Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-13 14:14 UTC

MartinPdeS / PackLab

Python/C++ toolkit for 3D hard-sphere systems: Percus–Yevick correlations, RSA packing, Metropolis Monte Carlo, and structure-aware scattering.

C++ · PythonMIT★ 1 star⑂ 0 forkssince Nov 2025View on GitHub ↗

MartinPdeS/PackLab holds a health index of 62 out of 100, placing it in the Moderate band. It scores highest on Engineering Quality (95/100) and lowest on Security (33/100). It was last updated today. A single contributor accounts for most of its recent work.

62
overall / 100
Moderate

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.

62
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 58 is calibrated to 62 on the published index scale (record calibration 2026-08-02).

Ownership

18 followers24 public repossince Feb 2017Amsterdam Medical Center

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
PyPIPackLab0.7.132,281200 days agorsapackingparticlesspheressimulation

Metrics by category

Vitality

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

71Good · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
3.5/36Commit cadence — 5/52 weeks with commits
16.9/18Commit volume — 76 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year76
human_commit_share1
days_since_last_push0
active_weeks_last_year5
How it's scored
27/27Ships releases — 2 releases published
36/36Release recency — latest release 0 days ago
12.6/27Release cadence — a release every ~151 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count2
latest_release_tagv0.7.13
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases151

Community & Adoption

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

39Weak · 17% of overall
How it's scored
0/60Stars — 1 stars
0/25Forks — 0 forks
0/15Watchers — 0 watchers
Inputs used
forks0
stars1
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno
How it's scored
44.8/80Monthly downloads — 2,281 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesPackLab
dependents—
ecosystemspypi
total_downloads—
monthly_downloads2,281
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
0/22.5Commit distribution — top contributor authored 100% of commits
1.4/13.5Contributor breadth — 1 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1
How it's scored
0/42Issue resolution — no issues or no data
9/30PR acceptance — 6/20 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_prs6
open_issues0
closed_issues0
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio—
closed_unmerged_prs14
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Issue resolution, Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
9.2/25Owner reach — 18 followers of MartinPdeS
22.2/25Track record — 24 public repos, account ~9 yr old
Inputs used
followers18
owner_typeUser
is_verified—
owner_loginMartinPdeS
public_repos24
account_age_days3,522
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 — 20 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesPackLab
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall
How it's scored
24/24CI workflows — 5 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

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://martinpdes.github.io/PackLab/
10/10Repository description
10/10Topics — 9 topics
10/10Wiki
Inputs used
topicscpp, hard-spheres, monte-carlo, packing, percus-yevick, python, scientific-computing, sphere, structure-factor
has_wikiyes
homepagehttps://martinpdes.github.io/PackLab/
docs_sitehttps://martinpdes.github.io/PackLab/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

33At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml
has_codeql_workflowno
has_security_policyno
has_dependabot_configyes
Excluded from scoring (no data or not applicable): Dependency lockfiles. 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.

76Good · 4% of overall
How it's scored
45/45Agent instructions — .github/copilot-instructions.md, AGENTS.md
0/15Machine-readable docs (llms.txt)
1.4/40Legible commit history — 2 of 78 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.026
agent_instruction_files.github/copilot-instructions.md, AGENTS.md
agent_instruction_max_bytes6,283
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — PackLab/py.typed
0/10Reproducible environment
5.1/10Demonstrated agent practice — 2 of the last 78 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageyes
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configsPackLab/py.typed
agent_commit_share0.026
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
45/45Type-checkable code — C++ (statically typed)
55/55Manageable file sizes — 0/110 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes19,667
source_files_sampled110
oversized_source_files0
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 — examples, 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

1GitHub stars
1contributors
76commits, last 12 months
0days since last push
2releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • deps.dev does not index pypi:PackLab@0.7.13; advisories assessed against the repository dependency graph instead
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Direct dependencies 3
RegistryPackageVersion constraintManifest
PyPImatplotlib—pyproject.toml
PyPInumpy—pyproject.toml
PyPITypedUnit—pyproject.toml
All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

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.