Öffentliches RegisterExzellent
Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-07-29 02:07 UTC
flagos-ai / FlagGems
FlagGems is an operator library for large language models implemented in the Triton Language.
flagos-ai/FlagGems erreicht einen Gesundheitsindex von 85 von 100 und liegt damit im Bereich Exzellent. Am stärksten schneidet es bei Vitality (99/100) ab, am schwächsten bei AI Readiness (55/100). Zuletzt heute aktualisiert. 6 Mitwirkende tragen den Großteil der jüngsten Arbeit.
85
gesamt / 100Software-Gesundheitsindex
Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.
85
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Eigentümerschaft
FlagOSOrganisation
248 Follower51 öffentliche Reposseit Nov. 2025
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht |
|---|---|---|---|---|---|
| PyPI | flag_gems | 5.0.2 | - | 7 | vor 95 Tagen |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
99
Entwicklungsaktivität
99Exzellent
Wie die Bewertung erfolgt
| 36/36 | Push-Aktualität — letzter Push vor 0 Tagen |
| 35.3/36 | Commit-Rhythmus — 51/52 Wochen mit Commits |
| 18/18 | Commit-Volumen — 2.337 Commits im letzten Jahr |
| 0/10 | OpenSSF Scorecard: Maintained — keine Daten |
Verwendete Eingangsdaten
| commits_last_year | 2.337 |
| human_commit_share | 0,97 |
| days_since_last_push | 0 |
| active_weeks_last_year | 51 |
Release-Disziplin
100Exzellent
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 6 Releases veröffentlicht |
| 36/36 | Release-Aktualität — letztes Release vor 34 Tagen |
| 27/27 | Release-Rhythmus — ein Release etwa alle 34,1 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 6 |
| latest_release_tag | v5.3.0 |
| releases_from_tags | nein |
| days_since_latest_release | 34 |
| mean_days_between_releases | 34,1 |
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
85
Wie die Bewertung erfolgt
| 49/60 | Stars — 1.057 Stars |
| 22.3/25 | Forks — 473 Forks |
| 6.7/15 | Watcher — 17 Watcher |
Verwendete Eingangsdaten
| forks | 473 |
| stars | 1.057 |
| watchers | 17 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community-Gesundheit
92Exzellent
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (Apache-2.0) |
| 18/18 | CONTRIBUTING-Leitfaden |
| 13.5/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 6.3/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| has_contributing | ja |
| has_issue_template | nein |
| has_code_of_conduct | ja |
| has_pull_request_template | ja |
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
76
Maintainer-Resilienz (Bus-Faktor)
87Exzellent
Wie die Bewertung erfolgt
| 48.6/54 | Bus-Faktor — 6 Beitragende decken die Hälfte aller Commits ab |
| 15.9/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 29 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 99 Beitragende |
| 0/10 | OpenSSF Scorecard: Contributors — keine Daten |
Verwendete Eingangsdaten
| bus_factor | 6 |
| contributors_sampled | 99 |
| top_contributor_share | 0,294 |
Wie die Bewertung erfolgt
| 23.8/46.8 | Issue-Lösungsquote — 51 % der Issues geschlossen |
| 27.6/38.3 | PR-Annahme — 2.869/3.981 entschiedene PRs gemergt |
| 0/15 | OpenSSF Scorecard: Code-Review — keine Daten |
Verwendete Eingangsdaten
| merged_prs | 2.869 |
| open_issues | 313 |
| closed_issues | 325 |
| issue_closed_ratio | 0,509 |
| closed_unmerged_prs | 1.112 |
Eigentümerschaft & Trägerschaft
61Mittel
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 17.2/25 | Reichweite des Inhabers — 248 Follower von flagos-ai |
| 13.9/25 | Kontohistorie — 51 öffentliche Repos, Kontoalter ca. 0 Jahre |
Verwendete Eingangsdaten
| followers | 248 |
| owner_type | Organization |
| is_verified | — |
| owner_login | flagos-ai |
| public_repos | 51 |
| account_age_days | 262 |
Paketpflege
100Exzellent
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 95 Tagen |
| 20/20 | Versionshistorie — 7 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | flag_gems |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 95 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
85
Engineering-Praktiken
92Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 16 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 0/20 | OpenSSF Scorecard: CI-Tests — keine Daten |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
75Gut
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 0/15 | Dokumentations-/Homepage-Site |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 3 Topics |
| 0/10 | Wiki |
Verwendete Eingangsdaten
| topics | pytorch, triton, triton-kernels |
| has_wiki | nein |
| homepage | — |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
78
Sicherheitslage
73Gut
Wie die Bewertung erfolgt
| 30/30 | Sicherheitsrichtlinie (SECURITY.md) |
| 25/25 | Dependabot-Konfiguration |
| 0/25 | Lockfiles für Abhängigkeiten — veröffentlichte Bibliothek — Lockfiles sind Sache der Anwendung, hier nicht erwartet |
| 0/20 | CodeQL-Workflow |
Verwendete Eingangsdaten
| source | file_signals |
| lockfiles | — |
| manifests | cpp/pyproject.toml, pyproject.toml, setup.py |
| has_codeql_workflow | nein |
| has_security_policy | ja |
| has_dependabot_config | ja |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lockfiles für Abhängigkeiten. Die verbleibenden Gewichte wurden renormalisiert.
Abhängigkeits-Advisories
100Exzellent
Wie die Bewertung erfolgt
| 35/35 | Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory |
| 25/25 | Indirekte Abhängigkeiten ohne bekannte Advisories — keine indirekte Abhängigkeit trägt ein bekanntes Advisory |
| 0/40 | Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum |
Verwendete Eingangsdaten
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 6 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:flag_gems@5.0.2 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 6 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.
55
AI-Agentenkontext & Leitlinien
40Gefährdet
Wie die Bewertung erfolgt
| 0/45 | Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 40/40 | Lesbare Commit-Historie — 91 von 97 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text) |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| legible_history_share | 0,938 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Wie die Bewertung erfolgt
| 12.6/18 | Bootstrap mit einem Befehl — docs/themes/hugo-book/go.mod (Toolchain-Konvention, kein Task-Runner) |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 0/11 | Statische Typprüfung |
| 0/10 | Reproduzierbare Umgebung |
| 10/10 | Belegte Agentenpraxis — 24 der letzten 100 Commits von Agenten verfasst oder ihnen zugeschrieben |
| 8/8 | Automatisierte Wartung — 3 der letzten 100 Commits sind automatisierte Abhängigkeits-Updates |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — keine Daten |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | — |
| has_dockerfile | nein |
| typed_language | nein |
| bootstrap_files | — |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | 0,24 |
| toolchain_manifests | docs/themes/hugo-book/go.mod |
| dependency_bot_commit_share | 0,03 |
AI-Code-Lesbarkeit für Modelle
55Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 54.7/55 | Handhabbare Dateigrößen — 17/3.414 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 2.707.645 |
| source_files_sampled | 3.414 |
| oversized_source_files | 17 |
Maschinenlesbare AI-Schnittstellen
40Gefährdet
Wie die Bewertung erfolgt
| 0/40 | API-Schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP-Server |
| 40/40 | Lauffähige Beispiele — example, examples |
Verwendete Eingangsdaten
| example_dirs | example, examples |
| has_mcp_signal | nein |
| api_schema_files | — |
Eckdaten
1.057GitHub-Sterne
99Mitwirkende
2.337Commits, letzte 12 Monate
0Tage seit letztem Push
6Releases
6Bus-Faktor
313offene Issues
PyPIPaket-Ökosysteme
Warnungen zur Datenerhebung
- Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
- Could not fetch pypi package 'flag-gems-cpp-__VENDOR__' from its registry
- OpenSSF Scorecard did not return a usable result (2026/07/29 02:06:51 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks
Weitere Details
Stern- und Fork-Verlauf 0 ★ / 473 ⇿
0Sterne
473Forks
6Releases
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.
Major 1Minor 2Patch 1
Jeder Punkt umfasst 2 Tage.
Direkte Abhängigkeiten 5
| Registry | Paket | Versionsvorgabe | Manifest |
|---|---|---|---|
| PyPI | flag-gems | — | cpp/pyproject.toml |
| PyPI | packaging | >=26.0 | pyproject.toml |
| PyPI | PyYAML | ==6.0.1 | pyproject.toml |
| PyPI | sqlalchemy | ==2.0.48 | pyproject.toml |
| PyPI | numpy | — | pyproject.toml |
Alle Abhängigkeiten 48
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 3 direkte und 45 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | packaging | — | direkt |
| PyPI | pyyaml | 6.0.1 | direkt |
| PyPI | sqlalchemy | 2.0.48 | direkt |
| PyPI | apex | 0.1 | indirekt |
| PyPI | benchflow | 1.0.0 | indirekt |
| PyPI | cambricon-dali | 0.13.0 | indirekt |
| PyPI | cmake | — | indirekt |
| PyPI | colorama | 0.4.6 | indirekt |
| PyPI | coverage | 7.13.5 | indirekt |
| PyPI | datasets | — | indirekt |
| PyPI | distro | 1.9.0 | indirekt |
| PyPI | flash-attn | 2.4.2+7e2dd4d | indirekt |
| PyPI | hydrax | 0.1.0 | indirekt |
| PyPI | hyperparameter | 0.5.6 | indirekt |
| PyPI | mkl | 2024.0.0 | indirekt |
| PyPI | ninja | — | indirekt |
| PyPI | openpyxl | 3.1.5 | indirekt |
| PyPI | pandas | — | indirekt |
| PyPI | pandas | 2.3.3 | indirekt |
| PyPI | psutil | 6.1.0 | indirekt |
| PyPI | pybind11 | — | indirekt |
| PyPI | pybind11 | 3.0.3 | indirekt |
| PyPI | pyefml | 1.9.10 | indirekt |
| PyPI | pytest | 9.0.3 | indirekt |
| PyPI | pytest-md-report | 0.8.0 | indirekt |
| PyPI | regex | 2026.4.4 | indirekt |
| PyPI | scikit-build-core | — | indirekt |
| PyPI | scikit-build-core | 0.12.2 | indirekt |
| PyPI | scipy | — | indirekt |
| PyPI | setuptools | — | indirekt |
| PyPI | setuptools-scm | — | indirekt |
| PyPI | torch | 2.7.0+cpu | indirekt |
| PyPI | torch-gcu | 2.10.0+3.7.20260408 | indirekt |
| PyPI | torch-mlu | 1.29.2+torch2.7.1 | indirekt |
| PyPI | torch-mlu-ops | 1.8.0+torch2.7.1 | indirekt |
| PyPI | torch-musa | 2.9.0 | indirekt |
| PyPI | torch-npu | 2.9.0 | indirekt |
| PyPI | torch-plugin | 0.1.0 | indirekt |
| PyPI | torch-ptpu | 0.2.3+torch2.11 | indirekt |
| PyPI | torch-txda | 0.1.0+20260615.37ba6bbd | indirekt |
| PyPI | torch-xray | 2.0.4 | indirekt |
| PyPI | torchaudio | 2.4.1+metax3.7.2.0 | indirekt |
| PyPI | torchvision | 0.15.1+metax3.7.2.0 | indirekt |
| PyPI | transformers | — | indirekt |
| PyPI | txops | 0.1.0+20260508.60287151 | indirekt |
| PyPI | vllm | — | indirekt |
| PyPI | xformers | 0.0.29+1e7a8ec.d20260114 | indirekt |
| PyPI | xmlir | 1.0.0.1 | indirekt |
Abhängigkeits-Advisories 0
Die Installation von pypi:flag_gems@5.0.2 zieht 6 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": "Fix rms_norm implementation (#5009)",
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"body": "* chore: add Apache 2.0 copyright header to all source files\n\nCopyright 2026 FlagOS Contributors\n\nThis PR was written in part with the assistance of generative AI.\n\n* fix: apply black formatting after adding copyright headers\n\nThis PR was written in part with the assistance of generative AI.",
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"value": 40,
"inputs": {
"example_dirs": [
"example",
"examples"
],
"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": "example, examples",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "example, examples"
}
}
],
"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",
"Could not fetch pypi package 'flag-gems-cpp-__VENDOR__' from its registry",
"OpenSSF Scorecard did not return a usable result (2026/07/29 02:06:51 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks"
],
"report_type": "repository",
"generated_at": "2026-07-29T02:07:20.592958Z",
"schema_version": "0.27.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/f/flagos-ai/FlagGems.svg",
"full_name": "flagos-ai/FlagGems",
"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.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.