Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-16 13:03 UTC

scanny / python-pptx

Create Open XML PowerPoint documents in Python

PythonMIT★ 3,533 stars⑂ 737 forkssince Nov 2012View on GitHub ↗
KindLibraryCommand-line toolhow this is determined

scanny/python-pptx holds a health index of 40 out of 100, placing it in the Weak band. It scores highest on Engineering Quality (84/100) and lowest on Vitality (13/100). It was last updated 769 days ago. A single contributor accounts for most of its recent work.

40
overall / 100
Weak

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.

40
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 48 is calibrated to 47 on the published index scale (record calibration 2026-08-02).

Ownership

Steve CannyPersonal account
373 followers5 public repossince Jul 2012

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
PyPIpython-pptx1.0.252,132,58751769 days agopowerpointpptpptxopenxmloffice

Metrics by category

Vitality

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

13Critical · 21% of overall
How it's scored
0/36Push recency — last push 769 days ago
0/36Commit cadence — 0/52 weeks with commits
0/18Commit volume — 0 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year0
human_commit_share1
days_since_last_push769
active_weeks_last_year0
How it's scored
16.2/27Ships releases — 47 version tags (no GitHub releases)
0/36Release recency — latest release 769 days ago
12.6/27Release cadence — a release every ~229 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count47
latest_release_tagv1.0.2
releases_from_tagsyes
days_since_latest_release769
mean_days_between_releases229

Community & Adoption

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

79Good · 17% of overall
How it's scored
57.6/60Stars — 3,533 stars
23.9/25Forks — 737 forks
10.5/15Watchers — 78 watchers
Inputs used
forks737
stars3,533
watchers78
growth_stateorganic
growth_factor_pct100
How it's scored
22.5/22.5README
22.5/22.5License — recognized 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_badges0
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno
How it's scored
80/80Monthly downloads — 52,132,587 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagespython-pptx
dependents—
ecosystemspypi
total_downloads—
monthly_downloads52,132,587
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?

44Weak · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.8/22.5Commit distribution — top contributor authored 96% of commits
9.5/13.5Contributor breadth — 7 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled7
top_contributor_share0.965
How it's scored
22.1/42Issue resolution — 53% of issues closed
3.9/30PR acceptance — 13/99 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_prs13
open_issues451
closed_issues501
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.526
closed_unmerged_prs86
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 backing — personal (user) account
0/20Verified domain — not applicable to user accounts
18.5/25Owner reach — 373 followers of scanny
17.7/25Track record — 5 public repos, account ~14 yr old
Inputs used
followers373
owner_typeUser
is_verified—
owner_loginscanny
public_repos5
account_age_days5,161
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
4/35Publish recency — latest publish 769 days ago
20/20Version history — 51 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespython-pptx
ecosystemspypi
any_deprecatedno
min_days_since_publish769

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.black]), 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://python-pptx.readthedocs.io/en/latest/
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepage—
docs_sitehttps://python-pptx.readthedocs.io/en/latest/
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-docs.txt, requirements-test.txt, requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

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_packages4
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-pptx@1.0.2 runtime dependency closure — what installing the published package pulls in — 4 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.

61Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 97 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.97
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — Makefile, docs/Makefile, spec/gen_spec/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.black]), tox.ini
11/11Static type checking — src/pptx/py.typed
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, docs/Makefile, spec/gen_spec/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configssrc/pptx/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (src/pptx/py.typed)
54.5/55Manageable file sizes — 2/222 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes88,344
source_files_sampled222
oversized_source_files2

Key facts

3,533GitHub stars
7contributors
0commits, last 12 months
769days since last push
47releases
1bus factor
451open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Star history carried forward from 2026-07-21: GitHub restricted the stargazers API to repository admins, so it can no longer be collected. The repository has 3533 stars today.
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 3,462 ★ / 737 ⇿
3,462Stars
737Forks
47Releases

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

Star history is shown as collected on 2026-07-21. 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.

01,2502,5003,7503,462697402013-012019-112026-08
Major 1Minor 4Patch 42

Each point covers 13 days.

Direct dependencies 4
RegistryPackageVersion constraintManifest
PyPIPillow>=3.3.2pyproject.toml
PyPIXlsxWriter>=0.5.7pyproject.toml
PyPIlxml>=3.1.0pyproject.toml
PyPItyping_extensions>=4.9.0pyproject.toml
All dependencies 20

Full resolved dependency set from the GitHub dependency graph: 4 direct and 16 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIlxml—direct
PyPIpillow—direct
PyPItyping-extensions—direct
PyPIxlsxwriter—direct
PyPIalabaster—indirect
PyPIbehave—indirect
PyPIbuild—indirect
PyPIflake8—indirect
PyPIjinja22.11.3indirect
PyPImarkupsafe0.23indirect
PyPImock—indirect
PyPIpyparsing—indirect
PyPIpytest—indirect
PyPIpytest-coverage—indirect
PyPIpytest-xdist—indirect
PyPIruff—indirect
PyPIsetuptools—indirect
PyPIsphinx1.8.6indirect
PyPItox—indirect
PyPItwine—indirect
Dependency advisories 0

Installing pypi:python-pptx@1.0.2 pulls in 4 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.