Public record
Software health reportschema 0.9.0 · metrics 1.13.0 · 2026-07-14 09:13 UTC

rerun-io / rerun

Visualize, query, and stream to train on multimodal robotics data.

Rust · PythonApache-2.0★ 11,126 stars⑂ 793 forkssince Apr 2022View on GitHub ↗

rerun-io/rerun holds a health index of 82 out of 100, placing it in the Good band. It scores highest on Vitality (100/100) and lowest on Security (40/100). It was last updated today. 3 contributors account for most of its recent work.

82
overall / 100
Good

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.

82
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe 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.

Ownership

rerun.ioOrganization
765 followers144 public repossince Feb 2022

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIrerun-notebook0.34.1-1856 days ago
PyPIrerun-sdk0.34.1-1406 days ago

Metrics by category

Vitality

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

100Excellent · 22% of overall
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 2,907 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year2,907
human_commit_share
days_since_last_push0
active_weeks_last_year52

Release discipline

100Excellent
How it's scored
27/27Ships releases — 80 releases published
36/36Release recency — latest release 6 days ago
27/27Release cadence — a release every ~10 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count80
latest_release_tag0.34.1
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases10

Community & Adoption

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

94Excellent · 18% of overall
How it's scored
60/60Stars — 11,126 stars
24.2/25Forks — 793 forks
10.4/15Watchers — 74 watchers
Inputs used
forks793
stars11,126
watchers74
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized 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
has_contributingyes
has_issue_templateno
has_code_of_conductyes
has_pull_request_templateyes

Sustainability & Governance

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

80Good · 24% of overall
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
16.4/22.5Commit distribution — top contributor authored 27% of commits
13.5/13.5Contributor breadth — 100 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor3
contributors_sampled100
top_contributor_share0.273
How it's scored
33.3/46.8Issue resolution — 71% of issues closed
35.2/38.3PR acceptance — 7,579/8,239 decided PRs merged
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs7,579
open_issues1,267
closed_issues3,147
issue_closed_ratio0.713
closed_unmerged_prs660
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
20.7/25Owner reach — 765 followers of rerun-io
21.8/25Track record — 144 public repos, account ~4 yr old
Inputs used
followers765
owner_typeOrganization
is_verified
owner_loginrerun-io
public_repos144
account_age_days1,614
How it's scored
25/25Published & resolvable — 2 package(s) on pypi
35/35Publish recency — latest publish 6 days ago
20/20Version history — 185 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesrerun-notebook, rerun-sdk
ecosystemspypi
any_deprecatedno
min_days_since_publish6

Engineering Quality

Are baseline engineering and documentation practices in place?

89Excellent · 20% of overall
How it's scored
24/24CI workflows — 54 workflow(s)
24/24Tests present
16/16Linter config — ruff.toml
0/9.6Pre-commit hooks
6.4/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigyes
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://rerun.io/
10/10Repository description
10/10Topics — 7 topics
0/10Wiki
Inputs used
topicscomputer-vision, cpp, multimodal, python, robotics, rust, visualization
has_wikino
homepagehttps://rerun.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

40At risk · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilesCargo.lock, package-lock.json, uv.lock, yarn.lock
manifestsCargo.toml, pyproject.toml, rerun_js/package.json, rerun_notebook/package.json, rerun_notebook/pyproject.toml, rerun_pixi_env/pyproject.toml, rerun_py/Cargo.toml, rerun_py/pyproject.toml, run_wasm/Cargo.toml
has_codeql_workflowno
has_security_policyyes
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score.

92Excellent · 0% of overall
How it's scored
45/45Agent instructions — AGENTS.md, CLAUDE.md
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtno
legible_history_share
agent_instruction_filesAGENTS.md, CLAUDE.md
agent_instruction_max_bytes9,121
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
18/18One-command bootstrap — examples/c/minimal/Makefile
22/22Automated tests
11/11Lint / format config — ruff.toml
11/11Static type checking — examples/python/_isolated/mypy.ini, pyrightconfig.json, rerun_js/web-viewer-react/tsconfig.json, rerun_js/web-viewer/tsconfig.json, rerun_notebook/tsconfig.json, rerun_py/rerun_bindings/py.typed, rerun_py/rerun_sdk/rerun/py.typed
10/10Reproducible environment — Dockerfile, lockfile
0/10Demonstrated agent practice — no data
0/8Automated maintenance — no data
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfilesCargo.lock, package-lock.json, uv.lock, yarn.lock
has_dockerfileyes
typed_languageyes
bootstrap_filesexamples/c/minimal/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsexamples/python/_isolated/mypy.ini, pyrightconfig.json, rerun_js/web-viewer-react/tsconfig.json, rerun_js/web-viewer/tsconfig.json, rerun_notebook/tsconfig.json, rerun_py/rerun_bindings/py.typed, rerun_py/rerun_sdk/rerun/py.typed
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.
How it's scored
45/45Type-checkable code — Rust (statically typed)
54.5/55Manageable file sizes — 38/4,107 source files over 60KB
Inputs used
primary_languageRust
largest_source_bytes302,643
source_files_sampled4,107
oversized_source_files38
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — crates/store/re_protos/proto/rerun/v1alpha1/cloud.proto, crates/store/re_protos/proto/rerun/v1alpha1/common.proto, crates/store/re_protos/proto/rerun/v1alpha1/log_msg.proto, crates/store/re_protos/proto/rerun/v1alpha1/sdk_comms.proto, crates/store/re_protos/proto/rerun/v1alpha1/viewer.proto, examples/python/objectron/objectron/proto/a_r_capture_metadata.proto, examples/python/objectron/objectron/proto/annotation_data.proto, examples/python/objectron/objectron/proto/object.proto
20/20MCP server
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalyes
api_schema_filescrates/store/re_protos/proto/rerun/v1alpha1/cloud.proto, crates/store/re_protos/proto/rerun/v1alpha1/common.proto, crates/store/re_protos/proto/rerun/v1alpha1/log_msg.proto, crates/store/re_protos/proto/rerun/v1alpha1/sdk_comms.proto, crates/store/re_protos/proto/rerun/v1alpha1/viewer.proto, examples/python/objectron/objectron/proto/a_r_capture_metadata.proto, examples/python/objectron/objectron/proto/annotation_data.proto, examples/python/objectron/objectron/proto/object.proto

Key facts

11,126GitHub stars
100contributors
2,907commits, last 12 months
0days since last push
80releases
3bus factor
1,267open issues
crates.io, npm, PyPIpackage ecosystems

Data collection warnings

  • Could not fetch pypi package 'rerun-workspace' from its registry
  • Could not fetch pypi package 'rerun-pixi-env' from its registry
  • Could not fetch crates package 'rerun_py' from its registry
  • Could not fetch crates package 'run_wasm' from its registry
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Direct dependencies 73
RegistryPackageVersion constraintManifest
npm@rerun-io/web-viewerfile:../rerun_js/web-viewerrerun_notebook/package.json
PyPIanywidgetrerun_notebook/pyproject.toml
PyPIjupyter-ui-pollrerun_notebook/pyproject.toml
PyPIipykernel!= 7.0.*rerun_notebook/pyproject.toml
crates.iore_arrow_utilrerun_py/Cargo.toml
crates.iore_authrerun_py/Cargo.toml
crates.iore_build_inforerun_py/Cargo.toml
crates.iore_chunkrerun_py/Cargo.toml
crates.iore_lenses_corererun_py/Cargo.toml
crates.iore_lensesrerun_py/Cargo.toml
crates.iore_chunk_storererun_py/Cargo.toml
crates.iore_formatrerun_py/Cargo.toml
crates.iore_importerrerun_py/Cargo.toml
crates.iore_datafusionrerun_py/Cargo.toml
crates.iore_errorrerun_py/Cargo.toml
crates.iore_redap_clientrerun_py/Cargo.toml
crates.iore_grpc_clientrerun_py/Cargo.toml
crates.iore_grpc_serverrerun_py/Cargo.toml
crates.iore_logrerun_py/Cargo.toml
crates.iore_log_encodingrerun_py/Cargo.toml
crates.iore_log_typesrerun_py/Cargo.toml
crates.iore_mcaprerun_py/Cargo.toml
crates.iore_mp4_readerrerun_py/Cargo.toml
crates.iore_parquetrerun_py/Cargo.toml
crates.iore_memoryrerun_py/Cargo.toml
crates.iore_quota_channelrerun_py/Cargo.toml
crates.iore_sdkrerun_py/Cargo.toml
crates.iore_sorbetrerun_py/Cargo.toml
crates.iore_tracingrerun_py/Cargo.toml
crates.iore_tuidrerun_py/Cargo.toml
crates.iore_types_corererun_py/Cargo.toml
crates.iore_urirerun_py/Cargo.toml
crates.iore_videorerun_py/Cargo.toml
crates.iore_web_viewer_serverrerun_py/Cargo.toml
crates.iore_serverrerun_py/Cargo.toml
crates.ioanyhowrerun_py/Cargo.toml
crates.ioarrowrerun_py/Cargo.toml
crates.iochronorerun_py/Cargo.toml
crates.iocomfy-tablererun_py/Cargo.toml
crates.iocrossbeamrerun_py/Cargo.toml
crates.iodatafusionrerun_py/Cargo.toml
crates.iodatafusion-ffirerun_py/Cargo.toml
crates.iodocument-featuresrerun_py/Cargo.toml
crates.iofuturesrerun_py/Cargo.toml
crates.ioitertoolsrerun_py/Cargo.toml
crates.iojiffrerun_py/Cargo.toml
crates.iomemmap2rerun_py/Cargo.toml
crates.ioinferrerun_py/Cargo.toml
crates.iomimallocrerun_py/Cargo.toml
crates.ionumpyrerun_py/Cargo.toml
crates.ioparking_lotrerun_py/Cargo.toml
crates.iopyo3rerun_py/Cargo.toml
crates.iorandrerun_py/Cargo.toml
crates.iorustlsrerun_py/Cargo.toml
crates.iostrumrerun_py/Cargo.toml
crates.iostrum_macrosrerun_py/Cargo.toml
crates.iotracingrerun_py/Cargo.toml
crates.iouuidrerun_py/Cargo.toml
crates.iore_protosrerun_py/Cargo.toml
crates.iotokiorerun_py/Cargo.toml
crates.iotokio-streamrerun_py/Cargo.toml
crates.iotonicrerun_py/Cargo.toml
crates.iourlrerun_py/Cargo.toml
crates.iothiserrorrerun_py/Cargo.toml
PyPIattrs>=23.1.0rerun_py/pyproject.toml
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PyPIpyarrow>=18.0.0rerun_py/pyproject.toml
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crates.iocargo-run-wasmrun_wasm/Cargo.toml
crates.iopico-argsrun_wasm/Cargo.toml
crates.iowebbrowserrun_wasm/Cargo.toml
Raw JSON report machine-readable
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              {
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                "key": "owner_reach",
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              {
                "key": "track_record",
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                    "code": "account_age_years",
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          },
          {
            "key": "package_maintenance",
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            "notes": [],
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              "ecosystems": "pypi",
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                "key": "published_resolvable",
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                "detail": "2 package(s) on pypi",
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                "key": "version_history",
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                "key": "not_deprecated",
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        ],
        "description": "Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?"
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      {
        "key": "engineering",
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                "key": "ci_workflows",
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                "points": 24,
                "status": "met",
                "details": [
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                    "code": "ci_workflows",
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                "max_points": 24
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              {
                "key": "tests_present",
                "name": "Tests present",
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                "points": 24,
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              {
                "key": "linter_config",
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                "status": "met",
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                "key": "pre_commit_hooks",
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              {
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                "key": "openssf_scorecard_ci_tests",
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                "key": "documentation_directory",
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                "key": "documentation_homepage_site",
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                "key": "wiki",
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                "status": "missed",
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          }
        ],
        "description": "Are baseline engineering and documentation practices in place?"
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      {
        "key": "security",
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        "name": "Security",
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        "weight": 0.16,
        "metrics": [
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            "key": "security_posture",
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                "code": "excluded_no_data",
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                "code": "weights_renormalized",
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            ],
            "value": 40,
            "inputs": {
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                "key": "security_policy_security_md",
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                "points": 30,
                "status": "met",
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              {
                "key": "dependabot_config",
                "name": "Dependabot config",
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                "points": 0,
                "status": "missed",
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                "key": "dependency_lockfiles",
                "name": "Dependency lockfiles",
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                "status": "excluded",
                "details": [
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                ],
                "max_points": 25
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              {
                "key": "codeql_workflow",
                "name": "CodeQL workflow",
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                "points": 0,
                "status": "missed",
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            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
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            "note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
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                "code": "jurisdiction_evidence_limits",
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            ],
            "value": 100,
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              "meaning": "self-published location evidence; not nationality or citizenship",
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              "review_only_matches": 0,
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            "components": [
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                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
                "detail": "no confirmed policy-scope location match",
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                "status": "met",
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        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
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      {
        "key": "ai_readiness",
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            "value": 75,
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                "key": "machine_readable_docs_llms_txt",
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                "status": "excluded",
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                "max_points": 40
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            ]
          },
          {
            "key": "ai_verify_loop",
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                "status": "met",
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                "max_points": 11
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                "status": "met",
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                "points": 10,
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                "status": "excluded",
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                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
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                "points": 0,
                "status": "excluded",
                "details": [
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                    "code": "no_data",
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                ],
                "max_points": 8
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              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "OpenSSF Scorecard unavailable",
                "points": 0,
                "status": "excluded",
                "details": [
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                    "code": "no_data",
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                ],
                "max_points": 10
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            ]
          },
          {
            "key": "ai_code_legibility",
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            "name": "Code legibility for models",
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            "notes": [],
            "value": 100,
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                "key": "type_checkable_code",
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                "points": 45,
                "status": "met",
                "details": [
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                    "code": "statically_typed_language",
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                "max_points": 45
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        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "Could not fetch pypi package 'rerun-workspace' from its registry",
    "Could not fetch pypi package 'rerun-pixi-env' from its registry",
    "Could not fetch crates package 'rerun_py' from its registry",
    "Could not fetch crates package 'run_wasm' from its registry",
    "OpenSSF Scorecard timed out after 240s; skipping Scorecard checks"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-14T09:13:54.033750Z",
  "schema_version": "0.9.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/r/rerun-io/rerun.svg",
  "full_name": "rerun-io/rerun",
  "license_state": "standard",
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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 v1.13.0, schema v0.9.0 — full methodology · metrics wiki.

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