Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-15 13:29 UTC

Tejas-TA / predikit

The missing bridge between your ML models and your AI agents.

PythonMIT★ 400 stars⑂ 135 forkssince May 2026View on GitHub ↗
KindLibraryCommand-line toolhow this is determined
#agents#langchain#llm#machine-learning#openai#python#sklearn#xgboost#model-serving#pydantic#snowflake

Tejas-TA/predikit holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (95/100) and lowest on Sustainability & Governance (38/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

81
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.

81
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 69 is calibrated to 81 on the published index scale (record calibration 2026-08-02).

Ownership

TejasPersonal account
65 followers16 public repossince Jul 2015

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIpredikit0.6.341,315201 day agoagentsai-agentsfunction-callinglangchainllmmachine-learningml-toolsmlopsmodel-servingopenaipydanticscikit-learnsklearntool-usexgboost

Metrics by category

Vitality

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

83Excellent · 21% of overall

Development activity

71Good
How it's scored
36/36Push recency — last push 1 days ago
9.7/36Commit cadence — 14/52 weeks with commits
15.7/18Commit volume — 55 commits in the last year
10/10OpenSSF Scorecard: Maintained — 15 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year55
human_commit_share1
days_since_last_push1
active_weeks_last_year14

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 15 releases published
36/36Release recency — latest release 23 days ago
27/27Release cadence — a release every ~7 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count15
latest_release_tagv0.6.2
releases_from_tagsno
days_since_latest_release23
mean_days_between_releases7
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?

72Good · 17% of overall

Popularity & adoption

70Good
How it's scored
42.2/60Stars — 400 stars
17.7/25Forks — 135 forks
10.5/15Watchers — 77 watchers
Inputs used
forks135
stars400
watchers77
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

77Good
How it's scored
61.5/80Monthly downloads — 41,315 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagespredikit
dependents—
ecosystemspypi
total_downloads—
monthly_downloads41,315
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?

38Weak · 23% of overall

Maintainer resilience (bus factor)

13Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
1.2/22.5Commit distribution — top contributor authored 94% of commits
2.7/13.5Contributor breadth — 2 contributors
0/10OpenSSF Scorecard: Contributors — project has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.945

Issue & PR responsiveness

3Critical
How it's scored
0/42Issue resolution — 0% of issues closed
3/30PR acceptance — 1/10 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs1
open_issues17
closed_issues0
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0
closed_unmerged_prs9
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

55Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
13.1/25Owner reach — 65 followers of Tejas-TA
21/25Track record — 16 public repos, account ~11 yr old
Inputs used
followers65
owner_typeUser
is_verified—
owner_loginTejas-TA
public_repos16
account_age_days4,079
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 1 days ago
20/20Version history — 20 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespredikit
ecosystemspypi
any_deprecatedno
min_days_since_publish1

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-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_configyes
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://pypi.org/project/predikit/
10/10Repository description
10/10Topics — 11 topics
10/10Wiki
Inputs used
topicsagents, langchain, llm, machine-learning, openai, python, sklearn, xgboost, model-serving, pydantic, snowflake
has_wikiyes
homepagehttps://pypi.org/project/predikit/
docs_sitehttps://pypi.org/project/predikit/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

60Moderate · 16% of overall

Security posture

50Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
0/2.5Contributors — project has 0 contributing companies or organizations -- score normalized to 0
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 — 15 commit(s) and 0 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
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5
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_packages12
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:predikit@0.6.3 runtime dependency closure — what installing the published package pulls in — 12 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.

70Good · 4% of overall

Agent context & guidance

76Good
How it's scored
45/45Agent instructions — CLAUDE.md
0/15Machine-readable docs (llms.txt)
31/40Legible commit history — 32 of 55 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.582
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes3,056

Verify loop (build / test / typecheck)

51Moderate
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — src/predikit/py.typed
0/10Reproducible environment
7.3/10Demonstrated agent practice — 2 of the last 55 commits agent-authored or agent-credited
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_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configssrc/predikit/py.typed
agent_commit_share0.036
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (src/predikit/py.typed)
55/55Manageable file sizes — 0/37 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes9,399
source_files_sampled37
oversized_source_files0

Machine-readable interfaces

100Exceptional
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 — examples
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

400GitHub stars
2contributors
55commits, last 12 months
1days since last push
15releases
1bus factor
17open issues
PyPIpackage ecosystems

Data collection warnings

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

More detail

Star and fork history 0 ★ / 135 ⇿
0Stars
135Forks
10Releases

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.

0255075100125150131472026-052026-062026-07
Major 0Minor 2Patch 8
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 · 2026-09-15 13:29 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained15 commit(s) and 0 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
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 3
RegistryPackageVersion constraintManifest
PyPIpydantic>=2.0pyproject.toml
PyPIscikit-learn>=1.2pyproject.toml
PyPInumpy>=1.24pyproject.toml
All dependencies 196

Full resolved dependency set from the GitHub dependency graph: 7 direct and 189 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpy—direct
PyPInumpy2.2.6direct
PyPInumpy2.4.6direct
PyPIpydantic—direct
PyPIpydantic2.13.4direct
PyPIscikit-learn—direct
PyPIscikit-learn1.7.2direct
PyPIaiohappyeyeballs2.6.2indirect
PyPIaiohttp3.14.1indirect
PyPIaiosignal1.4.0indirect
PyPIalembic1.18.4indirect
PyPIannotated-doc0.0.4indirect
PyPIannotated-types0.7.0indirect
PyPIanyio4.14.0indirect
PyPIasn1crypto1.5.1indirect
PyPIast-serialize0.5.0indirect
PyPIasync-timeout5.0.1indirect
PyPIattrs26.1.0indirect
PyPIblinker1.9.0indirect
PyPIboto31.43.34indirect
PyPIbotocore1.43.34indirect
PyPIcachetools5.5.2indirect
PyPIcertifi2026.6.17indirect
PyPIcffi2.0.0indirect
PyPIcfgv3.5.0indirect
PyPIcharset-normalizer3.4.7indirect
PyPIclick—indirect
PyPIclick8.4.1indirect
PyPIcloudpickle3.1.1indirect
PyPIcolorama0.4.6indirect
PyPIcontourpy1.3.2indirect
PyPIcontourpy1.3.3indirect
PyPIcoverage7.14.2indirect
PyPIcryptography48.0.1indirect
PyPIcycler0.12.1indirect
PyPIdatabricks-sdk0.118.0indirect
PyPIdistlib0.4.3indirect
PyPIdocker7.1.0indirect
PyPIexceptiongroup1.3.1indirect
PyPIfastapi0.138.0indirect
PyPIfilelock3.29.4indirect
PyPIflask3.1.3indirect
PyPIflask-cors6.0.5indirect
PyPIfonttools4.63.0indirect
PyPIfrozenlist1.8.0indirect
PyPIfsspec2025.12.0indirect
PyPIgitdb4.0.12indirect
PyPIgitpython3.1.50indirect
PyPIgoogle-auth2.55.0indirect
PyPIgraphene3.4.3indirect
PyPIgraphql-core3.2.11indirect
PyPIgraphql-relay3.2.0indirect
PyPIgreenlet3.5.2indirect
PyPIgunicorn26.0.0indirect
PyPIh110.16.0indirect
PyPIh24.3.0indirect
PyPIhpack4.1.0indirect
PyPIhttpcore1.0.9indirect
PyPIhttpx0.28.1indirect
PyPIhttpx-sse0.4.3indirect
PyPIhuey3.0.3indirect
PyPIhyperframe6.1.0indirect
PyPIidentify2.6.19indirect
PyPIidna3.18indirect
PyPIimportlib-metadata9.0.0indirect
PyPIimportlib-resources6.5.2indirect
PyPIiniconfig2.3.0indirect
PyPIitsdangerous2.2.0indirect
PyPIjinja23.1.6indirect
PyPIjmespath1.1.0indirect
PyPIjoblib—indirect
PyPIjoblib1.5.3indirect
PyPIjsonpatch1.33indirect
PyPIjsonpointer3.1.1indirect
PyPIjsonschema4.26.0indirect
PyPIjsonschema-specifications2025.9.1indirect
PyPIkiwisolver1.5.0indirect
PyPIlangchain-core—indirect
PyPIlangchain-core1.4.8indirect
PyPIlangchain-protocol0.0.18indirect
PyPIlangsmith0.8.18indirect
PyPIlibrt0.11.0indirect
PyPIllvmlite0.36.0indirect
PyPIllvmlite0.47.0indirect
PyPImako1.3.12indirect
PyPImarkupsafe3.0.3indirect
PyPImatplotlib3.10.9indirect
PyPImatplotlib3.11.0indirect
PyPImcp—indirect
PyPImcp1.29.0indirect
PyPImlflow—indirect
PyPImlflow3.14.0indirect
PyPImlflow-skinny3.14.0indirect
PyPImlflow-tracing3.14.0indirect
PyPImultidict6.7.1indirect
PyPImypy—indirect
PyPImypy2.1.0indirect
PyPImypy-extensions1.1.0indirect
PyPInodeenv1.10.0indirect
PyPInumba0.53.1indirect
PyPInumba0.65.1indirect
PyPInvidia-nccl-cu122.30.7indirect
PyPIopentelemetry-api1.42.1indirect
PyPIopentelemetry-proto1.42.1indirect
PyPIopentelemetry-sdk1.42.1indirect
PyPIopentelemetry-semantic-conventions0.63b1indirect
PyPIorjson3.11.9indirect
PyPIpackaging25.0indirect
PyPIpandas—indirect
PyPIpandas2.3.3indirect
PyPIpathspec1.1.1indirect
PyPIpillow12.2.0indirect
PyPIplatformdirs4.10.0indirect
PyPIpluggy1.6.0indirect
PyPIpre-commit—indirect
PyPIpre-commit4.6.0indirect
PyPIpredikit0.6.0indirect
PyPIprettytable3.17.0indirect
PyPIpropcache0.5.2indirect
PyPIprotobuf6.33.6indirect
PyPIpyarrow24.0.0indirect
PyPIpyasn10.6.3indirect
PyPIpyasn1-modules0.4.2indirect
PyPIpycparser3.0indirect
PyPIpydantic-core2.46.4indirect
PyPIpydantic-settings2.14.2indirect
PyPIpygments2.20.0indirect
PyPIpyjwt2.13.0indirect
PyPIpyopenssl26.2.0indirect
PyPIpyparsing3.3.2indirect
PyPIpytest—indirect
PyPIpytest9.1.1indirect
PyPIpytest-cov—indirect
PyPIpytest-cov7.1.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-discovery1.4.2indirect
PyPIpython-dotenv1.2.2indirect
PyPIpython-multipart0.0.32indirect
PyPIpytimeparse1.1.8indirect
PyPIpytz2026.2indirect
PyPIpywin32312indirect
PyPIpyyaml6.0.3indirect
PyPIreferencing0.37.0indirect
PyPIrequests2.34.2indirect
PyPIrequests-toolbelt1.0.0indirect
PyPIretrying1.4.2indirect
PyPIrpds-py0.30.0indirect
PyPIrpds-py2026.6.3indirect
PyPIruff—indirect
PyPIruff0.15.18indirect
PyPIs3transfer0.19.0indirect
PyPIscipy1.15.3indirect
PyPIscipy1.17.1indirect
PyPIscipy1.18.0indirect
PyPIsetuptools82.0.1indirect
PyPIshap0.49.1indirect
PyPIshap0.51.0indirect
PyPIshap0.52.0indirect
PyPIsix1.17.0indirect
PyPIskops0.14.0indirect
PyPIslicer0.0.8indirect
PyPIsmmap5.0.3indirect
PyPIsnowflake-connector-python4.6.0indirect
PyPIsnowflake-ml-python—indirect
PyPIsnowflake-ml-python1.43.0indirect
PyPIsnowflake-snowpark-python1.52.0indirect
PyPIsortedcontainers2.4.0indirect
PyPIsqlalchemy2.0.51indirect
PyPIsqlparse0.5.5indirect
PyPIsse-starlette3.4.6indirect
PyPIstarlette1.3.1indirect
PyPItenacity9.1.4indirect
PyPIthreadpoolctl3.6.0indirect
PyPItomli2.4.1indirect
PyPItomlkit0.15.0indirect
PyPItqdm4.68.3indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2026.2indirect
PyPItzlocal5.4.3indirect
PyPIurllib32.7.0indirect
PyPIuuid-utils0.16.2indirect
PyPIuvicorn0.49.0indirect
PyPIvirtualenv21.5.1indirect
PyPIwaitress3.0.2indirect
PyPIwcwidth0.8.1indirect
PyPIwebsockets16.0indirect
PyPIwerkzeug3.1.8indirect
PyPIwheel0.47.0indirect
PyPIxgboost—indirect
PyPIxgboost3.2.0indirect
PyPIxgboost3.3.0indirect
PyPIxxhash3.7.0indirect
PyPIyarl1.24.2indirect
PyPIzipp4.1.0indirect
PyPIzstandard0.25.0indirect
Dependency advisories 0

Installing pypi:predikit@0.6.3 pulls in 12 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.

Raw JSON report machine-readable

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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.