Öffentliches RegisterGefährdet
Software-GesundheitsberichtSchema 0.26.0 · Metriken 1.13.0 · 2026-07-22 00:12 UTC
pymc-devs / pymc
Bayesian Modeling and Probabilistic Programming in Python
pymc-devs/pymc erreicht einen Gesundheitsindex von 44 von 100 und liegt damit im Bereich Gefährdet. Am stärksten schneidet es bei Vitality (98/100) ab, am schwächsten bei AI Readiness (52/100). Zuletzt heute aktualisiert. 6 Mitwirkende tragen den Großteil der jüngsten Arbeit.
44
gesamt / 100Software-Gesundheitsindex
Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.
44
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Die Richtlinie für Hochrisikojurisdiktionen wendet einen Multiplikator von 50 % auf die gewichtete Gesamtgesundheit an und begrenzt sie auf die Obergrenze Gefährdet von 49.
Eigentümerschaft
PyMCOrganisation
619 Follower44 öffentliche Reposseit Mai 2009
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht |
|---|---|---|---|---|---|
| PyPI | pymc | 6.1.0 | - | 101 | vor 14 Tagen |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
98
Entwicklungsaktivität
96Exzellent
Wie die Bewertung erfolgt
| 36/36 | Push-Aktualität — letzter Push vor 0 Tagen |
| 32.5/36 | Commit-Rhythmus — 47/52 Wochen mit Commits |
| 18/18 | Commit-Volumen — 263 Commits im letzten Jahr |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 18 issue activity found in the last 90 days -- score normalized to 10 |
Verwendete Eingangsdaten
| commits_last_year | 263 |
| human_commit_share | 0,98 |
| days_since_last_push | 0 |
| active_weeks_last_year | 47 |
Release-Disziplin
100Exzellent
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 100 Releases veröffentlicht |
| 36/36 | Release-Aktualität — letztes Release vor 14 Tagen |
| 27/27 | Release-Rhythmus — ein Release etwa alle 18 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 100 |
| latest_release_tag | v6.1.0 |
| releases_from_tags | nein |
| days_since_latest_release | 14 |
| mean_days_between_releases | 18 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
92
Popularität & Verbreitung
98Exzellent
Wie die Bewertung erfolgt
| 60/60 | Stars — 9.679 Stars |
| 25/25 | Forks — 2.265 Forks |
| 13/15 | Watcher — 218 Watcher |
Verwendete Eingangsdaten
| forks | 2.265 |
| stars | 9.679 |
| watchers | 218 |
| growth_state | organic |
| growth_factor_pct | 100 |
Community-Gesundheit
86Exzellent
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 16.9/22.5 | Lizenz — Lizenzdatei vorhanden, keine anerkannte Lizenz |
| 18/18 | CONTRIBUTING-Leitfaden |
| 13.5/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 6.3/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| has_contributing | ja |
| has_issue_template | nein |
| has_code_of_conduct | ja |
| has_pull_request_template | ja |
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
88
Maintainer-Resilienz (Bus-Faktor)
91Exzellent
Wie die Bewertung erfolgt
| 48.6/54 | Bus-Faktor — 6 Beitragende decken die Hälfte aller Commits ab |
| 19.2/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 14 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 98 Beitragende |
| 10/10 | OpenSSF Scorecard: Contributors — project has 70 contributing companies or organizations |
Verwendete Eingangsdaten
| bus_factor | 6 |
| contributors_sampled | 98 |
| top_contributor_share | 0,145 |
Reaktionsfähigkeit bei Issues & PRs
88Exzellent
Wie die Bewertung erfolgt
| 42.7/46.8 | Issue-Lösungsquote — 91 % der Issues geschlossen |
| 31.3/38.3 | PR-Annahme — 3.661/4.480 entschiedene PRs gemergt |
| 13.5/15 | OpenSSF Scorecard: Code-Review — Found 12/13 approved changesets -- score normalized to 9 |
Verwendete Eingangsdaten
| merged_prs | 3.661 |
| open_issues | 305 |
| closed_issues | 3.220 |
| issue_closed_ratio | 0,913 |
| closed_unmerged_prs | 819 |
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 20.1/25 | Reichweite des Inhabers — 619 Follower von pymc-devs |
| 24/25 | Kontohistorie — 44 öffentliche Repos, Kontoalter ca. 17 Jahre |
Verwendete Eingangsdaten
| followers | 619 |
| owner_type | Organization |
| is_verified | — |
| owner_login | pymc-devs |
| public_repos | 44 |
| account_age_days | 6.286 |
Paketpflege
100Exzellent
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 14 Tagen |
| 20/20 | Versionshistorie — 101 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | pymc |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 14 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
96
Engineering-Praktiken
94Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 10 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard: CI-Tests — 13 out of 13 merged PRs checked by a CI test -- score normalized to 10 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
100Exzellent
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 15/15 | Dokumentations-/Homepage-Site — https://www.pymc.io |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 7 Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | python, statistical-analysis, bayesian-inference, mcmc, variational-inference, probabilistic-programming, pytensor |
| has_wiki | ja |
| homepage | https://www.pymc.io |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
52
Sicherheitslage
40Gefährdet
Jurisdiktionsrisiko-Warnung
Exposition gegenüber einer Hochrisikojurisdiktion erkannt
1 Profile wichtiger MitwirkenderRussiaSicherheit und Gesamtbewertung: Obergrenze Gefährdet
Grundlage sind selbst veröffentlichte öffentliche Profilstandorte. Dieses Governance-Signal bestimmt weder Nationalität, Staatsangehörigkeit, Absicht, Sanktionsstatus noch persönliche Vertrauenswürdigkeit.
So wird Jurisdiktionsexposition bewertetWie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — keine Daten |
| 2.5/2.5 | CI-Tests — 13 out of 13 merged PRs checked by a CI test -- score normalized to 10 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 6.8/7.5 | Code-Review — Found 12/13 approved changesets -- score normalized to 9 |
| 2.5/2.5 | Contributors — project has 70 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.2/2.5 | Lizenz — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 18 issue activity found in the last 90 days -- score normalized to 10 |
| 5/5 | Packaging — packaging workflow detected |
| 4/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 8 |
| 5/5 | SAST — SAST tool is run on all commits |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — keine Daten |
| 7.5/7.5 | Token-Permissions — GitHub workflow tokens follow principle of least privilege |
| 3/7.5 | Vulnerabilities — 6 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 7,9 |
| high_risk_jurisdiction_cap | 49 |
| high_risk_jurisdiction_multiplier | 50 |
| security_posture_after_multiplier | 40 |
| security_posture_before_jurisdiction | 79 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, signed_releases. Die verbleibenden Gewichte wurden renormalisiert. Die Richtlinie für Hochrisikojurisdiktionen wendet einen Multiplikator von 50 % an und begrenzt die Sicherheitslage auf die Obergrenze Gefährdet von 49.
Abhängigkeits-Advisories
100Exzellent
Wie die Bewertung erfolgt
| 35/35 | Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory |
| 25/25 | Indirekte Abhängigkeiten ohne bekannte Advisories — keine indirekte Abhängigkeit trägt ein bekanntes Advisory |
| 0/40 | Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum |
Verwendete Eingangsdaten
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 32 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:pymc@6.1.0 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 32 Paketen. Erreichbarkeit wird nicht analysiert.
AI Readiness
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.
52
AI-Agentenkontext & Leitlinien
23Kritisch
Wie die Bewertung erfolgt
| 0/45 | Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 22.9/40 | Lesbare Commit-Historie — 42 von 98 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text) |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| legible_history_share | 0,429 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Wie die Bewertung erfolgt
| 18/18 | Bootstrap mit einem Befehl — Makefile |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 0/11 | Statische Typprüfung |
| 10/10 | Reproduzierbare Umgebung — Dockerfile |
| 0/10 | Belegte Agentenpraxis — keine von Agenten verfassten Commits unter den letzten 100 |
| 8/8 | Automatisierte Wartung — 2 der letzten 100 Commits sind automatisierte Abhängigkeits-Updates |
| 8/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 8 |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | — |
| has_dockerfile | ja |
| typed_language | nein |
| bootstrap_files | Makefile |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0,02 |
AI-Code-Lesbarkeit für Modelle
53Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 52.9/55 | Handhabbare Dateigrößen — 10/259 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 126.843 |
| source_files_sampled | 259 |
| oversized_source_files | 10 |
Maschinenlesbare AI-Schnittstellen
40Gefährdet
Wie die Bewertung erfolgt
| 0/40 | API-Schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP-Server |
| 40/40 | Lauffähige Beispiele — notebooks |
Verwendete Eingangsdaten
| example_dirs | notebooks |
| has_mcp_signal | nein |
| api_schema_files | — |
Eckdaten
9.679GitHub-Sterne
98Mitwirkende
263Commits, letzte 12 Monate
0Tage seit letztem Push
100Releases
6Bus-Faktor
305offene Issues
PyPIPaket-Ökosysteme
Weitere Details
Stern- und Fork-Verlauf 9.679 ★ / 2.265 ⇿
9.679Sterne
2.265Forks
93Releases
Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.
Es wird nur die jüngste Historie angezeigt — dieses Repository überschreitet das Erfassungsfenster, sodass die früheste Historie nicht erfasst wird.
Major 3Minor 33Patch 51
Jeder Punkt umfasst 5 Tage.
OpenSSF Scorecard 7.9 / 10
7.9Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).
| 10 | Binary-Artifacts | no binaries found in the repo |
| k. A. | Branch-Protection | internal 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 |
| 10 | CI-Tests | 13 out of 13 merged PRs checked by a CI test -- score normalized to 10 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 9 | Code-Review | Found 12/13 approved changesets -- score normalized to 9 |
| 10 | Contributors | project has 70 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 9 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 18 issue activity found in the last 90 days -- score normalized to 10 |
| 10 | Packaging | packaging workflow detected |
| 8 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 8 |
| 10 | SAST | SAST tool is run on all commits |
| 0 | Security-Policy | security policy file not detected |
| k. A. | Signed-Releases | no releases found |
| 10 | Token-Permissions | GitHub workflow tokens follow principle of least privilege |
| 4 | Vulnerabilities | 6 existing vulnerabilities detected |
Alle Abhängigkeiten 34
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 34 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | arviz | — | indirekt |
| PyPI | cachetools | — | indirekt |
| PyPI | cloudpickle | — | indirekt |
| PyPI | ipython | — | indirekt |
| PyPI | jupyter-sphinx | — | indirekt |
| PyPI | mcbackend | — | indirekt |
| PyPI | mypy | 1.19.1 | indirekt |
| PyPI | myst-nb | — | indirekt |
| PyPI | netcdf4 | — | indirekt |
| PyPI | numdifftools | — | indirekt |
| PyPI | numpy | — | indirekt |
| PyPI | numpydoc | — | indirekt |
| PyPI | nutpie | — | indirekt |
| PyPI | pandas | — | indirekt |
| PyPI | polyagamma | — | indirekt |
| PyPI | pre-commit | — | indirekt |
| PyPI | pymc-sphinx-theme | — | indirekt |
| PyPI | pytensor | — | indirekt |
| PyPI | pytest | — | indirekt |
| PyPI | pytest-cov | — | indirekt |
| PyPI | rich | — | indirekt |
| PyPI | scipy | — | indirekt |
| PyPI | sphinx | — | indirekt |
| PyPI | sphinx-copybutton | — | indirekt |
| PyPI | sphinx-design | — | indirekt |
| PyPI | sphinx-notfound-page | — | indirekt |
| PyPI | sphinx-remove-toctrees | — | indirekt |
| PyPI | sphinxext-rediraffe | — | indirekt |
| PyPI | threadpoolctl | — | indirekt |
| PyPI | types-cachetools | — | indirekt |
| PyPI | typing-extensions | — | indirekt |
| PyPI | versioneer | 0.29 | indirekt |
| PyPI | watermark | — | indirekt |
| PyPI | zarr | — | indirekt |
Abhängigkeits-Advisories 0
Die Installation von pypi:pymc@6.1.0 zieht 32 Pakete nach sich, direkt und transitiv: 0 tragen bekannte Advisories, davon 0 direkte Abhängigkeiten.
Keine bekannten Advisories betreffen die bewerteten Abhängigkeiten.
Ein Advisory bedeutet, dass die im Abhängigkeitsgraphen erfasste Version in den betroffenen Bereich eines Advisories fällt. Erreichbarkeit wird nicht analysiert, und der Graph enthält Entwicklungs- und Test-Pins — ein Fund kann das Werkzeug betreffen und nicht die ausgelieferte Software.
JSON-Rohbericht maschinenlesbar
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"tag": "v5.27.1",
"kind": "patch",
"published_at": "2026-01-26T11:55:46Z"
},
{
"tag": "v5.27.0",
"kind": "minor",
"published_at": "2025-12-21T12:44:44Z"
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{
"tag": "v5.26.1",
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}Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.
Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.26.0 — vollständige Methodik · Metriken-Wiki.
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