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Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-07-27 22:27 UTC

CompOmics / ms2rescore

Modular and user-friendly platform for AI-assisted rescoring of peptide identifications

PythonApache-2.0★ 66 Sterne⑂ 22 Forksseit Aug. 2017Auf GitHub ansehen ↗

CompOmics/ms2rescore erreicht einen Gesundheitsindex von 72 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Engineering Quality (90/100) ab, am schwächsten bei AI Readiness (43/100). Zuletzt vor 3 Tagen aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.

72
gesamt / 100
Gut

Software-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.

72
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)
VitalitätCommunity &VerbreitungNachhaltigkeit &GovernanceEngineering-QualitätSicherheitAI Readiness

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.

Eigentümerschaft

129 Follower114 öffentliche Reposseit März 2015

Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlichtTags
PyPIms2rescore4.0.1-45vor 3 Tagenms2rescorems2pipdeeplcproteomicsmass-spectrometrypeptide-identificationrescoringmachine-learning

Metriken nach Kategorie

Vitalität

Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?

79Gut · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 3 Tagen
13.2/36Commit-Rhythmus — 19/52 Wochen mit Commits
18/18Commit-Volumen — 210 Commits im letzten Jahr
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
Verwendete Eingangsdaten
commits_last_year210
human_commit_share1
days_since_last_push3
active_weeks_last_year19
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 43 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 3 Tagen
19.8/27Release-Rhythmus — ein Release etwa alle 78,8 Tage
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Verwendete Eingangsdaten
releases_count43
latest_release_tagv4.0.1
releases_from_tagsnein
days_since_latest_release3
mean_days_between_releases78,8

Community & Verbreitung

Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?

56Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
29.4/60Stars — 66 Stars
11/25Forks — 22 Forks
3.9/15Watcher — 6 Watcher
Verwendete Eingangsdaten
forks22
stars66
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (Apache-2.0)
18/18CONTRIBUTING-Leitfaden
0/13.5Verhaltenskodex
0/7.2Issue-Vorlage
0/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductnein
has_pull_request_templatenein

Nachhaltigkeit & Governance

Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?

73Gut · 24 % des Gesamtindex
Wie die Bewertung erfolgt
25.2/54Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab
11.5/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 49 % der Commits
9.5/13.5Breite der Beitragenden — 7 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Verwendete Eingangsdaten
bus_factor2
contributors_sampled7
top_contributor_share0,489
Wie die Bewertung erfolgt
41.4/46.8Issue-Lösungsquote — 89 % der Issues geschlossen
32.1/38.3PR-Annahme — 110/131 entschiedene PRs gemergt
1.5/15OpenSSF Scorecard: Code-Review — Found 1/7 approved changesets -- score normalized to 1
Verwendete Eingangsdaten
merged_prs110
open_issues14
closed_issues109
issue_closed_ratio0,886
closed_unmerged_prs21
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
15.2/25Reichweite des Inhabers — 129 Follower von CompOmics
25/25Kontohistorie — 114 öffentliche Repos, Kontoalter ca. 11 Jahre
Verwendete Eingangsdaten
followers129
owner_typeOrganization
is_verified
owner_loginCompOmics
public_repos114
account_age_days4.143

Paketpflege

100Exzellent
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
35/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 3 Tagen
20/20Versionshistorie — 45 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesms2rescore
ecosystemspypi
any_deprecatednein
min_days_since_publish3

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

90Exzellent · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 2 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration
9.6/9.6Pre-Commit-Hooks
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests — 4 out of 5 merged PRs checked by a CI test -- score normalized to 8
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_configja

Dokumentation

90Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://ms2rescore.readthedocs.io
10/10Repository-Beschreibung
10/10Topics — 5 Topics
0/10Wiki
Verwendete Eingangsdaten
topicsproteomics, ms2pip, percolator, deeplc, peptide-identification
has_wikinein
homepagehttps://ms2rescore.readthedocs.io
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?

58Mittel · 16 % des Gesamtindex

Sicherheitslage

47Gefährdet
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — keine Daten
2/2.5CI-Tests — 4 out of 5 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 1/7 approved changesets -- score normalized to 1
2.5/2.5Contributors — project has 6 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Lizenz — license file detected
7.5/7.5Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4,7
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
35/35Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory
25/25Indirekte Abhängigkeiten ohne bekannte Advisories — keine indirekte Abhängigkeit trägt ein bekanntes Advisory
0/40Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum
Verwendete Eingangsdaten
sourceosv
advisories0
affected_packages0
assessed_packages69
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:ms2rescore@4.0.1 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 69 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.

43Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
32/40Lesbare Commit-Historie — 60 von 100 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text)
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share0,6
agent_instruction_files
agent_instruction_max_bytes
Wie die Bewertung erfolgt
0/18Bootstrap mit einem Befehl
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration
0/11Statische Typprüfung
10/10Reproduzierbare Umgebung — Dockerfile, lockfile
4/10Belegte Agentenpraxis — 2 der letzten 100 Commits von Agenten verfasst oder ihnen zugeschrieben
0/8Automatisierte Wartung — keine automatisierten Abhängigkeits-Updates beobachtet
1/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfilesuv.lock
has_dockerfileja
typed_languagenein
bootstrap_files
has_devcontainernein
has_linter_configja
typecheck_configs
agent_commit_share0,02
toolchain_manifests
dependency_bot_commit_share0
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
53.9/55Handhabbare Dateigrößen — 1/49 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes116.571
source_files_sampled49
oversized_source_files1
Wie die Bewertung erfolgt
0/40API-Schema (OpenAPI/GraphQL/proto)
0/20MCP-Server
40/40Lauffähige Beispiele — examples, notebooks
Verwendete Eingangsdaten
example_dirsexamples, notebooks
has_mcp_signalnein
api_schema_files

Eckdaten

66GitHub-Sterne
7Mitwirkende
210Commits, letzte 12 Monate
3Tage seit letztem Push
43Releases
2Bus-Faktor
14offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

Weitere Details

Stern- und Fork-Verlauf 0 ★ / 22 ⇿
0Sterne
22Forks
41Releases

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.

048121620242212019-082023-012026-06
Major 2Minor 3Patch 13

Jeder Punkt umfasst 7 Tage.

OpenSSF Scorecard 4.7 / 10
4.7Gesamtwert

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).Scorecard v5.5.0 · 2026-07-27 22:27 UTC

10Binary-Artifactsno binaries found in the repo
k. A.Branch-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
8CI-Tests4 out of 5 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 1/7 approved changesets -- score normalized to 1
10Contributorsproject has 6 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
1Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 1
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direkte Abhängigkeiten 18
RegistryPaketVersionsvorgabeManifest
PyPIcascade-config>=0.4.0pyproject.toml
PyPIclick>=7pyproject.toml
PyPIcustomtkinter>=5,<6pyproject.toml
PyPIdeeplc>=4.0.0pyproject.toml
PyPIim2deep>=2.0.1pyproject.toml
PyPIjinja2>=3pyproject.toml
PyPIlxml>=4.5pyproject.toml
PyPIms2pip>=4.2.0pyproject.toml
PyPIms2rescore_rs>=0.5.0pyproject.toml
PyPInumpy>=1.25,<3.0pyproject.toml
PyPIpackaging>=25.0pyproject.toml
PyPIpandas>=1pyproject.toml
PyPIplotly>=5pyproject.toml
PyPIpsm_utils>=1.1pyproject.toml
PyPIpyarrow>=14pyproject.toml
PyPIpyteomics>=4.7.2pyproject.toml
PyPIrich>=12pyproject.toml
PyPIristretto-ms>=0.3.0pyproject.toml
Alle Abhängigkeiten 210

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 19 direkte und 191 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIcascade-config0.4.0direkt
PyPIclick8.4.2direkt
PyPIcustomtkinter5.2.2direkt
PyPIdeeplc4.0.0direkt
PyPIim2deep2.0.2direkt
PyPIjinja23.1.6direkt
PyPIlxml6.1.1direkt
PyPIms2pip4.2.0direkt
PyPIms2rescore-rs0.5.0direkt
PyPInumpy2.4.6direkt
PyPInumpy2.5.1direkt
PyPIpackaging26.2direkt
PyPIpandas2.3.3direkt
PyPIplotly6.9.0direkt
PyPIpsm-utils1.5.5direkt
PyPIpyarrow25.0.0direkt
PyPIpyteomics4.7.5direkt
PyPIrich14.3.4direkt
PyPIristretto-ms0.3.0direkt
PyPIaiohappyeyeballs2.7.1indirekt
PyPIaiohttp3.14.3indirekt
PyPIaiosignal1.4.0indirekt
PyPIalabaster1.0.0indirekt
PyPIaltgraph0.17.5indirekt
PyPIannotated-types0.8.0indirekt
PyPIanyio4.14.2indirekt
PyPIappnope0.1.4indirekt
PyPIasttokens3.0.2indirekt
PyPIattrs26.1.0indirekt
PyPIbabel2.18.0indirekt
PyPIbeautifulsoup44.15.0indirekt
PyPIblack26.5.1indirekt
PyPIbleach6.4.0indirekt
PyPIcbor26.1.3indirekt
PyPIcertifi2026.7.22indirekt
PyPIcffi2.1.0indirekt
PyPIcfgv3.5.0indirekt
PyPIcharset-normalizer3.4.9indirekt
PyPIcolorama0.4.6indirekt
PyPIcomm0.2.3indirekt
PyPIcontourpy1.3.3indirekt
PyPIcoverage7.15.2indirekt
PyPIcuda-bindings13.3.1indirekt
PyPIcuda-pathfinder1.6.0indirekt
PyPIcuda-toolkit13.0.3.0indirekt
PyPIcycler0.12.1indirekt
PyPIdarkdetect0.8.0indirekt
PyPIdebugpy1.8.21indirekt
PyPIdecorator5.3.1indirekt
PyPIdefusedxml0.7.1indirekt
PyPIdistlib0.4.3indirekt
PyPIdocutils0.22.4indirekt
PyPIexecuting2.2.1indirekt
PyPIfastjsonschema2.21.2indirekt
PyPIfilelock3.32.0indirekt
PyPIfonttools4.63.0indirekt
PyPIfrozenlist1.8.0indirekt
PyPIfsspec2026.6.0indirekt
PyPIgreenlet3.5.4indirekt
PyPIh110.16.0indirekt
PyPIidentify2.6.19indirekt
PyPIidna3.18indirekt
PyPIimagesize2.0.0indirekt
PyPIiniconfig2.3.0indirekt
PyPIipykernel7.3.0indirekt
PyPIipython9.15.0indirekt
PyPIipython-pygments-lexers1.1.1indirekt
PyPIipywidgets8.1.8indirekt
PyPIjedi0.20.0indirekt
PyPIjoblib1.5.3indirekt
PyPIjsonschema4.26.0indirekt
PyPIjsonschema-specifications2025.9.1indirekt
PyPIjupyter-client8.9.1indirekt
PyPIjupyter-core5.9.1indirekt
PyPIjupyterlab-pygments0.3.0indirekt
PyPIjupyterlab-widgets3.0.16indirekt
PyPIkiwisolver1.5.0indirekt
PyPIlightning2.6.5indirekt
PyPIlightning-utilities0.15.3indirekt
PyPImacholib1.16.4indirekt
PyPImarkdown-it-py4.2.0indirekt
PyPImarkupsafe3.0.3indirekt
PyPImatplotlib3.11.1indirekt
PyPImatplotlib-inline0.2.2indirekt
PyPImdit-py-plugins0.6.1indirekt
PyPImdurl0.1.2indirekt
PyPImistune3.3.4indirekt
PyPIml-dtypes0.5.4indirekt
PyPImpmath1.3.0indirekt
PyPIms2rescore4.0.1indirekt
PyPImultidict6.7.1indirekt
PyPImumble0.3.0indirekt
PyPImypy-extensions1.1.0indirekt
PyPImyst-parser5.1.0indirekt
PyPInarwhals2.24.0indirekt
PyPInbclient0.11.0indirekt
PyPInbconvert7.17.1indirekt
PyPInbformat5.10.4indirekt
PyPInbsphinx0.9.8indirekt
PyPInest-asyncio21.7.2indirekt
PyPInetworkx3.6.1indirekt
PyPInodeenv1.10.0indirekt
PyPInumpydoc1.10.0indirekt
PyPInvidia-cublas13.1.1.3indirekt
PyPInvidia-cuda-cupti13.0.85indirekt
PyPInvidia-cuda-nvrtc13.0.88indirekt
PyPInvidia-cuda-runtime13.0.96indirekt
PyPInvidia-cudnn-cu139.20.0.48indirekt
PyPInvidia-cufft12.0.0.61indirekt
PyPInvidia-cufile1.15.1.6indirekt
PyPInvidia-curand10.4.0.35indirekt
PyPInvidia-cusolver12.0.4.66indirekt
PyPInvidia-cusparse12.6.3.3indirekt
PyPInvidia-cusparselt-cu130.8.1indirekt
PyPInvidia-nccl-cu122.30.7indirekt
PyPInvidia-nccl-cu132.29.7indirekt
PyPInvidia-nvjitlink13.3.33indirekt
PyPInvidia-nvshmem-cu133.4.5indirekt
PyPInvidia-nvtx13.0.85indirekt
PyPIonnx1.22.0indirekt
PyPIonnx2torch1.5.15indirekt
PyPIpandocfilters1.5.1indirekt
PyPIparso0.8.7indirekt
PyPIpathspec1.1.1indirekt
PyPIpefile2024.8.26indirekt
PyPIpexpect4.9.0indirekt
PyPIpillow12.3.0indirekt
PyPIplatformdirs4.11.0indirekt
PyPIpluggy1.6.0indirekt
PyPIpre-commit4.6.1indirekt
PyPIprompt-toolkit3.0.52indirekt
PyPIpropcache0.5.2indirekt
PyPIprotobuf7.35.1indirekt
PyPIpsims1.3.6indirekt
PyPIpsutil7.2.2indirekt
PyPIptyprocess0.7.0indirekt
PyPIpure-eval0.2.3indirekt
PyPIpycparser3.0indirekt
PyPIpydantic2.13.4indirekt
PyPIpydantic-core2.46.4indirekt
PyPIpygments2.20.0indirekt
PyPIpyinstaller6.21.0indirekt
PyPIpyinstaller-hooks-contrib2026.6indirekt
PyPIpyopenms3.5.0indirekt
PyPIpyparsing3.3.2indirekt
PyPIpytest9.1.1indirekt
PyPIpytest-cov7.1.0indirekt
PyPIpython-dateutil2.9.0.post0indirekt
PyPIpython-discovery1.5.0indirekt
PyPIpytokens0.4.1indirekt
PyPIpytorch-lightning2.6.5indirekt
PyPIpytz2026.2indirekt
PyPIpywin32-ctypes0.2.3indirekt
PyPIpyyaml6.0.3indirekt
PyPIpyzmq27.1.0indirekt
PyPIreferencing0.37.0indirekt
PyPIrequests2.34.2indirekt
PyPIroman-numerals4.1.0indirekt
PyPIrpds-py2026.6.3indirekt
PyPIruff0.16.0indirekt
PyPIrustyms0.10.0indirekt
PyPIscikit-learn1.9.0indirekt
PyPIscipy1.17.1indirekt
PyPIscipy1.18.0indirekt
PyPIsemver3.0.4indirekt
PyPIsetuptools83.0.0indirekt
PyPIsix1.17.0indirekt
PyPIsnowballstemmer3.1.1indirekt
PyPIsoupsieve2.9.1indirekt
PyPIsphinx9.0.4indirekt
PyPIsphinx9.1.0indirekt
PyPIsphinx-argparse0.6.0indirekt
PyPIsphinx-autobuild2025.8.25indirekt
PyPIsphinx-inline-tabs2025.12.21.14indirekt
PyPIsphinx-rtd-theme3.1.0indirekt
PyPIsphinxcontrib-applehelp2.0.0indirekt
PyPIsphinxcontrib-devhelp2.0.0indirekt
PyPIsphinxcontrib-htmlhelp2.1.0indirekt
PyPIsphinxcontrib-jquery4.1indirekt
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Abhängigkeits-Advisories 0

Die Installation von pypi:ms2rescore@4.0.1 zieht 69 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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          "headline": "Merge remote-tracking branch 'origin/worktree-changelog-history' into…",
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          "body": "README.md had `[ProteomeDiscoverer](#)`, a bare `#` link with no real\ntarget. MyST resolves it to an empty cross-reference string, which is\nharmless locally (just a warning) but crashes sphinx-argparse's xref\nresolution on ReadTheDocs, failing the whole docs build. De-linked to\nplain text instead of guessing an unverified vendor URL.",
          "is_bot": false,
          "headline": "fix: remove placeholder link crashing ReadTheDocs build",
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          "headline": "Update changelog",
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          "oid": "f990cde731fa82940725928fc9bb2d8af7114134",
          "body": "…ad code\n\nmodel_dir was a real MS2PIPFeatureGenerator kwarg missing from the config\nschema, and im2deep's reference_dataset schema default claimed\n\"Meier_unimod.parquet\" while the actual default falls through to IM2Deep's\nown bundled reference dataset. Also removes an unused self._verbose\nleftover in im2deep.py, mirroring the one already removed from deeplc.py.",
          "is_bot": false,
          "headline": "fix: document im2deep's model_dir/reference_dataset defaults, drop de…",
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          "oid": "a0c59fcbd666b5665a8e19ba829d224bd3832118",
          "body": "…e kwarg distribution\n\ndeeplc_retrain was redundant since it's already scoped under the deeplc\nfeature generator's own config block. Renamed to finetune; the old name is\nstill accepted for backward compatibility but now emits a DeprecationWarning\nand is mapped onto the new option, with an explicit finetune taking\nprecedence.\n\nAlso consolidated the near-duplicate predict/finetune kwarg filtering into a\nsingle helper, and dropped the unused self._verbose attribute.",
          "is_bot": false,
          "headline": "refactor: rename deeplc_retrain to finetune, clean up predict/finetun…",
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          "body": "ms2_tolerance was removed as an MS2PIPFeatureGenerator kwarg in favor of the\ntop-level tolerance_value/tolerance_mode, but the key was left behind in\nconfig defaults, schema, docs, and the sage example.",
          "is_bot": false,
          "headline": "fix: remove stale ms2_tolerance from ms2pip config",
          "author_name": "RalfG",
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          "headline": "Update publish.yml",
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          "oid": "1fa7aedd6b7eae834a41789fbd225a686513d182",
          "body": "Release/4.0",
          "is_bot": false,
          "headline": "Merge pull request #265 from CompOmics/release/4.0",
          "author_name": "ArthurDeclercq",
          "author_login": "ArthurDeclercq",
          "committed_at": "2026-07-17T17:12:29Z",
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        },
        {
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          "body": null,
          "is_bot": false,
          "headline": "fix in reference dataset function im2deep",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-17T16:45:09Z",
          "body_truncated": false,
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        },
        {
          "oid": "1e3f5ab2f713a97d272a7f02a16749b7167f344e",
          "body": null,
          "is_bot": false,
          "headline": "code review final bugfixes and discrepancies",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-17T16:26:02Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "96eeeed076878e72273326cbdb1a240217db93f6",
          "body": "Fix /docs after ristretto integration",
          "is_bot": false,
          "headline": "Merge pull request #270 from CompOmics/fix/docs-after-ristretto",
          "author_name": "Ralf Gabriels",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T15:15:12Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ce23c8583bb8d235329e1a63f5ae4b8a3531ab18",
          "body": "… into fix/docs-after-ristretto",
          "is_bot": false,
          "headline": "Merge branch 'release/4.0' of https://github.com/CompOmics/ms2rescore…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T15:08:00Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ee8a1c83f5db86e7f7e977546b6569c2c3175707",
          "body": "Move package-level private rescoring functions to new module _ristretto_utils.py",
          "is_bot": false,
          "headline": "Merge pull request #269 from CompOmics/fix/rescoring-module",
          "author_name": "ArthurDeclercq",
          "author_login": "ArthurDeclercq",
          "committed_at": "2026-07-17T14:51:02Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "58f3c47bab9207a942233b5eadf5d550d75ecbe4",
          "body": null,
          "is_bot": false,
          "headline": "fix: correct test import",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T14:37:20Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ac1e427e50717701b80bd3a662a3acf789a487f4",
          "body": null,
          "is_bot": false,
          "headline": "docs: add rescoring module",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:57:10Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "0f1f5231272cc9de4003dca08104a053af2814c9",
          "body": "…s2rescore into fix/docs-after-ristretto",
          "is_bot": false,
          "headline": "Merge branch 'fix/rescoring-module' of https://github.com/CompOmics/m…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:54:18Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "e09fb2137af2ec341d131b98b317e2f5204cbc26",
          "body": null,
          "is_bot": false,
          "headline": "fix: update tests",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:52:38Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "adeb0ec94e5761aa92f9fb73f054eab2f4f61bc1",
          "body": null,
          "is_bot": false,
          "headline": "Move back some functions only used in rescoring.py",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:47:50Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "bcd475395ae8cc3e41c00a3336bb450cd5adbddd",
          "body": "… into fix/rescoring-module",
          "is_bot": false,
          "headline": "Merge branch 'release/4.0' of https://github.com/CompOmics/ms2rescore…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:39:43Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "7a52505fbf5d000501632f8e54153751a30aaa83",
          "body": "…to_utils.py",
          "is_bot": false,
          "headline": "Move package-level private rescoring functions to new module _ristret…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:39:33Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "45c45ae90c959631c34a17b0a5a73c997f10d302",
          "body": null,
          "is_bot": false,
          "headline": "end to end test",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-17T10:57:21Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "24fadb75b22e88d41258b33d973fe6524f07bc61",
          "body": null,
          "is_bot": false,
          "headline": "docs: update in-depth-python-api and userguide",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T10:23:12Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "0ab30b69551e98415c44dc7ed42af0991ad3719e",
          "body": "Replace mokapot rescoring engine with ristretto",
          "is_bot": false,
          "headline": "Merge pull request #268 from CompOmics/feat/add-ristretto",
          "author_name": "ArthurDeclercq",
          "author_login": "ArthurDeclercq",
          "committed_at": "2026-07-17T09:03:16Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "c255a3fcfd7fa6f8f0d4e05509e7dceba0ac7fdc",
          "body": null,
          "is_bot": false,
          "headline": "fix import error for python 3.12",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-17T08:40:04Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "59de3f31e2cda5fc086e12d11048967cb955805c",
          "body": "Review findings from the cumulative-diff review, addressed:\n\n- rescoring: null never actually disabled rescoring (CascadeConfig's\n  none_overrides_value=False silently ignores it), and the \"skip\n  rescoring\" path that was reachable only in principle would have\n  written no output at all. Rather than\n[…]\nreference).\n\nAlso reformats CHANGELOG.md's \"Breaking changes\" section (non-standard\nKeep a Changelog category) into the canonical Added/Changed/Removed\nsections, with breaking items tagged [BREAKING].",
          "is_bot": false,
          "headline": "Remove ability to skip rescoring; run infer_score_direction per-run",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T16:18:24Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "0f24e598a48cca9c51664b8af7140fcdd1e5711f",
          "body": "Cumulative summary of the mokapot -> ristretto rescoring engine\nmigration (report_fdr, model choice, auto score-direction inference,\noutput-file renames, GUI log.html fix, and the resulting config/API\nremovals), merged into the existing Unreleased section.",
          "is_bot": false,
          "headline": "Add ristretto migration entries to CHANGELOG",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T15:50:30Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "fba6411e330732d1456e1d74b3f18ec60e7bd5a1",
          "body": "…of-date",
          "is_bot": false,
          "headline": "docs: minor cleanup; temporarily remove overview figure as it is out-…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T15:34:56Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "95a9ee6928ed58e02447e5976d956eee0a0ea214",
          "body": "The GUI's logging setup only ever attached a plain FileHandler for\n<prefix>.log.txt -- unlike the CLI, it never used a recording Rich console\nor called save_html(), so <prefix>.log.html (documented as always written)\nwas silently never created for GUI-launched runs. Added the same\nConsole(record=Tru\n[…]\naises.\n\nAlso add an INFO-level log line when rescoring actually starts, since the\nper-fold/rollup/competition detail ristretto itself logs is moving to\nDEBUG level (fixed at the source, in ristretto).",
          "is_bot": false,
          "headline": "Fix missing HTML log file in GUI runs; log when rescoring starts",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T15:07:55Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "617cec085aabe9688be7903546a398aa21330356",
          "body": "…nal API surface\n\n- Fix rescore() always returning empty feature_weights/n_iterations: it\n  rebuilds the final result via ristretto.evaluate() (no training, so those\n  fields come back empty), discarding the real values from the earlier\n  ristretto.rescore() call. Now carried over explicitly, includ\n[…]\ne that explicit rather than presenting it as public API. Also\n  privatized build_features_dataframe and fix_constant_pep, which were\n  only ever called internally within the rescoring workflow itself.",
          "is_bot": false,
          "headline": "Fix empty feature weights, simplify output naming, and minimize inter…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T14:55:43Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "dc62a277fad5dce4ed1b70d317dd237a5f3bf71e",
          "body": "A user-provided rescoring dict (e.g. \"rescoring\": {} or {\"model\": \"lda\"})\nis passed through as-is by the config cascade -- same as feature_generators\nand psm_generator, where an override is taken at face value rather than\nmerged with defaults (CascadeConfig's max_recursion_depth stops it from\nreachi\n[…]\nig[\"rescoring\"] to ristretto.rescore()\nvia **kwargs instead of naming train_fdr/model explicitly, so ristretto's\nown defaults (train_fdr=0.01, model=\"svm\") apply to whatever the user's\nconfig omitted.",
          "is_bot": false,
          "headline": "Fix KeyError on partial/empty rescoring config",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T13:10:51Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "49104a38ba6de291222a2d0afb7b84d97f3c69a5",
          "body": "Clarifies alongside the existing null (skip rescoring) note that\nrescoring: {} is equivalent to omitting the key, since cascade_config\ndeep-merges schema defaults into a partial dict. Also fixes a stale\nmention that rescoring only exposed train_fdr (model was added earlier).",
          "is_bot": false,
          "headline": "Document that an empty rescoring object uses the schema defaults",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:51:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "aa7a2c013d105819b3e32cb160a4ee28f97ea26e",
          "body": "…e-ristretto-fixes",
          "is_bot": false,
          "headline": "Merge remote-tracking branch 'origin/feat/add-ristretto' into worktre…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:50:04Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "2610434c03c257e1a540b44e89ed151f902f327d",
          "body": "Adds a top-level report_fdr setting (default 0.01) used for console-logged\nidentification counts, HTML report stats/charts, and FlashLFQ output\nfiltering. This is independent of rescoring.train_fdr, which only controls\nristretto's semi-supervised training. Standalone report regeneration\n(ms2rescore-report) gains a --fdr option to override the report_fdr stored\nin the original run's full-config.json, without rerunning rescoring.",
          "is_bot": false,
          "headline": "Add report_fdr config option to replace hardcoded 1% FDR threshold",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:49:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "8d8aec14de9bd1e2498584cf2f6425f3adc136ac",
          "body": "… into feat/add-ristretto",
          "is_bot": false,
          "headline": "Merge branch 'release/4.0' of https://github.com/CompOmics/ms2rescore…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:35:36Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "7d8ed98782fb24870f9dd2259246c95dc9c28a3b",
          "body": "Add rescoring.model config option (svm, the default iterative\nPercolator-style SVM; or lda, a single-pass Fisher LDA that's faster\nbut less powerful on hard-to-separate datasets), threaded through to\nristretto.rescore()'s model kwarg. Adds a GUI dropdown alongside the\nexisting training-FDR field.\n\nR\n[…]\nthe jsonschema2md CLI's\npinned Python interpreter is missing on this machine (pre-existing\nenvironment issue, unrelated to this change) -- the generated\nsections were mirrored exactly by hand instead.",
          "is_bot": false,
          "headline": "Expose ristretto's model choice; always write rescoring tables",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:28:34Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "519a3bce4dadd38f3bbae60bbce0325c38d94e76",
          "body": "ristretto-ms>=0.3.0 is now on PyPI, so the local ../ironbrew editable\npath override is no longer needed. Removes the now-empty\n[tool.uv.sources] override and its TODO, and regenerates uv.lock to\nresolve ristretto-ms from the registry.",
          "is_bot": false,
          "headline": "Depend on published ristretto-ms instead of the local editable path",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:13:13Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "cfe700f86ce527f199509d02908d2949192a6e55",
          "body": "Rescoring result tables are now plain TSV (not Parquet) at every\nidentification level, and only the post-rescoring (\"after\") result is\nwritten -- \"before\" is reconstructed on demand from the main PSM\nlist's provenance data, so it's never persisted. Also documents the\nmax_psm_rank_output > 1 caveat (multi-rank rows, not statistically\nrigorous FDR) and drops the now-obsolete after_report paragraph.",
          "is_bot": false,
          "headline": "Update output-files.rst for the TSV-only, after-only rescoring tables",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T11:28:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "b153875ecc24fcf27c8c128467c138ad4cf65e6c",
          "body": "max_psm_rank_output is a special feature for surfacing ambiguous\nresults (e.g. mumble's multiple candidate peptidoforms per spectrum),\nnot for statistically rigorous FDR control when > 1 -- that trade-off\nis now explicit and applied consistently everywhere, instead of\ncomputing a separate always-ran\n[…]\nindexing returns the same underlying PSM\nobject references, not copies, so evaluate_before_from_provenance now\ntakes a real deep copy before mutating scores, to avoid corrupting\nthe caller's psm_list.",
          "is_bot": false,
          "headline": "Rework max_psm_rank_output and report reconstruction",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T11:27:32Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "6e425d19010c4fe6c40c969728a239c44523df98",
          "body": "build_features_dataframe now includes a \"run\" column, and every\nristretto.rescore()/evaluate() call passes run_col=\"run\" (requires\nristretto-ms>=0.3.0). Competition and CV splitting now group by\n(run, spectrum_id) at the source, instead of spectrum_id alone --\npreviously, two different physical spec\n[…]\nntirely: \"run\" now\ncomes back natively on every RescoreResult.psms from ristretto itself,\nso there's no more index-alignment hack needed on the ms2rescore side.\n\nBumps the ristretto-ms pin to >=0.3.0.",
          "is_bot": false,
          "headline": "Fix multi-run spectrum_id collisions via ristretto's new run_col",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:49:38Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "5a7706a3a105beba66ece6bb14458bddb7d61145",
          "body": "write_rescoring_tables() writes an additional set of *_after_report\nParquet tables when max_psm_rank_output > 1 (the rank-1 report view,\ndistinct from the multi-rank *_after files), which wasn't mentioned\nanywhere in the output-files reference.",
          "is_bot": false,
          "headline": "Document after_report Parquet output files",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:11:06Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "723a9a09d380ce588140dc97a62a400bd190202a",
          "body": "…n report\n\ncompute_id_stats/identification_overlap consumed before_result unmasked,\nunlike core.py's console-log count, which excludes mumble-generated\nalternate candidates via psm.metadata['original_psm']. Attach that\nmask onto before_result.psms itself (evaluate_before), the same way\nrun already i\n[…]\nfor that level.\n\nAdds direct unit coverage for both: _n_identified's masking behavior,\nand identification_overlap's run-based disambiguation (which the old\nbare-spectrum_id grouping would undercount).",
          "is_bot": false,
          "headline": "Apply mumble original_psm mask and run+spectrum_id key consistently i…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:10:35Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "96b8dc0ac4362a22dbcbb746b022ac17ce9245ee",
          "body": "fix_constant_pep previously only filtered result.psms, leaving\npeptidoform/peptide/protein rollups computed on the decoy-contaminated\npopulation -- _write_group_metadata would then look up stale\nscores/qvalues/PEPs for surviving PSMs. Now recomputes q-values, PEP,\nand rollups from scratch via ristre\n[…]\nt while the\nreal output looked fine. Split the fix into a result-only core\n(_fix_constant_pep_result) plus the existing psm_list-aware wrapper,\nand apply the core check to report_result independently.",
          "is_bot": false,
          "headline": "Refresh rollups in fix_constant_pep and apply it to report_result too",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:05:14Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "98c5d51e0dc150b5eddb4afd283f1d3f94a189d7",
          "body": null,
          "is_bot": false,
          "headline": "commit new lock file",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-16T08:13:47Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "4726d4d10685d9d9181b5b9849d85e48ae357a0b",
          "body": "fix_constant_pep now takes/returns a (PSMList, RescoreResult) pair;\nrescore() returns (psm_list, after_result, after_report_result).\nRescoreResult.psms now always carries a run column. Adds coverage for\nthe new report_result (rank-1 view, distinct from a multi-rank final\nresult) and the after_report Parquet table written when they differ.\nAlso asserts rescoring_tables_unavailable is set when the ristretto\nParquet tables are missing.",
          "is_bot": false,
          "headline": "Update tests for rescore()'s 3-tuple return and RescoreResult schema",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:14:47Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "9abf1c917111df815ddc0c3c881fb1acf1663019",
          "body": "The \"Rescoring result tables\" table had inconsistent column-divider\npositions across its border/header/content rows (four different\nwidths), which docutils/Sphinx rejects as a malformed table. Rebuilt\nit with consistent column widths (verified programmatically: every\nrow shares identical +/| column positions).",
          "is_bot": false,
          "headline": "Fix malformed RST grid table in output-files.rst",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:11:37Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "f6af833dac16dc1bec5b28bc6c25b4b5aec45672",
          "body": "…p levels\n\ncreate_psm_dataframe: filter psm_list to rank==1 (the report is always\na single-best-PSM-per-spectrum view, regardless of max_psm_rank_output)\nand merge before/after by (run, spectrum_id) instead of (spectrum_id,\npeptidoform). The old peptidoform-keyed merge produced NaN\nscore_before/qval\n[…]\n after_report table (rank-1 view) over after (which may be\nmulti-rank) when both exist. The overview tab shows an explicit notice\nwhen tables are unavailable instead of silently rendering empty stats.",
          "is_bot": false,
          "headline": "Rework report before/after comparison to (run, spectrum_id) + 4 rollu…",
          "author_name": "RalfG",
          "author_login": "RalfG",
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                "name": "Dependency-Update-Tool",
                "detail": "no update tool detected",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "fuzzing",
                "name": "Fuzzing",
                "detail": "project is not fuzzed",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "license",
                "name": "License",
                "detail": "license file detected",
                "points": 2.5,
                "status": "met",
                "details": [],
                "max_points": 2.5
              },
              {
                "key": "maintained",
                "name": "Maintained",
                "detail": "30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "packaging",
                "name": "Packaging",
                "detail": "packaging workflow detected",
                "points": 5,
                "status": "met",
                "details": [],
                "max_points": 5
              },
              {
                "key": "pinned_dependencies",
                "name": "Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 1",
                "points": 0.5,
                "status": "partial",
                "details": [],
                "max_points": 5
              },
              {
                "key": "sast",
                "name": "SAST",
                "detail": "SAST tool is not run on all commits -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "security_policy",
                "name": "Security-Policy",
                "detail": "security policy file not detected",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "signed_releases",
                "name": "Signed-Releases",
                "detail": "Project has not signed or included provenance with any releases.",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "token_permissions",
                "name": "Token-Permissions",
                "detail": "detected GitHub workflow tokens with excessive permissions",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "vulnerabilities",
                "name": "Vulnerabilities",
                "detail": "0 existing vulnerabilities detected",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              }
            ]
          },
          {
            "key": "dependency_advisories",
            "band": "excellent",
            "name": "Dependency advisories",
            "note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:ms2rescore@4.0.1 runtime dependency closure — what installing the published package pulls in — 69 packages. Reachability is not analyzed.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "no_advisories_left_outstanding"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              },
              {
                "code": "advisories_scope_published",
                "params": {
                  "package": "pypi:ms2rescore@4.0.1",
                  "assessed": 69
                }
              },
              {
                "code": "advisories_reachability",
                "params": {}
              }
            ],
            "value": 100,
            "inputs": {
              "source": "osv",
              "advisories": 0,
              "affected_packages": 0,
              "assessed_packages": 69,
              "unassessed_packages": 0,
              "affected_by_severity": "none",
              "direct_affected_packages": 0
            },
            "components": [
              {
                "key": "direct_dependencies_free_of_known_advisories",
                "name": "Direct dependencies free of known advisories",
                "detail": "no direct dependency carries a known advisory",
                "points": 35,
                "status": "met",
                "details": [
                  {
                    "code": "no_direct_advisories",
                    "params": {}
                  }
                ],
                "max_points": 35
              },
              {
                "key": "indirect_dependencies_free_of_known_advisories",
                "name": "Indirect dependencies free of known advisories",
                "detail": "no indirect dependency carries a known advisory",
                "points": 25,
                "status": "met",
                "details": [
                  {
                    "code": "no_indirect_advisories",
                    "params": {}
                  }
                ],
                "max_points": 25
              },
              {
                "key": "no_advisories_left_outstanding",
                "name": "No advisories left outstanding",
                "detail": "no advisory carries a publication date",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "advisories_no_publication_date",
                    "params": {}
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "malicious_dependencies",
            "band": "excellent",
            "name": "Malicious dependencies",
            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "source": "osv",
              "meaning": "reported as a malicious package by the OpenSSF corpus; the remedy is removal or moving off the compromised name, never an upgrade of the same artifact. Versions the registry has since pulled are listed but not scored",
              "packages": [],
              "red_flag": false,
              "assessed_packages": 69,
              "malicious_packages": 0,
              "direct_malicious_packages": 0,
              "withdrawn_malicious_packages": 0,
              "installable_malicious_packages": 0
            },
            "components": [
              {
                "key": "no_dependency_reported_as_a_malicious_package",
                "name": "No dependency reported as a malicious package",
                "detail": "no dependency is reported as a malicious package",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "no_malicious_dependencies",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
            "band": "excellent",
            "name": "High-Risk Jurisdiction Exposure",
            "note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
                "params": {}
              }
            ],
            "value": 100,
            "inputs": {
              "meaning": "self-published location evidence; not nationality or citizenship",
              "red_flag": false,
              "exposures": [],
              "policy_countries": [
                "Russia",
                "Iran",
                "North Korea"
              ],
              "review_only_matches": 0,
              "assessed_self_published_locations": 6
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
                "detail": "no confirmed policy-scope location match",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "jurisdiction_no_match",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          }
        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
        "band": "at_risk",
        "name": "AI Readiness",
        "value": 43,
        "weight": 0,
        "metrics": [
          {
            "key": "ai_agent_context",
            "band": "at_risk",
            "name": "Agent context & guidance",
            "note": null,
            "notes": [],
            "value": 32,
            "inputs": {
              "has_llms_txt": false,
              "legible_history_share": 0.6,
              "agent_instruction_files": [],
              "agent_instruction_max_bytes": null
            },
            "components": [
              {
                "key": "agent_instructions",
                "name": "Agent instructions",
                "detail": "no CLAUDE.md / AGENTS.md / editor rules",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_instructions",
                    "params": {}
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              },
              {
                "key": "legible_commit_history",
                "name": "Legible commit history",
                "detail": "60 of 100 human commits state their intent (structured subject or explanatory body)",
                "points": 32,
                "status": "partial",
                "details": [
                  {
                    "code": "legible_history",
                    "params": {
                      "legible": 60,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "at_risk",
            "name": "Verify loop (build / test / typecheck)",
            "note": null,
            "notes": [],
            "value": 48,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [
                "uv.lock"
              ],
              "has_dockerfile": true,
              "typed_language": false,
              "bootstrap_files": [],
              "has_devcontainer": false,
              "has_linter_config": true,
              "typecheck_configs": [],
              "agent_commit_share": 0.02,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
            },
            "components": [
              {
                "key": "one_command_bootstrap",
                "name": "One-command bootstrap",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 18
              },
              {
                "key": "automated_tests",
                "name": "Automated tests",
                "detail": null,
                "points": 22,
                "status": "met",
                "details": [],
                "max_points": 22
              },
              {
                "key": "lint_format_config",
                "name": "Lint / format config",
                "detail": null,
                "points": 11,
                "status": "met",
                "details": [],
                "max_points": 11
              },
              {
                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
                "detail": "Dockerfile, lockfile",
                "points": 10,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "Dockerfile, lockfile"
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "2 of the last 100 commits agent-authored or agent-credited",
                "points": 4,
                "status": "partial",
                "details": [
                  {
                    "code": "agent_authored_commits",
                    "params": {
                      "count": 2,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "no automated dependency updates observed",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_dependency_automation",
                    "params": {}
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 1",
                "points": 1,
                "status": "partial",
                "details": [],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "moderate",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 54,
            "inputs": {
              "primary_language": "Python",
              "largest_source_bytes": 116571,
              "source_files_sampled": 49,
              "oversized_source_files": 1
            },
            "components": [
              {
                "key": "type_checkable_code",
                "name": "Type-checkable code",
                "detail": "Python without a type-check config",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_typecheck_config_language",
                    "params": {
                      "language": "Python"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "1/49 source files over 60KB",
                "points": 53.9,
                "status": "partial",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 49,
                      "oversized": 1
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "example_dirs": [
                "examples",
                "notebooks"
              ],
              "has_mcp_signal": false,
              "api_schema_files": []
            },
            "components": [
              {
                "key": "api_schema_openapi_graphql_proto",
                "name": "API schema (OpenAPI/GraphQL/proto)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 40
              },
              {
                "key": "mcp_server",
                "name": "MCP server",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "examples, notebooks",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "examples, notebooks"
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          }
        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-27T22:27:21.075230Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/c/CompOmics/ms2rescore.svg",
  "full_name": "CompOmics/ms2rescore",
  "license_state": "standard",
  "license_spdx": "Apache-2.0"
}

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 v0.27.0 — vollständige Methodik · Metriken-Wiki.

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