1/25Track record — 42 public repos, account ~2 yr old
Inputs used
followers1,747
owner_typeOrganization
is_verifiedno
owner_loginOpenDCAI
public_repos42
account_age_days773

Engineering Quality

Are baseline engineering and documentation practices in place?

67Good · 19% of overall
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
14/20OpenSSF Scorecard: CI-Tests — 14 out of 19 merged PRs checked by a CI test -- score normalized to 7
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://OpenDCAI.github.io/DataFlow-Doc/
10/10Repository description
10/10Topics — 13 topics
10/10Wiki
Inputs used
topicsdata, data-cleaning, data-pipelines, data-processing, data-science, data-synthesis, llms, operators, data-agent, sglang-bankend, vllm-backend, gradio-interface, quick-data-processing
has_wikiyes
homepagehttps://OpenDCAI.github.io/DataFlow-Doc/
docs_sitehttps://OpenDCAI.github.io/DataFlow-Doc/
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

60Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
2.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
1.8/2.5CI-Tests — 14 out of 19 merged PRs checked by a CI test -- score normalized to 7
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
3.8/7.5Code-Review — Found 15/30 approved changesets -- score normalized to 5
2.5/2.5Contributors — project has 6 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 23 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
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
0/5Security-Policy — security policy file not 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_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5
Excluded from scoring (no data or not applicable): 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
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories76
affected_packages2
assessed_packages4
unassessed_packages74
affected_by_severitycritical 1, high 1
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 4 resolved dependencies against OSV. 74 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.

46Weak · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
34.1/40Legible commit history — 64 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.64
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
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_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
54.2/55Manageable file sizes — 6/417 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes306,985
source_files_sampled417
oversized_source_files6
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 — example
Inputs used
example_dirsexample
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

8,186GitHub stars
54contributors
350commits, last 12 months
5days since last push
12releases
4bus factor
9open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'open-dataflow' points at a different repository (https://github.com/Open-DataFlow/DataFlow); excluded from ecosystem scoring

More detail

Star and fork history 0 ★ / 1,146 ⇿
0Stars
1,146Forks
2Releases

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

02004006008001,0001,2001,146192026-012026-052026-09
Major 0Minor 0Patch 2
OpenSSF Scorecard 5.0 / 10
5.0aggregate

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 02:37 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
7CI-Tests14 out of 19 merged PRs checked by a CI test -- score normalized to 7
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
5Code-ReviewFound 15/30 approved changesets -- score normalized to 5
10Contributorsproject has 6 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained23 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
8Vulnerabilities2 existing vulnerabilities detected
All dependencies 78

Full resolved dependency set from the GitHub dependency graph: 0 direct and 78 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaccelerate—indirect
PyPIaddict—indirect
PyPIappdirs—indirect
PyPIchonkie—indirect
PyPIcloudpickle—indirect
PyPIcolorama—indirect
PyPIcolorlog—indirect
PyPIcontractions—indirect
PyPIcookiecutter—indirect
PyPIdatasets—indirect
PyPIdatasketch—indirect
PyPIdb-dtypes—indirect
PyPIdistflow—indirect
PyPIdocstring-parser—indirect
PyPIevaluate—indirect
PyPIfastapi—indirect
PyPIfasttext-wheel—indirect
PyPIfsspec—indirect
PyPIfunc-timeout—indirect
PyPIgcsfs—indirect
PyPIgoogle-api-core—indirect
PyPIgoogle-api-python-client—indirect
PyPIgoogle-cloud-aiplatform—indirect
PyPIgoogle-cloud-bigquery—indirect
PyPIgoogle-cloud-bigquery-storage—indirect
PyPIgoogle-genai—indirect
PyPIgradio—indirect
PyPIhttpx—indirect
PyPIjson5—indirect
PyPIlangkit—indirect
PyPIlibrosa—indirect
PyPIlitellm—indirect
PyPIllamafactory—indirect
PyPIlxml-html-clean—indirect
PyPImath-verify—indirect
PyPImineru2.0.6indirect
PyPImodelscope—indirect
PyPInltk—indirect
PyPInumpy—indirect
PyPIopen-dataflow—indirect
PyPIopenai—indirect
PyPIpandas—indirect
PyPIpresidio-analyzer—indirect
PyPIpresidio-anonymizer—indirect
PyPIpsutil—indirect
PyPIpyarrow20.0.0indirect
PyPIpydantic—indirect
PyPIpyfiglet—indirect
PyPIpymupdf—indirect
PyPIpymysql—indirect
PyPIpypdf—indirect
PyPIpytest—indirect
PyPIpyyaml—indirect
PyPIrapidfuzz—indirect
PyPIrequests—indirect
PyPIrich—indirect
PyPIscipy—indirect
PyPIsentencepiece—indirect
PyPIsetuptools—indirect
PyPIsglang—indirect
PyPIsimhash—indirect
PyPIsoundfile—indirect
PyPIsqlglot—indirect
PyPIsseclient-py—indirect
PyPIsymspellpy—indirect
PyPItermcolor—indirect
PyPItiktoken—indirect
PyPItokenizers—indirect
PyPItorch—indirect
PyPItorchaudio—indirect
PyPItorchvision—indirect
PyPItqdm—indirect
PyPItrafilatura—indirect
PyPItransformers—indirect
PyPIuvicorn—indirect
PyPIvendi-score0.0.3indirect
PyPIvllm0.6.3indirect
PyPIword2number—indirect
Dependency advisories 2

This repository publishes no package the index resolves, so its own dependency graph was assessed — 4 packages, which also include development and test pins that never ship: 2 carry known advisories, of which 0 are direct. 74 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
vllm0.6.3indirectcritical740.28.0
pyarrow20.0.0indirecthigh223.0.1

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.

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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.