Public record
Software health reportschema 0.12.0 · metrics 2.10.0 · 2026-07-18 05:44 UTC

douglasmonsky / agent-maintainer

Maintainability checks and repair-loop diagnostics for AI-assisted Python repositories.

PythonMIT★ 0 stars⑂ 0 forkssince Jun 2026View on GitHub ↗
KindLibraryhow this is determined
#ai#ai-assisted-development#ci#code-quality#coding-agents#developer-tools#maintainability#pre-commit#python#repository-maintenance#static-analysis#testing#ai-agents#ai-tools#claude-code#codex#free#hooks#open-source#pypi-package

douglasmonsky/agent-maintainer holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (96/100) and lowest on Community & Adoption (49/100). It was last updated today. A single contributor accounts for most of its recent work.

80
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.

80
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 68 is calibrated to 80 on the published index scale (record calibration 2026-08-02).

Ownership

Douglas MonskyPersonal account
4 followers27 public repossince Jun 2017

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
PyPIagent-maintainer0.1.0b810,28060 days agoaicoding-agentsmaintainabilitycipythoncode-qualitydeveloper-tools

Metrics by category

Vitality

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

70Good · 21% of overall

Development activity

57Moderate
How it's scored
36/36Push recency — last push 0 days ago
2.8/36Commit cadence — 4/52 weeks with commits
18/18Commit volume — 1,175 commits in the last year
0/10OpenSSF Scorecard: Maintained — project was created within the last 90 days. Please review its contents carefully
Inputs used
commits_last_year1,175
human_commit_share—
days_since_last_push0
active_weeks_last_year4

Release discipline

90Excellent
How it's scored
27/27Ships releases — 7 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~3.4 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count7
latest_release_tagv0.1.0b8
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases3.4

Community & Adoption

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

49Weak · 17% of overall

Popularity & adoption

1Critical
How it's scored
0/60Stars — 0 stars
0/25Forks — 0 forks
0/15Watchers — 0 watchers
Inputs used
forks0
stars0
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized 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_badges—
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services—
has_pull_request_templateyes

Ecosystem adoption (downloads)

67Good
How it's scored
53.5/80Monthly downloads — 10,280 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesagent-maintainer
dependents—
ecosystemspypi
total_downloads—
monthly_downloads10,280
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?

56Moderate · 23% of overall

Maintainer resilience (bus factor)

12Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.1/22.5Commit distribution — top contributor authored 100% 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_share0.995

Issue & PR responsiveness

82Excellent
How it's scored
42/42Issue resolution — 100% of issues closed
29/30PR acceptance — 385/398 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/5 approved changesets -- score normalized to 0
Inputs used
merged_prs385
open_issues0
closed_issues5
prs_merged_7d—
prs_decided_7d—
prs_merged_30d—
prs_decided_30d—
issue_closed_ratio1
closed_unmerged_prs13
first_time_authors_30d—
first_time_prs_merged_30d—
first_time_prs_decided_30d—
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

47Weak
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
5/25Owner reach — 4 followers of douglasmonsky
22.5/25Track record — 27 public repos, account ~9 yr old
Inputs used
followers4
owner_typeUser
is_verified—
owner_logindouglasmonsky
public_repos27
account_age_days3,326
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 0 days ago
20/20Version history — 6 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesagent-maintainer
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
16/16Linter config — .flake8, .pylintrc
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 5 out of 5 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://github.com/douglasmonsky/agent-maintainer#readme
10/10Repository description
10/10Topics — 20 topics
10/10Wiki
Inputs used
topicsai, ai-assisted-development, ci, code-quality, coding-agents, developer-tools, maintainability, pre-commit, python, repository-maintenance, static-analysis, testing, ai-agents, ai-tools, claude-code, codex, free, hooks, open-source, pypi-package
has_wikiyes
homepagehttps://github.com/douglasmonsky/agent-maintainer#readme
docs_sitehttps://github.com/douglasmonsky/agent-maintainer#readme
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

62Moderate · 16% of overall

Security posture

62Moderate
How it's scored
6/7.5Binary-Artifacts — binaries present in source code
0/7.5Branch-Protection — no data
2.5/2.5CI-Tests — 5 out of 5 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/5 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
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
0/7.5Maintained — project was created within the last 90 days. Please review its contents carefully
5/5Packaging — packaging workflow detected
2/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
5/5SAST — SAST tool is run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
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_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate6.2
Excluded from scoring (no data or not applicable): Branch-Protection. Remaining weights renormalized.

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.

86Excellent · 4% of overall

Agent context & guidance

75Good
How it's scored
45/45Agent instructions — AGENTS.md, tests/fixtures/existing_app_onboarding/flat-service/AGENTS.md, tests/fixtures/existing_app_onboarding/uv-web-app/AGENTS.md
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share—
agent_instruction_filesAGENTS.md, tests/fixtures/existing_app_onboarding/flat-service/AGENTS.md, tests/fixtures/existing_app_onboarding/uv-web-app/AGENTS.md
agent_instruction_max_bytes6,364
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.

Verify loop (build / test / typecheck)

90Excellent
How it's scored
18/18One-command bootstrap — justfile
22/22Automated tests
11/11Lint / format config — .flake8, .pylintrc
11/11Static type checking — config/pyrightconfig.json, src/docsync/py.typed
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no data
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
4/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
Inputs used
has_nixno
has_testsyes
lockfilespackage-lock.json, uv.lock
has_dockerfileno
typed_languageno
bootstrap_filesjustfile
has_devcontainerno
has_linter_configyes
typecheck_configsconfig/pyrightconfig.json, src/docsync/py.typed
agent_commit_share—
toolchain_manifests—
dependency_bot_commit_share0
Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (config/pyrightconfig.json, src/docsync/py.typed)
55/55Manageable file sizes — 0/904 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes18,381
source_files_sampled904
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 — example, examples
Inputs used
example_dirsexample, examples
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

0GitHub stars
2contributors
1,175commits, last 12 months
0days since last push
7releases
1bus factor
0open issues
npm, PyPIpackage ecosystems

More detail

OpenSSF Scorecard 6.2 / 10
6.2aggregate

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 · 2026-07-18 05:44 UTC

8Binary-Artifactsbinaries present in source code
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
10CI-Tests5 out of 5 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/5 approved changesets -- score normalized to 0
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintainedproject was created within the last 90 days. Please review its contents carefully
10Packagingpackaging workflow detected
4Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 4
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPIdefusedxml>=0.7.1pyproject.toml
PyPIPyYAML>=6.0.3pyproject.toml
All dependencies 127

Full resolved dependency set from the GitHub dependency graph: 3 direct and 124 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIdefusedxml—direct
PyPIdefusedxml0.7.1direct
PyPIpyyaml—direct
npm@nodelib/fs.scandir2.1.5indirect
npm@nodelib/fs.stat2.0.5indirect
npm@nodelib/fs.walk1.2.8indirect
npm@sindresorhus/merge-streams4.0.0indirect
npm@taplo/cli0.7.0indirect
npm@types/debug4.1.13indirect
npm@types/katex0.16.8indirect
npm@types/ms2.1.0indirect
npm@types/unist2.0.11indirect
npmansi-regex6.2.2indirect
npmargparse2.0.1indirect
npmbraces3.0.3indirect
npmcharacter-entities2.0.2indirect
npmcharacter-entities-legacy3.0.0indirect
npmcharacter-reference-invalid2.0.1indirect
npmcommander8.3.0indirect
npmdebug4.4.3indirect
npmdecode-named-character-reference1.3.0indirect
npmdequal2.0.3indirect
npmdevlop1.1.0indirect
npmentities4.5.0indirect
npmfast-glob3.3.3indirect
npmfastq1.20.1indirect
npmfill-range7.1.1indirect
npmget-east-asian-width1.6.0indirect
npmglob-parent5.1.2indirect
npmglobby16.2.0indirect
npmignore7.0.5indirect
npmis-alphabetical2.0.1indirect
npmis-alphanumerical2.0.1indirect
npmis-decimal2.0.1indirect
npmis-extglob2.1.1indirect
npmis-glob4.0.3indirect
npmis-hexadecimal2.0.1indirect
npmis-number7.0.0indirect
npmis-path-inside4.0.0indirect
npmjs-yaml5.2.0indirect
npmjsonc-parser3.3.1indirect
npmjsonpointer5.0.1indirect
npmkatex0.16.47indirect
npmlinkify-it5.0.1indirect
npmmarkdown-it14.2.0indirect
npmmarkdownlint0.41.0indirect
npmmarkdownlint-cli20.23.0indirect
npmmarkdownlint-cli2-formatter-default0.0.6indirect
npmmdurl2.0.0indirect
npmmerge21.4.1indirect
npmmicromark4.0.2indirect
npmmicromark-core-commonmark2.0.3indirect
npmmicromark-extension-directive4.0.0indirect
npmmicromark-extension-gfm-autolink-literal2.1.0indirect
npmmicromark-extension-gfm-footnote2.1.0indirect
npmmicromark-extension-gfm-table2.1.1indirect
npmmicromark-extension-math3.1.0indirect
npmmicromark-factory-destination2.0.1indirect
npmmicromark-factory-label2.0.1indirect
npmmicromark-factory-space2.0.1indirect
npmmicromark-factory-title2.0.1indirect
npmmicromark-factory-whitespace2.0.1indirect
npmmicromark-util-character2.1.1indirect
npmmicromark-util-chunked2.0.1indirect
npmmicromark-util-classify-character2.0.1indirect
npmmicromark-util-combine-extensions2.0.1indirect
npmmicromark-util-decode-numeric-character-reference2.0.2indirect
npmmicromark-util-encode2.0.1indirect
npmmicromark-util-html-tag-name2.0.1indirect
npmmicromark-util-normalize-identifier2.0.1indirect
npmmicromark-util-resolve-all2.0.1indirect
npmmicromark-util-sanitize-uri2.0.1indirect
npmmicromark-util-subtokenize2.1.0indirect
npmmicromark-util-symbol2.0.1indirect
npmmicromark-util-types2.0.2indirect
npmmicromatch4.0.8indirect
npmms2.1.3indirect
npmparse-entities4.0.2indirect
npmpicomatch2.3.2indirect
npmpunycode.js2.3.1indirect
npmqueue-microtask1.2.3indirect
npmreusify1.1.0indirect
npmrun-parallel1.2.0indirect
npmslash5.1.0indirect
npmsmol-toml1.7.0indirect
npmstring-width8.2.1indirect
npmstrip-ansi7.2.0indirect
npmto-regex-range5.0.1indirect
npmuc.micro2.1.0indirect
npmunicorn-magic0.4.0indirect
PyPIactionlint-py1.7.12.24indirect
PyPIagent-maintainer—indirect
PyPIbandit1.9.4indirect
PyPIbuild1.5.1indirect
PyPIcheck-jsonschema0.37.4indirect
PyPIcoverage7.15.0indirect
PyPIcyclonedx-bom7.3.0indirect
PyPIdeptry0.25.1indirect
PyPIdiff-cover10.3.0indirect
PyPIhypothesis6.156.6indirect
PyPIimport-linter2.13indirect
PyPIinterrogate1.7.0indirect
PyPImcp—indirect
PyPImutmut3.6.0indirect
PyPIpip-audit2.10.1indirect
PyPIpip-licenses5.5.5indirect
PyPIpip-tools7.5.3indirect
PyPIpre-commit4.6.0indirect
PyPIpylint4.0.6indirect
PyPIpyright1.1.411indirect
PyPIpytest9.1.1indirect
PyPIpytest-cov7.1.0indirect
PyPIradon6.0.1indirect
PyPIruff0.15.21indirect
PyPIrust-just1.56.0indirect
PyPIsemgrep—indirect
PyPIsemgrep1.169.0indirect
PyPIsetuptools—indirect
PyPIsetuptools83.0.0indirect
PyPItach0.35.0indirect
PyPItwine6.2.0indirect
PyPIvulture2.16indirect
PyPIwemake-python-styleguide1.6.2indirect
PyPIwheel0.47.0indirect
PyPIxenon0.9.3indirect
PyPIyamllint1.38.0indirect
PyPIzizmor1.26.1indirect
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 v0.12.0 — full methodology · metrics wiki.

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