Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-19 17:33 UTC

hotdata-dev / sdk-python

Python SDK for Hotdata

PythonNo license detected★ 4 stars⑂ 0 forkssince Mar 2026View on GitHub ↗

hotdata-dev/sdk-python holds a health index of 75 out of 100, placing it in the Good band. It scores highest on Vitality (90/100) and lowest on Community & Adoption (26/100). It was last updated today. 2 contributors account for most of its recent work.

75
overall / 100
Good

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.

75
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 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

HotdataOrganization
15 followers27 public repossince Aug 2025

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIhotdata0.12.02,507223 days agohotdataapi-clientdata-platform

Metrics by category

Vitality

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

90Excellent · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
20.8/36Commit cadence — 30/52 weeks with commits
18/18Commit volume — 193 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year193
human_commit_share0.51
days_since_last_push0
active_weeks_last_year30

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 22 releases published
36/36Release recency — latest release 3 days ago
27/27Release cadence — a release every ~13.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count22
latest_release_tagv0.12.0
releases_from_tagsno
days_since_latest_release3
mean_days_between_releases13.1

Community & Adoption

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

26At Risk · 17% of overall
How it's scored
7.7/60Stars — 4 stars
0/25Forks — 0 forks
0/15Watchers — 0 watchers
Inputs used
forks0
stars4
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
0/22.5License — no license file detected
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseno
readme_badges0
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno
How it's scored
45.3/80Monthly downloads — 2,507 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageshotdata
dependents—
ecosystemspypi
total_downloads—
monthly_downloads2,507
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?

67Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
12/22.5Commit distribution — top contributor authored 47% of commits
5.4/13.5Contributor breadth — 4 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled4
top_contributor_share0.468
How it's scored
36.4/42Issue resolution — 87% of issues closed
22.1/30PR acceptance — 151/205 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs151
open_issues2
closed_issues13
prs_merged_7d4
prs_decided_7d4
prs_merged_30d5
prs_decided_30d5
issue_closed_ratio0.867
closed_unmerged_prs54
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.
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
8.7/25Owner reach — 15 followers of hotdata-dev
12.9/25Track record — 27 public repos, account ~1 yr old
Inputs used
followers15
owner_typeOrganization
is_verifiedno
owner_loginhotdata-dev
public_repos27
account_age_days432

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 3 days ago
20/20Version history — 22 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageshotdata
ecosystemspypi
any_deprecatedno
min_days_since_publish3

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall
How it's scored
24/24CI workflows — 6 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.pylint]), setup.cfg ([flake8]), tox.ini
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

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://www.hotdata.dev
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepagehttps://www.hotdata.dev
docs_sitehttps://www.hotdata.dev
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

46Weak · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilesuv.lock
manifestspyproject.toml, requirements.txt, setup.cfg
has_codeql_workflowno
has_security_policyno
has_dependabot_configyes
Excluded from scoring (no data or not applicable): Dependency lockfiles. 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_packages8
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:hotdata@0.12.0 runtime dependency closure — what installing the published package pulls in — 8 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
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://www.hotdata.dev/llms.txt)
40/40Legible commit history — 51 of 51 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://www.hotdata.dev/llms.txt
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.pylint]), setup.cfg ([flake8]), tox.ini
11/11Static type checking — hotdata/py.typed
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 3 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configshotdata/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.03
How it's scored
27/45Type-checkable code — Python with type-check config (hotdata/py.typed)
54.1/55Manageable file sizes — 5/319 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes237,311
source_files_sampled319
oversized_source_files5

Key facts

4GitHub stars
4contributors
193commits, last 12 months
0days since last push
22releases
2bus factor
2open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Direct dependencies 4
RegistryPackageVersion constraintManifest
PyPIurllib3(>=2.8.0,<3.0.0)pyproject.toml
PyPIpython-dateutil(>=2.8.2)pyproject.toml
PyPIpydantic(>=2.4.0)pyproject.toml
PyPItyping-extensions(>=4.7.1)pyproject.toml
All dependencies 21

Full resolved dependency set from the GitHub dependency graph: 8 direct and 13 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIpydantic—direct
PyPIpydantic2.13.4direct
PyPIpython-dateutil—direct
PyPIpython-dateutil2.9.0.post0direct
PyPItyping-extensions—direct
PyPItyping-extensions4.15.0direct
PyPIurllib3—direct
PyPIurllib32.8.0direct
PyPIannotated-types0.7.0indirect
PyPIflake8—indirect
PyPIhotdata0.12.0indirect
PyPImypy—indirect
PyPIpyarrow—indirect
PyPIpyarrow24.0.0indirect
PyPIpydantic-core2.46.4indirect
PyPIpytest—indirect
PyPIpytest-cov—indirect
PyPIsix1.17.0indirect
PyPItox—indirect
PyPItypes-python-dateutil—indirect
PyPItyping-inspection0.4.2indirect
Dependency advisories 0

Installing pypi:hotdata@0.12.0 pulls in 8 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

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 v0.34.0 — full methodology · metrics wiki.

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