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Software-GesundheitsberichtSchema 0.14.0 · Metriken 1.13.0 · 2026-07-18 23:16 UTC

HiDiHo01 / python-frank-energie

Asynchronous Python package for Frank Energie. Retrieve energy prices for Frank energie.

PythonApache-2.0★ 10 Sterne⑂ 3 Forksseit Apr. 2025Auf GitHub ansehen ↗

HiDiHo01/python-frank-energie erreicht einen Gesundheitsindex von 71 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (92/100) ab, am schwächsten bei AI Readiness (32/100). Zuletzt heute aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.

71
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.

71
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

RoyPersönliches Konto
12 Follower39 öffentliche Reposseit Dez. 2021

Dieses Repository gehört einem persönlichen Konto. Ein Projekt mit nur einem Eigentümer trägt ein höheres Kontinuitätsrisiko als ein organisationsgetragenes.

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlichtTags
PyPIpython-frank-energie2026.7.18-57vor 0 Tagenhome-assistantenergyasyncasyncioapifrank-energie

Metriken nach Kategorie

Vitalität

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

92Exzellent · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 0 Tagen
22.8/36Commit-Rhythmus — 33/52 Wochen mit Commits
18/18Commit-Volumen — 327 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_year327
human_commit_share
days_since_last_push0
active_weeks_last_year33

Release-Disziplin

100Exzellent
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 28 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 0 Tagen
27/27Release-Rhythmus — ein Release etwa alle 8,8 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count28
latest_release_tagv2026.7.18-beta.7
releases_from_tagsnein
days_since_latest_release0
mean_days_between_releases8,8
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?

33Gefährdet · 18 % des Gesamtindex
Wie die Bewertung erfolgt
15.5/60Stars — 10 Stars
2.5/25Forks — 3 Forks
0/15Watcher — 1 Watcher
Verwendete Eingangsdaten
forks3
stars10
watchers1
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)
0/18CONTRIBUTING-Leitfaden
0/13.5Verhaltenskodex
0/7.2Issue-Vorlage
0/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingnein
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?

69Mittel · 24 % des Gesamtindex
Wie die Bewertung erfolgt
25.2/54Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab
11.7/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 48 % der Commits
13.5/13.5Breite der Beitragenden — 10 Beitragende
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Verwendete Eingangsdaten
bus_factor2
contributors_sampled10
top_contributor_share0,481
Wie die Bewertung erfolgt
46.8/46.8Issue-Lösungsquote — 100 % der Issues geschlossen
33.8/38.3PR-Annahme — 105/119 entschiedene PRs gemergt
3/15OpenSSF Scorecard: Code-Review — Found 7/26 approved changesets -- score normalized to 2
Verwendete Eingangsdaten
merged_prs105
open_issues0
closed_issues6
issue_closed_ratio1
closed_unmerged_prs14
Wie die Bewertung erfolgt
10/30Organisatorische Trägerschaft — persönliches (Nutzer-)Konto
0/20Verifizierte Domain — für Nutzerkonten nicht anwendbar
8/25Reichweite des Inhabers — 12 Follower von HiDiHo01
20.8/25Kontohistorie — 39 öffentliche Repos, Kontoalter ca. 4 Jahre
Verwendete Eingangsdaten
followers12
owner_typeUser
is_verified
owner_loginHiDiHo01
public_repos39
account_age_days1.660
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Verifizierte Domain. Die verbleibenden Gewichte wurden renormalisiert.

Paketpflege

100Exzellent
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
35/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 0 Tagen
20/20Versionshistorie — 57 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagespython-frank-energie
ecosystemspypi
any_deprecatednein
min_days_since_publish0

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

90Exzellent · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 10 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration — .flake8, .pylintrc
9.6/9.6Pre-Commit-Hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 12 out of 12 merged PRs checked by a CI test -- score normalized to 10
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_configja

Dokumentation

85Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
0/15Dokumentations-/Homepage-Site
10/10Repository-Beschreibung
10/10Topics — 5 Topics
10/10Wiki
Verwendete Eingangsdaten
topicsautomation, homeassistant, homeassistant-integration, python, python3
has_wikija
homepage
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

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

67Mittel · 16 % des Gesamtindex
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — keine Daten
2.5/2.5CI-Tests — 12 out of 12 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-Review — Found 7/26 approved changesets -- score normalized to 2
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
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 — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
2.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 5
5/5SAST — SAST tool is run on all commits
2/5Security-Policy — security policy file detected
0/7.5Signed-Releases — keine Daten
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
6/7.5Vulnerabilities — 2 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6,7
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, signed_releases. 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.

32Gefährdet · 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, .pylintrc
0/11Statische Typprüfung
0/10Reproduzierbare Umgebung
0/10Belegte Agentenpraxis — keine Daten
5/8Automatisierte Wartung — Abhängigkeits-Automatisierung konfiguriert, in den erfassten Commits nicht beobachtet
5/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 5
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfiles
has_dockerfilenein
typed_languagenein
bootstrap_files
has_devcontainernein
has_linter_configja
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share0
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
51.1/55Handhabbare Dateigrößen — 2/28 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes173.122
source_files_sampled28
oversized_source_files2

Eckdaten

10GitHub-Sterne
10Mitwirkende
327Commits, letzte 12 Monate
0Tage seit letztem Push
28Releases
2Bus-Faktor
0offene Issues
PyPIPaket-Ökosysteme

Weitere Details

OpenSSF Scorecard 6.7 / 10
6.7Gesamtwert

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 · 2026-07-18 23:15 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
10CI-Tests12 out of 12 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 7/26 approved changesets -- score normalized to 2
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate 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
5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
10SASTSAST tool is run on all commits
4Security-Policysecurity policy file detected
k. A.Signed-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
8Vulnerabilities2 existing vulnerabilities detected
Direkte Abhängigkeiten 6
RegistryPaketVersionsvorgabeManifest
PyPIaiohttp>=3.13.5pyproject.toml
PyPIPyJWT>=2.10.1pyproject.toml
PyPIcryptography>=44.0.1pyproject.toml
PyPItyping-extensions>=4.10.0pyproject.toml
PyPIpydantic>=2.0.0pyproject.toml
PyPIidna>=3.7pyproject.toml
Alle Abhängigkeiten 94

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 6 direkte und 88 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIaiohttp3.14.1direkt
PyPIcryptography49.0.0direkt
PyPIidna3.18direkt
PyPIpydantic2.13.4direkt
PyPIpyjwt2.13.0direkt
PyPItyping-extensions4.16.0direkt
PyPIaiohappyeyeballs2.7.1indirekt
PyPIaiosignal1.4.0indirekt
PyPIannotated-doc0.0.4indirekt
PyPIannotated-types0.7.0indirekt
PyPIanyio4.14.2indirekt
PyPIaresponsesindirekt
PyPIaresponses3.0.0indirekt
PyPIattrs26.1.0indirekt
PyPIauthlib1.7.2indirekt
PyPIbandit1.9.4indirekt
PyPIblack26.5.1indirekt
PyPIblacken-docs1.20.0indirekt
PyPIcertifi2026.6.17indirekt
PyPIcffi2.1.0indirekt
PyPIcfgv3.5.0indirekt
PyPIclick8.4.2indirekt
PyPIcodespell2.4.3indirekt
PyPIcolorama0.4.6indirekt
PyPIcoverage7.15.2indirekt
PyPIdefusedxml0.7.1indirekt
PyPIdistlib0.4.3indirekt
PyPIdparse0.6.4indirekt
PyPIfilelock3.31.0indirekt
PyPIfreezegunindirekt
PyPIfreezegun1.5.5indirekt
PyPIfrozenlist1.8.0indirekt
PyPIh110.16.0indirekt
PyPIhttpcore1.0.9indirekt
PyPIhttpx0.28.1indirekt
PyPIidentify2.6.19indirekt
PyPIiniconfig2.3.0indirekt
PyPIjinja23.1.6indirekt
PyPIjoblib1.5.3indirekt
PyPIjoserfc1.7.3indirekt
PyPImarkdown-it-py4.2.0indirekt
PyPImarkupsafe3.0.3indirekt
PyPImarshmallow4.3.0indirekt
PyPImdurl0.1.2indirekt
PyPImultidict6.7.1indirekt
PyPImypyindirekt
PyPImypy-extensions1.1.0indirekt
PyPInltk3.10.0indirekt
PyPInodeenv1.10.0indirekt
PyPIpackaging26.2indirekt
PyPIpathspec1.1.1indirekt
PyPIpip26.1.2indirekt
PyPIplatformdirs4.10.1indirekt
PyPIpluggy1.6.0indirekt
PyPIpoetry2.4.1indirekt
PyPIpre-commit4.6.0indirekt
PyPIpre-commit-hooks6.0.0indirekt
PyPIpropcache0.5.2indirekt
PyPIpycparser3.0indirekt
PyPIpydantic-core2.46.4indirekt
PyPIpygments2.20.0indirekt
PyPIpytestindirekt
PyPIpytest9.1.1indirekt
PyPIpytest-asyncioindirekt
PyPIpytest-asyncio1.4.0indirekt
PyPIpytest-covindirekt
PyPIpytest-cov7.1.0indirekt
PyPIpython-dateutil2.9.0.post0indirekt
PyPIpython-discovery1.4.4indirekt
PyPIpython-frank-energieindirekt
PyPIpytokens0.4.1indirekt
PyPIpyyaml6.0.3indirekt
PyPIregex2026.7.10indirekt
PyPIrequests2.34.2indirekt
PyPIrich15.0.0indirekt
PyPIruamel-yaml0.19.1indirekt
PyPIruffindirekt
PyPIruff0.15.22indirekt
PyPIsafety3.8.1indirekt
PyPIsafety-schemas0.0.16indirekt
PyPIshellingham1.5.4indirekt
PyPIsix1.17.0indirekt
PyPIstevedore5.9.0indirekt
PyPIsyrupyindirekt
PyPIsyrupy5.5.3indirekt
PyPItenacity9.1.4indirekt
PyPItomlkit0.15.1indirekt
PyPItqdm4.69.0indirekt
PyPItruststore0.10.4indirekt
PyPItyper0.25.1indirekt
PyPItyping-inspection0.4.2indirekt
PyPIvirtualenv21.6.1indirekt
PyPIvulture2.16indirekt
PyPIyarl1.24.2indirekt
JSON-Rohbericht maschinenlesbar
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            "notes": [],
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            "inputs": {
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                "key": "openssf_scorecard_contributors",
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                "max_points": 46.75
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                "code": "excluded_no_data",
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            "inputs": {
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                "max_points": 30
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                "key": "owner_reach",
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        ],
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            "inputs": {
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                "key": "dangerous_workflow",
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                "key": "dependency_update_tool",
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              {
                "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 5",
                "points": 2.5,
                "status": "partial",
                "details": [],
                "max_points": 5
              },
              {
                "key": "sast",
                "name": "SAST",
                "detail": "SAST tool is run on all commits",
                "points": 5,
                "status": "met",
                "details": [],
                "max_points": 5
              },
              {
                "key": "security_policy",
                "name": "Security-Policy",
                "detail": "security policy file detected",
                "points": 2,
                "status": "partial",
                "details": [],
                "max_points": 5
              },
              {
                "key": "signed_releases",
                "name": "Signed-Releases",
                "detail": "no releases found",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "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": "2 existing vulnerabilities detected",
                "points": 6,
                "status": "partial",
                "details": [],
                "max_points": 7.5
              }
            ]
          },
          {
            "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": 4
            },
            "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": 32,
        "weight": 0,
        "metrics": [
          {
            "key": "ai_agent_context",
            "band": "critical",
            "name": "Agent context & guidance",
            "note": "Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "legible_commit_history"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 1,
            "inputs": {
              "has_llms_txt": false,
              "legible_history_share": null,
              "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": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "at_risk",
            "name": "Verify loop (build / test / typecheck)",
            "note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "demonstrated_agent_practice"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 48,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [],
              "has_dockerfile": false,
              "typed_language": false,
              "bootstrap_files": [],
              "has_devcontainer": false,
              "has_linter_config": true,
              "typecheck_configs": [],
              "agent_commit_share": null,
              "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": ".flake8, .pylintrc",
                "points": 11,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": ".flake8, .pylintrc"
                    }
                  }
                ],
                "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": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "dependency automation configured, none observed in the sampled commits",
                "points": 5,
                "status": "partial",
                "details": [
                  {
                    "code": "dependency_bot_config_only",
                    "params": {}
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 5",
                "points": 5,
                "status": "partial",
                "details": [],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "moderate",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 51,
            "inputs": {
              "primary_language": "Python",
              "largest_source_bytes": 173122,
              "source_files_sampled": 28,
              "oversized_source_files": 2
            },
            "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": "2/28 source files over 60KB",
                "points": 51.1,
                "status": "partial",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 28,
                      "oversized": 2
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          }
        ],
        "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": [],
  "report_type": "repository",
  "generated_at": "2026-07-18T23:16:09.051492Z",
  "schema_version": "0.14.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/h/HiDiHo01/python-frank-energie.svg",
  "full_name": "HiDiHo01/python-frank-energie",
  "license_state": "standard",
  "license_spdx": "Apache-2.0"
}

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Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.14.0 — vollständige Methodik · Metriken-Wiki.

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