Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 03:48 UTC

fabiocaccamo / python-benedict

:blue_book: dict subclass with keylist/keypath support, built-in I/O operations (base64, csv, html, ini, json, pickle, plist, query-string, toml, xls, xml, yaml), s3 support and many utilities.

Python · HTMLMIT★ 1,609 stars⑂ 50 forkssince May 2019View on GitHub ↗
KindDesktop applicationLibraryhow this is determined

fabiocaccamo/python-benedict holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on Community & Adoption (86/100) and lowest on AI Readiness (51/100). It was last updated 2 days ago. A single contributor accounts for most of its recent work.

86
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.

86
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 72 is calibrated to 86 on the published index scale (record calibration 2026-08-02).

Ownership

Fabio CaccamoPersonal account
583 followers37 public repossince Sep 2011

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

Package ecosystems

Metrics by category

Vitality

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

78Good · 21% of overall
How it's scored
36/36Push recencylast push 2 days ago
9/36Commit cadence13/52 weeks with commits
17.2/18Commit volume81 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year81
human_commit_share0.84
days_since_last_push2
active_weeks_last_year13
How it's scored
27/27Ships releases71 releases published
36/36Release recencylatest release 36 days ago
19.8/27Release cadencea release every ~113.7 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count71
latest_release_tag0.38.0
releases_from_tagsno
days_since_latest_release36
mean_days_between_releases113.7

Community & Adoption

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

86Excellent · 17% of overall
How it's scored
52/60Stars1,609 stars
14.1/25Forks50 forks
5.8/15Watchers12 watchers
Inputs used
forks50
stars1,609
watchers12
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges12
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesapi.securityscorecards.dev, shields.io
has_pull_request_templateyes
How it's scored
80/80Monthly downloads1,311,725 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagespython-benedict
dependents
ecosystemspypi
total_downloads
monthly_downloads1,311,725
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?

62Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
0.3/22.5Commit distributiontop contributor authored 99% of commits
13.5/13.5Contributor breadth10 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor1
contributors_sampled10
top_contributor_share0.988
How it's scored
40.2/42Issue resolution96% of issues closed
18.4/30PR acceptance283/462 decided PRs merged
0/13Newcomer PR acceptance0/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs283
open_issues5
closed_issues110
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d1
issue_closed_ratio0.957
closed_unmerged_prs179
first_time_authors_30d1
first_time_prs_merged_30d0
first_time_prs_decided_30d1
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
19.9/25Owner reach583 followers of fabiocaccamo
23.5/25Track record37 public repos, account ~14 yr old
Inputs used
followers583
owner_typeUser
is_verified
owner_loginfabiocaccamo
public_repos37
account_age_days5,452
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 36 days ago
20/20Version history71 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagespython-benedict
ecosystemspypi
any_deprecatedno
min_days_since_publish36

Engineering Quality

Are baseline engineering and documentation practices in place?

75Good · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter configtox.ini
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics20 topics
0/10Wiki
Inputs used
topicspython, dict, dictionary, keypath, base64, csv, json, pickle, plist, query-string, toml, xml, yaml, encode, decode, filter, flatten, subset, traverse, xls
has_wikino
homepage
docs_site
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

64Moderate · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestspyproject.toml, requirements-release.txt, requirements-test.txt, requirements.txt, setup.py
has_codeql_workflowno
has_security_policyyes
has_dependabot_configyes

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_packages3
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:python-benedict@0.38.0 runtime dependency closure — what installing the published package pulls in — 3 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.

51Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
10.8/40Legible commit history17 of 84 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.202
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configtox.ini
11/11Static type checkingbenedict/py.typed
0/10Reproducible environment
10/10Demonstrated agent practice8 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance14 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_files
has_devcontainerno
has_linter_configyes
typecheck_configsbenedict/py.typed
agent_commit_share0.08
toolchain_manifests
dependency_bot_commit_share0.14
How it's scored
27/45Type-checkable codePython with type-check config (benedict/py.typed)
54.7/55Manageable file sizes1/185 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes61,321
source_files_sampled185
oversized_source_files1

Key facts

1,609GitHub stars
10contributors
81commits, last 12 months
2days since last push
71releases
1bus factor
5open 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

Star and fork history 0 ★ / 50 ⇿
0Stars
50Forks
71Releases

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.

010203040504932019-122023-032026-07
Major 0Minor 38Patch 33

Each point covers 7 days.

Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPIpython-slugify>= 7.0.0, < 9.0.0pyproject.toml
PyPItyping_extensions>= 4.13.2, < 4.16.0pyproject.toml
All dependencies 47

Full resolved dependency set from the GitHub dependency graph: 3 direct and 44 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIpython-slugifydirect
PyPIpython-slugify8.0.4direct
PyPItyping-extensionsdirect
PyPIbeautifulsoup44.15.0indirect
PyPIboto31.43.38indirect
PyPIboto3-stubs1.43.38indirect
PyPIbuildindirect
PyPIbuild1.5.*indirect
PyPIcertifiindirect
PyPIcoverageindirect
PyPIcoverage7.14.*indirect
PyPIcyclonedx-bomindirect
PyPIcyclonedx-bom7.3.*indirect
PyPIdecouple-types1.0.2indirect
PyPIftfy6.3.1indirect
PyPIidnaindirect
PyPImailchecker6.0.20indirect
PyPImypyindirect
PyPImypy2.1.*indirect
PyPIopenpyxl3.1.5indirect
PyPIorjsonindirect
PyPIorjson3.11.*indirect
PyPIphonenumbers9.0.33indirect
PyPIpip-licensesindirect
PyPIpip-licenses5.5.*indirect
PyPIpre-commitindirect
PyPIpre-commit4.6.*indirect
PyPIpydanticindirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-decouple3.8indirect
PyPIpython-fsutil0.17.0indirect
PyPIpyyaml6.0.3indirect
PyPIrequests2.34.2indirect
PyPItomli2.4.1indirect
PyPItomli-w1.2.0indirect
PyPItoxindirect
PyPItox4.56.*indirect
PyPItypes-beautifulsoup44.12.0.20250516indirect
PyPItypes-html5lib1.1.11.20260518indirect
PyPItypes-openpyxl3.1.5.20260518indirect
PyPItypes-python-dateutil2.9.0.20260518indirect
PyPItypes-pyyaml6.0.12.20260518indirect
PyPItypes-toml0.10.8.20260518indirect
PyPItypes-xmltodict1.0.1.20260518indirect
PyPIurllib3indirect
PyPIxlrd2.0.2indirect
PyPIxmltodict1.0.4indirect
Dependency advisories 0

Installing pypi:python-benedict@0.38.0 pulls in 3 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 v0.31.0 — full methodology · metrics wiki.

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