Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-23 15:54 UTC

lebrice / SimpleParsing

Simple, Elegant, Typed Argument Parsing with argparse

PythonMIT★ 546 stars⑂ 63 forkssince Oct 2019View on GitHub ↗

lebrice/SimpleParsing holds a health index of 65 out of 100, placing it in the Good band. It scores highest on Engineering Quality (74/100) and lowest on Security (44/100). It was last updated 7 days ago. A single contributor accounts for most of its recent work.

65
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.

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

Ownership

Fabrice NormandinPersonal account
118 followers108 public repossince Jul 2015Mila - Quebec Artificial Intelligence Institute @mila-iqia

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 publish
PyPIsimple-parsing0.1.93,244,8007426 days ago

Metrics by category

Vitality

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

59Moderate · 21% of overall
How it's scored
36/36Push recencylast push 7 days ago
1.4/36Commit cadence2/52 weeks with commits
4.3/18Commit volume2 commits in the last year
1/10OpenSSF Scorecard: Maintained1 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 1
Inputs used
commits_last_year2
human_commit_share1
days_since_last_push7
active_weeks_last_year2
How it's scored
27/27Ships releases19 releases published
36/36Release recencylatest release 26 days ago
12.6/27Release cadencea release every ~136.8 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count19
latest_release_tagv0.1.9
releases_from_tagsno
days_since_latest_release26
mean_days_between_releases136.8
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?

57Moderate · 17% of overall
How it's scored
44.4/60Stars546 stars
14.9/25Forks63 forks
5/15Watchers9 watchers
Inputs used
forks63
stars546
watchers9
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
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_badges2
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesbadge.fury.io, github.com
has_pull_request_templateno

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
3.2/22.5Commit distributiontop contributor authored 86% of commits
13.5/13.5Contributor breadth32 contributors
6/10OpenSSF Scorecard: Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled32
top_contributor_share0.858
How it's scored
27.4/42Issue resolution65% of issues closed
25.7/30PR acceptance151/176 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
6/15OpenSSF Scorecard: Code-ReviewFound 14/30 approved changesets -- score normalized to 4
Inputs used
merged_prs151
open_issues59
closed_issues111
prs_merged_7d0
prs_decided_7d0
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio0.653
closed_unmerged_prs25
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
14.9/25Owner reach118 followers of lebrice
25/25Track record108 public repos, account ~11 yr old
Inputs used
followers118
owner_typeUser
is_verified
owner_loginlebrice
public_repos108
account_age_days4,054
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 26 days ago
20/20Version history74 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagessimple-parsing
ecosystemspypi
any_deprecatedno
min_days_since_publish26

Engineering Quality

Are baseline engineering and documentation practices in place?

74Good · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests2 out of 29 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics5 topics
0/10Wiki
Inputs used
topicsdataclasses, python, argument-parsing, argparse, argparse-alternative
has_wikino
homepage
docs_site
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

44Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
0/2.5CI-Tests2 out of 29 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
3/7.5Code-ReviewFound 14/30 approved changesets -- score normalized to 4
1.5/2.5Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0.8/7.5Maintained1 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 1
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities19 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate3
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

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_packages2
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:simple-parsing@0.1.9 runtime dependency closure — what installing the published package pulls in — 2 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.

62Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history89 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.89
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.ruff])
11/11Static type checkingsimple_parsing/py.typed
10/10Reproducible environmentlockfile
2/10Demonstrated agent practice1 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configssimple_parsing/py.typed
agent_commit_share0.01
toolchain_manifests
dependency_bot_commit_share0
How it's scored
27/45Type-checkable codePython with type-check config (simple_parsing/py.typed)
54.6/55Manageable file sizes1/151 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes62,014
source_files_sampled151
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples, notebooks
Inputs used
example_dirsexamples, notebooks
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

546GitHub stars
32contributors
2commits, last 12 months
7days since last push
19releases
1bus factor
59open 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 ★ / 63 ⇿
0Stars
63Forks
19Releases

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.

01325385063756332020-062023-072026-08
Major 0Minor 1Patch 15

Each point covers 6 days.

OpenSSF Scorecard 3.0 / 10
3.0aggregate

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 · 2026-08-23 15:54 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
0CI-Tests2 out of 29 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
4Code-ReviewFound 14/30 approved changesets -- score normalized to 4
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
1Maintained1 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 1
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities19 existing vulnerabilities detected
Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPIdocstring-parser~=0.15pyproject.toml
PyPItyping-extensions>=4.5.0pyproject.toml
All dependencies 5

Full resolved dependency set from the GitHub dependency graph: 2 direct and 3 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIdocstring-parserdirect
PyPItyping-extensionsdirect
PyPImatplotlibindirect
PyPInumpyindirect
PyPIorionindirect
Dependency advisories 0

Installing pypi:simple-parsing@0.1.9 pulls in 2 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.