Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-15 14:04 UTC

eoleedi / TimeTree-Exporter

A Tool for Exporting TimeTree Calendar to iCal / 將 TimeTree 日曆匯出成 iCal 格式

PythonMIT★ 176 stars⑂ 35 forkssince Dec 2023View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

eoleedi/TimeTree-Exporter holds a health index of 75 out of 100, placing it in the Good band. It scores highest on Vitality (81/100) and lowest on Community & Adoption (49/100). It was last updated 4 days ago. A single contributor accounts for most of its recent work.

75
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.

75
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 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

蔡鳳駿Personal account
30 followers52 public repossince Apr 2019SMIL Lab, National Taiwan Normal University

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
PyPItimetree-exporter0.9.0-206 days agoexportericalendaricstimetree

Metrics by category

Vitality

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

81Excellent · 21% of overall
How it's scored
36/36Push recency — last push 4 days ago
11.8/36Commit cadence — 17/52 weeks with commits
17.7/18Commit volume — 92 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year92
human_commit_share0.57
days_since_last_push4
active_weeks_last_year17
How it's scored
27/27Ships releases — 20 releases published
36/36Release recency — latest release 6 days ago
19.8/27Release cadence — a release every ~57.4 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count20
latest_release_tagv0.9.0
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases57.4

Community & Adoption

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

49Weak · 17% of overall
How it's scored
36.4/60Stars — 176 stars
12.8/25Forks — 35 forks
0/15Watchers — 2 watchers
Inputs used
forks35
stars176
watchers2
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
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_badges5
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
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 factor — 1 contributor(s) cover half of all commits
0.2/22.5Commit distribution — top contributor authored 99% of commits
4.1/13.5Contributor breadth — 3 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled3
top_contributor_share0.989
How it's scored
42/42Issue resolution — 100% of issues closed
22.5/30PR acceptance — 132/176 decided PRs merged
13/13Newcomer PR acceptance — 1/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs132
open_issues0
closed_issues30
prs_merged_7d1
prs_decided_7d1
prs_merged_30d2
prs_decided_30d2
issue_closed_ratio1
closed_unmerged_prs44
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
10.7/25Owner reach — 30 followers of eoleedi
24.6/25Track record — 52 public repos, account ~7 yr old
Inputs used
followers30
owner_typeUser
is_verified—
owner_logineoleedi
public_repos52
account_age_days2,724
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 6 days ago
20/20Version history — 20 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagestimetree-exporter
ecosystemspypi
any_deprecatedno
min_days_since_publish6

Engineering Quality

Are baseline engineering and documentation practices in place?

75Good · 19% of overall
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-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_configyes

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 6 topics
0/10Wiki
Inputs used
topicscalendar, export, exporter, ics, timetree, python
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?

60Moderate · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
25/25Dependency lockfiles — uv.lock
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilesuv.lock
manifestspyproject.toml
has_codeql_workflowno
has_security_policyno
has_dependabot_configyes

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_packages9
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:timetree-exporter@0.9.0 runtime dependency closure — what installing the published package pulls in — 9 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 instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 56 of 57 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.982
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 43 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.43
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/20 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes17,448
source_files_sampled20
oversized_source_files0

Key facts

176GitHub stars
3contributors
92commits, last 12 months
4days since last push
20releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'timetree-exporter' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • OpenSSF Scorecard did not return a usable result (exit code 0; Skipping github.com/eoleedi/TimeTree-Exporter: run: internal error: ListCommits:error during graphqlHandler.setup: internal error: githubv4.Query: Something went wrong while executing your query on 2026-09-15T14:04:09Z. Please include `CAA2:1F8920:A7FF4EB:A3CE340:6AA95056` when reporting this issue.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 35 ⇿
0Stars
35Forks
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.

01325383512024-032025-062026-09
Major 0Minor 8Patch 11

Each point covers 3 days.

Direct dependencies 4
RegistryPackageVersion constraintManifest
PyPIicalendar>=6.1.0,<8.0.0pyproject.toml
PyPItzdata>=2024.2,<2027.0pyproject.toml
PyPIrequests<3.0.0,>=2.32.5pyproject.toml
PyPIpwinput-eoleedi>=1.0.3.post1,<2.0.0.0pyproject.toml
All dependencies 33

Full resolved dependency set from the GitHub dependency graph: 4 direct and 29 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIicalendar7.3.0direct
PyPIpwinput-eoleedi1.0.3.post1direct
PyPIrequests2.34.2direct
PyPItzdata2026.3direct
PyPIcertifi2026.5.20indirect
PyPIcfgv3.5.0indirect
PyPIcharset-normalizer3.4.7indirect
PyPIcolorama0.4.6indirect
PyPIcoverage7.14.0indirect
PyPIdistlib0.4.0indirect
PyPIexceptiongroup1.3.1indirect
PyPIfilelock3.29.0indirect
PyPIidentify2.6.19indirect
PyPIidna3.16indirect
PyPIiniconfig2.3.0indirect
PyPInodeenv1.10.0indirect
PyPIpackaging26.2indirect
PyPIplatformdirs4.9.6indirect
PyPIpluggy1.6.0indirect
PyPIpre-commit4.6.0indirect
PyPIpygments2.20.0indirect
PyPIpytest9.1.1indirect
PyPIpytest-cov7.1.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-discovery1.3.1indirect
PyPIpyyaml6.0.3indirect
PyPIruff0.16.6indirect
PyPIsix1.17.0indirect
PyPItimetree-exporter0.9.0indirect
PyPItomli2.4.1indirect
PyPItyping-extensions4.15.0indirect
PyPIurllib32.7.0indirect
PyPIvirtualenv21.3.3indirect
Dependency advisories 0

Installing pypi:timetree-exporter@0.9.0 pulls in 9 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.