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Software-GesundheitsberichtSchema 0.11.0 · Metriken 1.13.0 · 2026-07-17 02:02 UTC

datadog / datadog-lambda-python

The Datadog AWS Lambda Layer for Python

Python · ShellApache-2.0★ 101 Sterne⑂ 51 Forksseit Apr. 2019Auf GitHub ansehen ↗

datadog/datadog-lambda-python erreicht einen Gesundheitsindex von 77 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (93/100) ab, am schwächsten bei AI Readiness (34/100). Zuletzt vor 3 Tagen aktualisiert. 5 Mitwirkende tragen den Großteil der jüngsten Arbeit.

77
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.

77
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

Datadog, Inc.Organisation
3.222 Follower1.194 öffentliche Reposseit Aug. 2010

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlichtTags
PyPIdatadog_lambda8.126.0-119vor 3 Tagendatadogawslambdalayer

Metriken nach Kategorie

Vitalität

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

93Exzellent · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 3 Tagen
31.2/36Commit-Rhythmus — 45/52 Wochen mit Commits
18/18Commit-Volumen — 139 Commits im letzten Jahr
10/10OpenSSF Scorecard: Maintained — 25 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Verwendete Eingangsdaten
commits_last_year139
human_commit_share
days_since_last_push3
active_weeks_last_year45
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 100 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 3 Tagen
27/27Release-Rhythmus — ein Release etwa alle 27,2 Tage
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Verwendete Eingangsdaten
releases_count100
latest_release_tagv8.126.0
releases_from_tagsnein
days_since_latest_release3
mean_days_between_releases27,2

Community & Verbreitung

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

64Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
32.4/60Stars — 101 Stars
14.2/25Forks — 51 Forks
6.2/15Watcher — 14 Watcher
Verwendete Eingangsdaten
forks51
stars101
watchers14
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
6.3/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductnein
has_pull_request_templateja

Nachhaltigkeit & Governance

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

88Exzellent · 24 % des Gesamtindex
Wie die Bewertung erfolgt
45.9/54Bus-Faktor — 5 Beitragende decken die Hälfte aller Commits ab
19.7/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 13 % der Commits
13.5/13.5Breite der Beitragenden — 63 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 15 contributing companies or organizations
Verwendete Eingangsdaten
bus_factor5
contributors_sampled63
top_contributor_share0,126
Wie die Bewertung erfolgt
44.3/46.8Issue-Lösungsquote — 95 % der Issues geschlossen
30.1/38.3PR-Annahme — 540/687 entschiedene PRs gemergt
12/15OpenSSF Scorecard: Code-Review — Found 25/29 approved changesets -- score normalized to 8
Verwendete Eingangsdaten
merged_prs540
open_issues7
closed_issues128
issue_closed_ratio0,948
closed_unmerged_prs147
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
25/25Reichweite des Inhabers — 3.222 Follower von DataDog
25/25Kontohistorie — 1.194 öffentliche Repos, Kontoalter ca. 15 Jahre
Verwendete Eingangsdaten
followers3.222
owner_typeOrganization
is_verified
owner_loginDataDog
public_repos1.194
account_age_days5.814

Paketpflege

100Exzellent
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
35/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 3 Tagen
20/20Versionshistorie — 119 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesdatadog_lambda
ecosystemspypi
any_deprecatednein
min_days_since_publish3

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

77Gut · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 5 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration — .flake8
9.6/9.6Pre-Commit-Hooks
0/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests — 26 out of 27 merged PRs checked by a CI test -- score normalized to 9
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_configja
Wie die Bewertung erfolgt
30/30README
0/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://docs.datadoghq.com/integrations/amazon_lambda/#installing-and-using-the-datadog-layer
10/10Repository-Beschreibung
0/10Topics
0/10Wiki
Verwendete Eingangsdaten
topics
has_wikinein
homepagehttps://docs.datadoghq.com/integrations/amazon_lambda/#installing-and-using-the-datadog-layer
has_readmeja
has_docs_dirnein
has_descriptionja

Sicherheit

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

52Mittel · 16 % des Gesamtindex
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
3/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.2/2.5CI-Tests — 26 out of 27 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6/7.5Code-Review — Found 25/29 approved changesets -- score normalized to 8
2.5/2.5Contributors — project has 15 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Lizenz — license file detected
7.5/7.5Maintained — 25 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — keine Daten
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
3.5/5SAST — SAST tool detected but not run on all commits
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
0/7.5Vulnerabilities — 15 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5,2
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): packaging. Die verbleibenden Gewichte wurden renormalisiert.

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.

34Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
0/40Lesbare Commit-Historie — keine Daten
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lesbare Commit-Historie. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
0/18Bootstrap mit einem Befehl
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration — .flake8
0/11Statische Typprüfung
10/10Reproduzierbare Umgebung — Dockerfile, lockfile
0/10Belegte Agentenpraxis — keine Daten
0/8Automatisierte Wartung — keine Daten
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfilespackage-lock.json, poetry.lock, yarn.lock
has_dockerfileja
typed_languagenein
bootstrap_files
has_devcontainernein
has_linter_configja
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis, Automatisierte Wartung. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
52.9/55Handhabbare Dateigrößen — 2/52 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes158.439
source_files_sampled52
oversized_source_files2

Eckdaten

101GitHub-Sterne
63Mitwirkende
139Commits, letzte 12 Monate
3Tage seit letztem Push
100Releases
5Bus-Faktor
7offene Issues
PyPIPaket-Ökosysteme

Weitere Details

OpenSSF Scorecard 5.2 / 10
5.2Gesamtwert

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 · 2026-07-17 02:02 UTC

10Binary-Artifactsno binaries found in the repo
4Branch-Protectionbranch protection is not maximal on development and all release branches
9CI-Tests26 out of 27 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
8Code-ReviewFound 25/29 approved changesets -- score normalized to 8
10Contributorsproject has 15 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained25 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
k. A.Packagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
7SASTSAST tool detected but not run on all commits
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
0Vulnerabilities15 existing vulnerabilities detected
Direkte Abhängigkeiten 10
RegistryPaketVersionsvorgabeManifest
PyPIdatadog>=0.51.0,<1.0.0pyproject.toml
PyPIwrapt^1.11.2pyproject.toml
PyPIddtracepyproject.toml
PyPIujsonpyproject.toml
PyPIurllib3pyproject.toml
PyPIbotocore^1.34.0pyproject.toml
PyPIrequestspyproject.toml
PyPIpytestpyproject.toml
PyPIpytest-benchmark^4.0pyproject.toml
PyPIflake8^5.0.4pyproject.toml
Alle Abhängigkeiten 141

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 11 direkte und 130 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
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PyPIddtrace3.19.7direkt
PyPIflake85.0.4direkt
PyPIpytest8.3.4direkt
PyPIpytest-benchmark4.0.0direkt
PyPIrequests2.32.4direkt
PyPIrequests2.33.1direkt
PyPIujson5.10.0direkt
PyPIurllib31.26.20direkt
PyPIwrapt1.17.2direkt
npm2-thenable1.0.0indirekt
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JSON-Rohbericht maschinenlesbar
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          "avatar_url": "https://avatars.githubusercontent.com/u/13263282?u=17bdcf5778f5d935c2ef76165ccfe823d9c6534b&v=4"
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        {
          "type": "User",
          "login": "rithikanarayan",
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          "avatar_url": "https://avatars.githubusercontent.com/u/93233069?v=4"
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        {
          "type": "User",
          "login": "apiarian-datadog",
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          "avatar_url": "https://avatars.githubusercontent.com/u/175138641?u=991f82cf67b142d730a4860dd6cd0563c6c91ac0&v=4"
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        {
          "type": "User",
          "login": "duncanista",
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          "avatar_url": "https://avatars.githubusercontent.com/u/30836115?u=c25d2f28fe89cfdc30f93280c90f41207b707c69&v=4"
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        {
          "type": "User",
          "login": "DylanLovesCoffee",
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          "avatar_url": "https://avatars.githubusercontent.com/u/25290232?u=f60ab92ffdf0c33a5557ebff77e4627277d3ea71&v=4"
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            "name": "Fuzzing",
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          {
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