Public record
Software health reportschema 0.27.0 · metrics 2.10.0 · 2026-07-31 05:35 UTC

alibaba / Pai-Megatron-Patch

The official repo of Pai-Megatron-Patch for LLM & VLM large scale training developed by Alibaba Cloud.

PythonApache-2.0★ 1,586 stars⑂ 234 forkssince Sep 2023View on GitHub ↗

alibaba/Pai-Megatron-Patch holds a health index of 45 out of 100, placing it in the Weak band. It scores highest on Sustainability & Governance (71/100) and lowest on Engineering Quality (36/100). It was last updated 227 days ago. A single contributor accounts for most of its recent work.

45
overall / 100
Weak

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.

45
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

AlibabaOrganization
20,549 followers540 public repossince Jul 2012

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

Metrics by category

Vitality

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

47Weak · 21% of overall
How it's scored
3.6/36Push recencylast push 227 days ago
9.7/36Commit cadence14/52 weeks with commits
13/18Commit volume27 commits in the last year
0/10OpenSSF Scorecard: Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year27
human_commit_share1
days_since_last_push227
active_weeks_last_year14
How it's scored
27/27Ships releases29 releases published
16.2/36Release recencylatest release 272 days ago
27/27Release cadencea release every ~31.1 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count29
latest_release_tagv0.12.3
releases_from_tagsno
days_since_latest_release272
mean_days_between_releases31.1
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?

65Good · 17% of overall
How it's scored
51.9/60Stars1,586 stars
19.7/25Forks234 forks
7.2/15Watchers21 watchers
Inputs used
forks234
stars1,586
watchers21
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized 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_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?

71Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
9.2/22.5Commit distributiontop contributor authored 59% of commits
13.5/13.5Contributor breadth33 contributors
10/10OpenSSF Scorecard: Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
Inputs used
bus_factor1
contributors_sampled33
top_contributor_share0.591
How it's scored
29.6/42Issue resolution70% of issues closed
28.1/30PR acceptance319/340 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
10.5/15OpenSSF Scorecard: Code-ReviewFound 21/30 approved changesets -- score normalized to 7
Inputs used
merged_prs319
open_issues110
closed_issues263
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.705
closed_unmerged_prs21
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
25/25Owner reach20,549 followers of alibaba
25/25Track record540 public repos, account ~14 yr old
Inputs used
followers20,549
owner_typeOrganization
is_verified
owner_loginalibaba
public_repos540
account_age_days5,131
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

36Weak · 19% of overall
How it's scored
0/24CI workflows
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
2/20OpenSSF Scorecard: CI-Tests4 out of 29 merged PRs checked by a CI test -- score normalized to 1
Inputs used
has_cino
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics
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?

41Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
0.2/2.5CI-Tests4 out of 29 merged PRs checked by a CI test -- score normalized to 1
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
5.2/7.5Code-ReviewFound 21/30 approved changesets -- score normalized to 7
2.5/2.5Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
0/10Dangerous-Workflowno data
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packagingno data
0/5Pinned-Dependenciesno data
0.5/5SASTSAST tool is not run on all commits -- score normalized to 1
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsno data
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated12
scorecard_versionv5.5.0
checks_inconclusive6
scorecard_aggregate4.2
Excluded from scoring (no data or not applicable): Branch-Protection, Dangerous-Workflow, Packaging, Pinned-Dependencies, Signed-Releases, Token-Permissions. Remaining weights renormalized.

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.

45Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history96 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.96
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
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Pinned-Dependencies. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
53.6/55Manageable file sizes11/438 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes82,591
source_files_sampled438
oversized_source_files11
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
Inputs used
example_dirsexamples
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

1,586GitHub stars
33contributors
27commits, last 12 months
227days since last push
29releases
1bus factor
110open issues
package ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 234 ⇿
0Stars
234Forks
28Releases

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.

0408012016020024022362023-092025-022026-07
Major 0Minor 7Patch 21

Each point covers 3 days.

OpenSSF Scorecard 4.2 / 10
4.2aggregate

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 · 2026-07-31 05:35 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
1CI-Tests4 out of 29 merged PRs checked by a CI test -- score normalized to 1
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7Code-ReviewFound 21/30 approved changesets -- score normalized to 7
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
n/aDangerous-Workflowno workflows found
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/aPackagingpackaging workflow not detected
n/aPinned-Dependenciesno dependencies found
1SASTSAST tool is not run on all commits -- score normalized to 1
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
n/aToken-PermissionsNo tokens found
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 0

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

RegistryPackageVersionRelation
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies to assess

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 v0.27.0 — full methodology · metrics wiki.

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