Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-27 18:51 UTC

gtalarico / pyairtable

Python Api Client for Airtable

PythonMIT★ 893 stars⑂ 156 forkssince Aug 2017View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

gtalarico/pyairtable holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (100/100) and lowest on Security (21/100). It was last updated 10 days 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

Gui TalaricoPersonal account
702 followers55 public repossince Nov 2014

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
PyPIpyairtable3.4.24,257,4984832 days agoairtableapiclientpyairtable

Metrics by category

Vitality

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

69Good · 21% of overall
How it's scored
28.8/36Push recencylast push 10 days ago
9/36Commit cadence13/52 weeks with commits
16.8/18Commit volume73 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year73
human_commit_share0.99
days_since_last_push10
active_weeks_last_year13
How it's scored
16.2/27Ships releases44 version tags (no GitHub releases)
36/36Release recencylatest release 32 days ago
19.8/27Release cadencea release every ~68.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count44
latest_release_tag3.4.2
releases_from_tagsyes
days_since_latest_release32
mean_days_between_releases68.5

Community & Adoption

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

72Good · 17% of overall
How it's scored
47.9/60Stars893 stars
18.3/25Forks156 forks
7.4/15Watchers22 watchers
Inputs used
forks156
stars893
watchers22
growth_stateorganic
growth_factor_pct100
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicescodecov.io, github.com, readthedocs.org, shields.io
has_pull_request_templateno
How it's scored
80/80Monthly downloads4,257,498 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagespyairtable
dependents
ecosystemspypi
total_downloads
monthly_downloads4,257,498
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?

71Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
10.6/22.5Commit distributiontop contributor authored 53% of commits
13.5/13.5Contributor breadth38 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor1
contributors_sampled38
top_contributor_share0.528
How it's scored
42/42Issue resolution100% of issues closed
23.8/30PR acceptance214/270 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs214
open_issues0
closed_issues195
prs_merged_7d0
prs_decided_7d0
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio1
closed_unmerged_prs56
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
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
20.5/25Owner reach702 followers of gtalarico
24.7/25Track record55 public repos, account ~11 yr old
Inputs used
followers702
owner_typeUser
is_verified
owner_logingtalarico
public_repos55
account_age_days4,316
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 32 days ago
20/20Version history48 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagespyairtable
ecosystemspypi
any_deprecatedno
min_days_since_publish32

Engineering Quality

Are baseline engineering and documentation practices in place?

100Exceptional · 19% of overall

Engineering practices

100Exceptional
How it's scored
24/24CI workflows1 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.black]), tox.ini
9.6/9.6Pre-commit hooks
6.4/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigyes
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://pyairtable.readthedocs.io
10/10Repository description
10/10Topics5 topics
10/10Wiki
Inputs used
topicspython, airtable, api, client, api-wrapper
has_wikiyes
homepagehttps://pyairtable.readthedocs.io
docs_sitehttps://pyairtable.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestspyproject.toml, requirements-dev.txt, requirements-test.txt, setup.cfg, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages11
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:pyairtable@3.4.2 runtime dependency closure — what installing the published package pulls in — 11 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.

81Excellent · 4% of overall
How it's scored
45/45Agent instructions.claude/CLAUDE.md, AGENTS.md
0/15Machine-readable docs (llms.txt)
23.7/40Legible commit history44 of 99 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.444
agent_instruction_files.claude/CLAUDE.md, AGENTS.md
agent_instruction_max_bytes581
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.black]), tox.ini
11/11Static type checkingpyairtable/py.typed
0/10Reproducible environment
10/10Demonstrated agent practice16 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance1 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configspyairtable/py.typed
agent_commit_share0.16
toolchain_manifests
dependency_bot_commit_share0.01
How it's scored
27/45Type-checkable codePython with type-check config (pyairtable/py.typed)
55/55Manageable file sizes0/65 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes50,024
source_files_sampled65
oversized_source_files0

Key facts

893GitHub stars
38contributors
73commits, last 12 months
10days since last push
44releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Star history carried forward from 2026-07-15: GitHub restricted the stargazers API to repository admins, so it can no longer be collected. The repository has 893 stars today.
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 892 ★ / 156 ⇿
892Stars
156Forks
44Releases

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.

Star history is shown as collected on 2026-07-15. GitHub restricted the stargazers API to repository administrators in July 2026, so this series can no longer be extended; the current star total above remains live.

02004006008001,000892154122017-082022-012026-07
Major 3Minor 20Patch 21

Each point covers 9 days.

Direct dependencies 5
RegistryPackageVersion constraintManifest
PyPIinflectionsetup.cfg
PyPIpydantic>= 2, < 3setup.cfg
PyPIrequests>= 2.22.0setup.cfg
PyPItyping_extensionssetup.cfg
PyPIurllib3>= 1.26setup.cfg
All dependencies 33

Full resolved dependency set from the GitHub dependency graph: 5 direct and 28 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIinflectiondirect
PyPIpydanticdirect
PyPIrequestsdirect
PyPItyping-extensionsdirect
PyPIurllib3direct
PyPIautodoc-pydanticindirect
PyPIblackindirect
PyPIbuild0.6.0.post1indirect
PyPIclickindirect
PyPIcodecovindirect
PyPIcogappindirect
PyPIflake8indirect
PyPImockindirect
PyPImypyindirect
PyPIpre-commitindirect
PyPIpytestindirect
PyPIpytest-covindirect
PyPIrequests-mockindirect
PyPIrevitron-sphinx-themeindirect
PyPIsphinx4.5.0indirect
PyPIsphinx-autoapiindirect
PyPIsphinx-autodoc-typehintsindirect
PyPIsphinxcontrib-applehelp1.0.4indirect
PyPIsphinxcontrib-devhelp1.0.2indirect
PyPIsphinxcontrib-htmlhelp2.0.1indirect
PyPIsphinxcontrib-qthelp1.0.3indirect
PyPIsphinxcontrib-serializinghtml1.1.5indirect
PyPIsphinxext-opengraphindirect
PyPItoxindirect
PyPItwine3.3.0indirect
PyPItypes-requestsindirect
PyPItypes-urllib3indirect
PyPIwheel0.46.2indirect
Dependency advisories 0

Installing pypi:pyairtable@3.4.2 pulls in 11 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 statisticsPyPI.