Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-15 04:51 UTC

meta-pytorch / tritonparse

TritonParse: A Compiler Tracer, Visualizer, and Reproducer for Triton Kernels

Python · TypeScriptBSD-3-Clause★ 215 stars⑂ 27 forkssince Apr 2025View on GitHub ↗
KindCommand-line toolWeb interfacehow this is determined
#compiler#debugging#interactive-visualization#ir-analysis#pytorch#structured-logging#triton#gpu-kernel#ir-visualization

meta-pytorch/tritonparse holds a health index of 91 out of 100, placing it in the Excellent band. It scores highest on Vitality (99/100) and lowest on Security (62/100). It was last updated today. A single contributor accounts for most of its recent work.

91
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.

91
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 77 is calibrated to 91 on the published index scale (record calibration 2026-08-02).

Ownership

Meta PyTorchOrganization · verified domain
1,422 followers60 public repossince Jun 2022

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPItritonparse0.5.14,89818866 days ago

Metrics by category

Vitality

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

99Exceptional · 21% of overall

Development activity

98Exceptional
How it's scored
36/36Push recency — last push 0 days ago
33.9/36Commit cadence — 49/52 weeks with commits
18/18Commit volume — 470 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year470
human_commit_share0.93
days_since_last_push0
active_weeks_last_year49

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 14 releases published
36/36Release recency — latest release 66 days ago
27/27Release cadence — a release every ~29.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count14
latest_release_tagv0.5.1
releases_from_tagsno
days_since_latest_release66
mean_days_between_releases29.9
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?

67Good · 17% of overall

Popularity & adoption

54Moderate
How it's scored
37.8/60Stars — 215 stars
11.8/25Forks — 27 forks
3.9/15Watchers — 6 watchers
Inputs used
forks27
stars215
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (BSD-3-Clause)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

62Moderate
How it's scored
49.2/80Monthly downloads — 4,898 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagestritonparse
dependents—
ecosystemspypi
total_downloads—
monthly_downloads4,898
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

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

66Good · 23% of overall

Maintainer resilience (bus factor)

37Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
4.9/22.5Commit distribution — top contributor authored 78% of commits
13.5/13.5Contributor breadth — 22 contributors
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled22
top_contributor_share0.782

Issue & PR responsiveness

45Weak
How it's scored
29.9/42Issue resolution — 71% of issues closed
0.4/30PR acceptance — 4/318 decided PRs merged
0/13Newcomer PR acceptance — 0/7 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs4
open_issues31
closed_issues77
prs_merged_7d0
prs_decided_7d5
prs_merged_30d0
prs_decided_30d7
issue_closed_ratio0.713
closed_unmerged_prs314
first_time_authors_30d3
first_time_prs_merged_30d0
first_time_prs_decided_30d7

Ownership & stewardship

94Exceptional
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
22.7/25Owner reach — 1,422 followers of meta-pytorch
21.5/25Track record — 60 public repos, account ~4 yr old
Inputs used
followers1,422
owner_typeOrganization
is_verifiedyes
owner_loginmeta-pytorch
public_repos60
account_age_days1,558

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 66 days ago
20/20Version history — 188 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagestritonparse
ecosystemspypi
any_deprecatedno
min_days_since_publish66

Engineering Quality

Are baseline engineering and documentation practices in place?

88Excellent · 19% of overall

Engineering practices

80Excellent
How it's scored
24/24CI workflows — 7 workflow(s)
24/24Tests present
16/16Linter config — eslint.config.js, pyproject.toml ([tool.ruff])
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_configyes
has_precommit_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://meta-pytorch.org/tritonparse/
10/10Repository description
10/10Topics — 9 topics
10/10Wiki
Inputs used
topicscompiler, debugging, interactive-visualization, ir-analysis, pytorch, structured-logging, triton, gpu-kernel, ir-visualization
has_wikiyes
homepagehttps://meta-pytorch.org/tritonparse/
docs_sitehttps://meta-pytorch.org/tritonparse/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

62Moderate · 16% of overall

Security posture

52Moderate
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
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 6 contributing companies or organizations
0/10Dangerous-Workflow — dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 2 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 — no SAST tool detected
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
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.2
Excluded from scoring (no data or not applicable): CI-Tests, 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_packages6
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:tritonparse@0.5.1 runtime dependency closure — what installing the published package pulls in — 6 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.

66Good · 4% of overall

Agent context & guidance

40Weak
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 93 of 93 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

80Excellent
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — eslint.config.js, pyproject.toml ([tool.ruff])
11/11Static type checking — website/tsconfig.json
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 7 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
lockfilespackage-lock.json
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configswebsite/tsconfig.json
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.07

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (website/tsconfig.json)
54.5/55Manageable file sizes — 2/238 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes100,023
source_files_sampled238
oversized_source_files2

Key facts

215GitHub stars
22contributors
470commits, last 12 months
0days since last push
14releases
1bus factor
31open issues
npm, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'tritonparse' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • pypi download figures for tritonparse carried forward from the previous scan (stats endpoint unavailable this scan)

More detail

Star and fork history 0 ★ / 27 ⇿
0Stars
27Forks
14Releases

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.

0510152025302732025-062026-012026-09
Major 0Minor 5Patch 9

Each point covers 2 days.

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 · 2026-09-15 04:50 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 6 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 2 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
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 11
RegistryPackageVersion constraintManifest
PyPIorjson>=3.9pyproject.toml
PyPIrich>=13.0pyproject.toml
PyPIzstandard>=0.19.0pyproject.toml
npm@heroicons/react^2.2.0website/package.json
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npmreact-resizable-panels^4.12.3website/package.json
npmreact-syntax-highlighter^16.1.1website/package.json
All dependencies 266

Full resolved dependency set from the GitHub dependency graph: 11 direct and 255 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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npmpostcss8.5.26indirect
npmprelude-ls1.2.1indirect
npmprismjs1.30.0indirect
npmproperty-information7.1.0indirect
npmpunycode2.3.1indirect
npmrefractor5.0.0indirect
npmrolldown1.2.6indirect
npmscheduler0.27.0indirect
npmsemver6.3.1indirect
npmsemver7.8.5indirect
npmshebang-command2.0.0indirect
npmshebang-regex3.0.0indirect
npmsource-map-js1.2.1indirect
npmspace-separated-tokens2.0.2indirect
npmstate-local1.0.7indirect
npmtailwindcss4.3.3indirect
npmtapable2.3.3indirect
npmtinyglobby0.2.17indirect
npmts-api-utils2.5.0indirect
npmtslib2.8.1indirect
npmtype-check0.4.0indirect
npmtypescript6.0.3indirect
npmtypescript-eslint8.69.0indirect
npmundici-types8.3.0indirect
npmupdate-browserslist-db1.2.3indirect
npmuri-js4.4.1indirect
npmvite8.2.2indirect
npmwhich2.0.2indirect
npmword-wrap1.2.5indirect
npmyallist3.1.1indirect
npmyocto-queue0.1.0indirect
npmzod4.3.6indirect
npmzod-validation-error4.0.2indirect
PyPIcoverage—indirect
PyPIparameterized—indirect
PyPIpytorch-triton—indirect
PyPIruff0.16.0indirect
PyPIruff-api0.2.0indirect
PyPIsetuptools—indirect
PyPIsetuptools-scm—indirect
PyPItriton—indirect
PyPIufmt2.9.0indirect
PyPIusort1.1.0indirect
Dependency advisories 0

Installing pypi:tritonparse@0.5.1 pulls in 6 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.

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96iree-org/ireeA retargetable MLIR-based machine learning compiler and runtime toolkit.Exceptional95flagos-ai/FlagGemsFlagGems is an operator library for large language models implemented in the Triton Language.Exceptional94pytorch/TensorRTPyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRTExceptional90evloghq/evlogDigging through logs is not observability. It's hope — wide events, structured errors, TypeScript-first, every runtime.Excellent89EnzymeAD/EnzymeHigh-performance automatic differentiation of LLVM and MLIR.Excellent100microsoft/onnxruntimeONNX Runtime: cross-platform, high performance ML inferencing and training acceleratorExceptional

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.