Öffentliches RegisterAußergewöhnlich
Software-GesundheitsberichtSchema 0.27.0 · Metriken 2.5.0 · 2026-07-29 00:57 UTC
fastmachinelearning / hls4ml
Machine learning on FPGAs using HLS
fastmachinelearning/hls4ml erreicht einen Gesundheitsindex von 93 von 100 und liegt damit im Bereich Außergewöhnlich. Am stärksten schneidet es bei Engineering Quality (95/100) ab, am schwächsten bei AI Readiness (50/100). Zuletzt heute aktualisiert. 3 Mitwirkende tragen den Großteil der jüngsten Arbeit.
93
gesamt / 100Software-Gesundheitsindex
Metriken werden auf einer standardisierten Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr gewichtetes Mittel, kalibriert auf die Verteilung des öffentlichen Registers, sodass die Stufen Perzentilbedeutung tragen; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze Gefährdet von 34.
93
Außergewöhnlich93-100Die Spitzengruppe des Registers (≈ obere 5 %); erfüllt im Wesentlichen alle geprüften Kriterien
Exzellent80-92Durchgehend stark; geringfügige Lücken
Gut65-79Gesund; Lücken sind begrenzt und beherrschbar
Mittel50-64Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Schwach35-49Wesentliche Schwächen in mehreren Bereichen
Gefährdet20-34Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-19Schwerwiegende 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.
Der gewichtete Gesamtwert 79 wird auf der veröffentlichten Indexskala auf 93 kalibriert (Register-Kalibrierung 2026-08-02).
Eigentümerschaft
Fast Machine Learning LabOrganisation
270 Follower61 öffentliche Reposseit Okt. 2017
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 | hls4ml | 1.3.0 | 3.335 | 16 | vor 130 Tagen |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
86
Entwicklungsaktivität
89Exzellent
Wie die Bewertung erfolgt
| 36/36 | Push-Aktualität — letzter Push vor 0 Tagen |
| 24.9/36 | Commit-Rhythmus — 36/52 Wochen mit Commits |
| 18/18 | Commit-Volumen — 121 Commits im letzten Jahr |
| 10/10 | OpenSSF Scorecard: Maintained — 18 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
Verwendete Eingangsdaten
| commits_last_year | 121 |
| human_commit_share | 0,8 |
| days_since_last_push | 0 |
| active_weeks_last_year | 36 |
Release-Disziplin
82Exzellent
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 20 Releases veröffentlicht |
| 27/36 | Release-Aktualität — letztes Release vor 130 Tagen |
| 19.8/27 | Release-Rhythmus — ein Release etwa alle 119 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 20 |
| latest_release_tag | v1.3.0 |
| releases_from_tags | nein |
| days_since_latest_release | 130 |
| mean_days_between_releases | 119 |
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?
76
Popularität & Verbreitung
86Exzellent
Wie die Bewertung erfolgt
| 53.8/60 | Stars — 2.081 Stars |
| 23/25 | Forks — 572 Forks |
| 9.5/15 | Watcher — 53 Watcher |
Verwendete Eingangsdaten
| forks | 572 |
| stars | 2.081 |
| watchers | 53 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community-Gesundheit
77Gut
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (Apache-2.0) |
| 18/18 | CONTRIBUTING-Leitfaden |
| 0/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 6.3/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| readme_badges | — |
| has_contributing | ja |
| has_issue_template | nein |
| has_code_of_conduct | nein |
| readme_badge_services | — |
| has_pull_request_template | ja |
Wie die Bewertung erfolgt
| 47/80 | Downloads pro Monat — 3.335 Downloads/Monat über pypi |
| 0/20 | Abhängige in der Registry — von diesem Ökosystem nicht ausgewiesen |
Verwendete Eingangsdaten
| packages | hls4ml |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 3.335 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Abhängige in der Registry. Die verbleibenden Gewichte wurden renormalisiert.
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
79
Wie die Bewertung erfolgt
| 36/54 | Bus-Faktor — 3 Beitragende decken die Hälfte aller Commits ab |
| 15.5/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 31 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 75 Beitragende |
| 10/10 | OpenSSF Scorecard: Contributors — project has 45 contributing companies or organizations |
Verwendete Eingangsdaten
| bus_factor | 3 |
| contributors_sampled | 75 |
| top_contributor_share | 0,309 |
Wie die Bewertung erfolgt
| 25/42 | Issue-Lösungsquote — 60 % der Issues geschlossen |
| 25.5/30 | PR-Annahme — 656/771 entschiedene PRs gemergt |
| 0/13 | Newcomer PR acceptance — kein PR eines Erstbeitragenden in 30 Tagen entschieden |
| 15/15 | OpenSSF Scorecard: Code-Review — all changesets reviewed |
Verwendete Eingangsdaten
| merged_prs | 656 |
| open_issues | 196 |
| closed_issues | 288 |
| prs_merged_7d | — |
| prs_decided_7d | — |
| prs_merged_30d | — |
| prs_decided_30d | — |
| issue_closed_ratio | 0,595 |
| closed_unmerged_prs | 115 |
| first_time_authors_30d | — |
| first_time_prs_merged_30d | — |
| first_time_prs_decided_30d | — |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): newcomer_pr_acceptance. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 17.5/25 | Reichweite des Inhabers — 270 Follower von fastmachinelearning |
| 25/25 | Kontohistorie — 61 öffentliche Repos, Kontoalter ca. 8 Jahre |
Verwendete Eingangsdaten
| followers | 270 |
| owner_type | Organization |
| is_verified | — |
| owner_login | fastmachinelearning |
| public_repos | 61 |
| account_age_days | 3.198 |
Paketpflege
100Außergewöhnlich
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 130 Tagen |
| 20/20 | Versionshistorie — 16 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | hls4ml |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 130 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
95
Engineering-Praktiken
92Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 5 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 18/20 | OpenSSF Scorecard: CI-Tests — 25 out of 26 merged PRs checked by a CI test -- score normalized to 9 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
100Außergewöhnlich
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 15/15 | Dokumentations-/Homepage-Site — https://fastmachinelearning.org/hls4ml |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 11 Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | hls, machine-learning, fpga, python, keras, pytorch, onnx, vivado, vivado-hls, neural-network, intel-hls |
| has_wiki | ja |
| homepage | https://fastmachinelearning.org/hls4ml |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
64
Sicherheitslage
55Mittel
Wie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — keine Daten |
| 2.2/2.5 | CI-Tests — 25 out of 26 merged PRs checked by a CI test -- score normalized to 9 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 7.5/7.5 | Code-Review — all changesets reviewed |
| 2.5/2.5 | Contributors — project has 45 contributing companies or organizations |
| 0/10 | Dangerous-Workflow — dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | Lizenz — license file detected |
| 7.5/7.5 | Maintained — 18 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
| 5/5 | Packaging — packaging workflow detected |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 0/5 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — keine Daten |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 5,5 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, signed_releases. Die verbleibenden Gewichte wurden renormalisiert.
Abhängigkeits-Advisories
100Außergewöhnlich
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 | 5 |
| 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:hls4ml@1.3.0 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 5 Paketen. Erreichbarkeit wird nicht analysiert.
AI Readiness
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Trägt ein bewusst kleines Gewicht (4 %): Agenten-Tooling ist ein echtes Pflegesignal, doch ein Repository ohne jedes Signal kann weiterhin 100/100 erreichen.
50
AI-Agentenkontext & Leitlinien
37Schwach
Wie die Bewertung erfolgt
| 0/45 | Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 36.7/40 | Lesbare Commit-Historie — 55 von 80 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text) |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| legible_history_share | 0,688 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Wie die Bewertung erfolgt
| 18/18 | Bootstrap mit einem Befehl — docs/Makefile, hls4ml/templates/quartus/Makefile |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 0/11 | Statische Typprüfung |
| 0/10 | Reproduzierbare Umgebung |
| 0/10 | Belegte Agentenpraxis — keine von Agenten verfassten Commits unter den letzten 100 |
| 8/8 | Automatisierte Wartung — 1 der letzten 100 Commits sind automatisierte Abhängigkeits-Updates |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | — |
| has_dockerfile | nein |
| typed_language | nein |
| bootstrap_files | docs/Makefile, hls4ml/templates/quartus/Makefile |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0,01 |
AI-Code-Lesbarkeit für Modelle
54Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 54.4/55 | Handhabbare Dateigrößen — 7/644 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 261.465 |
| source_files_sampled | 644 |
| oversized_source_files | 7 |
Eckdaten
2.081GitHub-Sterne
75Mitwirkende
121Commits, letzte 12 Monate
0Tage seit letztem Push
20Releases
3Bus-Faktor
196offene 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 ★ / 572 ⇿
0Sterne
572Forks
20Releases
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 10Patch 6
Jeder Punkt umfasst 8 Tage.
OpenSSF Scorecard 5.5 / 10
5.5Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).
| 10 | Binary-Artifacts | no binaries found in the repo |
| k. A. | Branch-Protection | internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md |
| 9 | CI-Tests | 25 out of 26 merged PRs checked by a CI test -- score normalized to 9 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 10 | Code-Review | all changesets reviewed |
| 10 | Contributors | project has 45 contributing companies or organizations |
| 0 | Dangerous-Workflow | dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 10 | Maintained | 18 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
| 10 | Packaging | packaging workflow detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 0 | Security-Policy | security policy file not detected |
| k. A. | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
Direkte Abhängigkeiten 5
| Registry | Paket | Versionsvorgabe | Manifest |
|---|---|---|---|
| PyPI | h5py | — | pyproject.toml |
| PyPI | numpy | — | pyproject.toml |
| PyPI | pydigitalwavetools | ==1.1 | pyproject.toml |
| PyPI | pyyaml | — | pyproject.toml |
| PyPI | quantizers | — | pyproject.toml |
Alle Abhängigkeiten 25
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 5 direkte und 20 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | h5py | — | direkt |
| PyPI | numpy | — | direkt |
| PyPI | pydigitalwavetools | 1.1 | direkt |
| PyPI | pyyaml | — | direkt |
| PyPI | quantizers | — | direkt |
| PyPI | da4ml | — | indirekt |
| PyPI | hgq | — | indirekt |
| PyPI | hgq2 | — | indirekt |
| PyPI | keras | 3.14.1 | indirekt |
| PyPI | keras-tuner | 1.1.3 | indirekt |
| PyPI | onnx | — | indirekt |
| PyPI | ortools | 9.4.1874 | indirekt |
| PyPI | pquant-ml | — | indirekt |
| PyPI | setuptools | — | indirekt |
| PyPI | setuptools-scm | — | indirekt |
| PyPI | sparsepixels | — | indirekt |
| PyPI | sphinx | — | indirekt |
| PyPI | sphinx-contributors | — | indirekt |
| PyPI | sphinx-github-changelog | — | indirekt |
| PyPI | sphinx-rtd-theme | — | indirekt |
| PyPI | sphinx-tabs | — | indirekt |
| PyPI | sympy | — | indirekt |
| PyPI | tensorflow | — | indirekt |
| PyPI | tensorflow-model-optimization | — | indirekt |
| PyPI | toposort | — | indirekt |
Abhängigkeits-Advisories 0
Die Installation von pypi:hls4ml@1.3.0 zieht 5 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
{
"data": {
"repo": {
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"hls",
"machine-learning",
"fpga",
"python",
"keras",
"pytorch",
"onnx",
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"vivado-hls",
"neural-network",
"intel-hls"
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"is_fork": false,
"size_kb": 278631,
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"homepage": "https://fastmachinelearning.org/hls4ml",
"languages": {
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"public_repos": 61,
"account_age_days": 3198
},
"license": {
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"spdx_id": "Apache-2.0",
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"file_present": true,
"scorecard_found": true,
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},
"activity": {
"releases": [
{
"tag": "v1.3.0",
"kind": "minor",
"published_at": "2026-03-20T14:59:12Z"
},
{
"tag": "v1.2.0",
"kind": "minor",
"published_at": "2025-11-03T20:19:32Z"
},
{
"tag": "v1.1.0",
"kind": "minor",
"published_at": "2025-03-17T16:51:08Z"
},
{
"tag": "v1.0.0",
"kind": "major",
"published_at": "2024-12-09T22:47:29Z"
},
{
"tag": "v0.8.1",
"kind": "patch",
"published_at": "2023-12-19T21:00:03Z"
},
{
"tag": "v0.8.0",
"kind": "minor",
"published_at": "2023-11-16T00:10:25Z"
},
{
"tag": "v0.8.0rc1",
"kind": "other",
"published_at": "2023-11-08T00:09:05Z"
},
{
"tag": "v0.7.1",
"kind": "patch",
"published_at": "2023-05-13T15:55:52Z"
},
{
"tag": "v0.7.0",
"kind": "minor",
"published_at": "2023-04-26T17:08:12Z"
},
{
"tag": "v0.7.0rc1",
"kind": "other",
"published_at": "2023-04-15T01:44:06Z"
},
{
"tag": "v0.6.0",
"kind": "minor",
"published_at": "2021-11-12T12:25:59Z"
},
{
"tag": "v0.5.0",
"kind": "minor",
"published_at": "2021-03-05T17:15:46Z"
},
{
"tag": "v0.5.0-beta",
"kind": "prerelease",
"published_at": "2021-01-18T15:37:23Z"
},
{
"tag": "v0.4.0",
"kind": "minor",
"published_at": "2020-10-30T16:49:04Z"
},
{
"tag": "v0.3.0",
"kind": "minor",
"published_at": "2020-07-31T09:06:47Z"
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"status": "partial",
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"weeks": 36
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}
],
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{
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"points": 18,
"status": "met",
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}
],
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{
"key": "openssf_scorecard_maintained",
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"points": 10,
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"details": [],
"max_points": 10
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]
},
{
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"notes": [
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"code": "excluded_no_data",
"params": {
"components": [
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},
{
"code": "weights_renormalized",
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],
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"inputs": {
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"latest_release_tag": "v1.3.0",
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"days_since_latest_release": 130,
"mean_days_between_releases": 119
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"components": [
{
"key": "ships_releases",
"name": "Ships releases",
"detail": "20 releases published",
"points": 27,
"status": "met",
"details": [
{
"code": "releases_published",
"params": {
"count": 20
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}
],
"max_points": 27
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{
"key": "release_recency",
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"detail": "latest release 130 days ago",
"points": 27,
"status": "partial",
"details": [
{
"code": "release_recency",
"params": {
"days": 130
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}
],
"max_points": 36
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{
"key": "release_cadence",
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"detail": "a release every ~119 days",
"points": 19.8,
"status": "partial",
"details": [
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"code": "release_cadence",
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"gap": 119
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}
],
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{
"key": "openssf_scorecard_signed_releases",
"name": "OpenSSF Scorecard: Signed-Releases",
"detail": "no releases found",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 10
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]
},
{
"key": "abandonment",
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"name": "Abandonment",
"note": null,
"notes": [],
"value": 100,
"inputs": {
"cap": null,
"state": "maintained",
"guards": [],
"signals": [],
"red_flag": false,
"multiplier_pct": 100,
"declared_reason": null,
"unverified_reason": null,
"unanswered_open_prs": null,
"unanswered_open_issues": null,
"days_since_last_merged_pr": null,
"days_since_last_human_commit": 8,
"days_since_last_human_commit_is_floor": false
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"components": [
{
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"points": 100,
"status": "met",
"details": [
{
"code": "abandonment_maintained",
"params": {
"days": 8
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}
],
"max_points": 100
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]
}
],
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{
"key": "community",
"band": "good",
"name": "Community & Adoption",
"value": 76,
"weight": 0.17,
"metrics": [
{
"key": "popularity",
"band": "excellent",
"name": "Popularity & adoption",
"note": null,
"notes": [],
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"inputs": {
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"stars": 2081,
"watchers": 53,
"growth_state": "unverified",
"growth_factor_pct": 100,
"growth_unverified_reason": "no_history"
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"components": [
{
"key": "stars",
"name": "Stars",
"detail": "2,081 stars",
"points": 53.8,
"status": "partial",
"details": [
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"code": "stars",
"params": {
"count": 2081
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}
],
"max_points": 60
},
{
"key": "forks",
"name": "Forks",
"detail": "572 forks",
"points": 23,
"status": "partial",
"details": [
{
"code": "forks",
"params": {
"count": 572
}
}
],
"max_points": 25
},
{
"key": "watchers",
"name": "Watchers",
"detail": "53 watchers",
"points": 9.5,
"status": "partial",
"details": [
{
"code": "watchers",
"params": {
"count": 53
}
}
],
"max_points": 15
}
]
},
{
"key": "community_health",
"band": "good",
"name": "Community health",
"note": null,
"notes": [],
"value": 77,
"inputs": {
"has_readme": true,
"has_license": true,
"readme_badges": null,
"has_contributing": true,
"has_issue_template": false,
"has_code_of_conduct": false,
"readme_badge_services": [],
"has_pull_request_template": true
},
"components": [
{
"key": "readme",
"name": "README",
"detail": null,
"points": 22.5,
"status": "met",
"details": [],
"max_points": 22.5
},
{
"key": "license",
"name": "License",
"detail": "recognized license (Apache-2.0)",
"points": 22.5,
"status": "met",
"details": [
{
"code": "license_standard",
"params": {}
},
{
"code": "license_spdx",
"params": {
"spdx": "Apache-2.0"
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}
],
"max_points": 22.5
},
{
"key": "contributing_guide",
"name": "CONTRIBUTING guide",
"detail": null,
"points": 18,
"status": "met",
"details": [],
"max_points": 18
},
{
"key": "code_of_conduct",
"name": "Code of conduct",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 13.5
},
{
"key": "issue_template",
"name": "Issue template",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.2
},
{
"key": "pr_template",
"name": "PR template",
"detail": null,
"points": 6.3,
"status": "met",
"details": [],
"max_points": 6.3
}
]
},
{
"key": "ecosystem_adoption",
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"name": "Ecosystem adoption (downloads)",
"note": "Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.",
"notes": [
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"code": "excluded_no_data",
"params": {
"components": [
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}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 59,
"inputs": {
"packages": [
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"dependents": null,
"ecosystems": "pypi",
"total_downloads": null,
"monthly_downloads": 3335
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"components": [
{
"key": "monthly_downloads",
"name": "Monthly downloads",
"detail": "3,335 downloads/month across pypi",
"points": 47,
"status": "partial",
"details": [
{
"code": "downloads_monthly",
"params": {
"count": 3335,
"ecosystems": "pypi"
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}
],
"max_points": 80
},
{
"key": "registry_dependents",
"name": "Registry dependents",
"detail": "not reported by this ecosystem",
"points": 0,
"status": "excluded",
"details": [
{
"code": "not_reported_by_this_ecosystem",
"params": {}
}
],
"max_points": 20
}
]
}
],
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},
{
"key": "governance",
"band": "good",
"name": "Sustainability & Governance",
"value": 79,
"weight": 0.23,
"metrics": [
{
"key": "maintainer_resilience",
"band": "good",
"name": "Maintainer resilience (bus factor)",
"note": null,
"notes": [],
"value": 75,
"inputs": {
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"contributors_sampled": 75,
"top_contributor_share": 0.309
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"components": [
{
"key": "bus_factor",
"name": "Bus factor",
"detail": "3 contributor(s) cover half of all commits",
"points": 36,
"status": "partial",
"details": [
{
"code": "bus_factor",
"params": {
"count": 3
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}
],
"max_points": 54
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{
"key": "commit_distribution",
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"detail": "top contributor authored 31% of commits",
"points": 15.5,
"status": "partial",
"details": [
{
"code": "top_contributor_share",
"params": {
"share": 31
}
}
],
"max_points": 22.5
},
{
"key": "contributor_breadth",
"name": "Contributor breadth",
"detail": "75 contributors",
"points": 13.5,
"status": "met",
"details": [
{
"code": "contributors_sampled",
"params": {
"count": 75
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}
],
"max_points": 13.5
},
{
"key": "openssf_scorecard_contributors",
"name": "OpenSSF Scorecard: Contributors",
"detail": "project has 45 contributing companies or organizations",
"points": 10,
"status": "met",
"details": [],
"max_points": 10
}
]
},
{
"key": "responsiveness",
"band": "good",
"name": "Issue & PR responsiveness",
"note": "Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
"newcomer_pr_acceptance"
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}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 75,
"inputs": {
"merged_prs": 656,
"open_issues": 196,
"closed_issues": 288,
"prs_merged_7d": null,
"prs_decided_7d": null,
"prs_merged_30d": null,
"prs_decided_30d": null,
"issue_closed_ratio": 0.595,
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"first_time_prs_merged_30d": null,
"first_time_prs_decided_30d": null
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"components": [
{
"key": "issue_resolution",
"name": "Issue resolution",
"detail": "60% of issues closed",
"points": 25,
"status": "partial",
"details": [
{
"code": "issues_closed_share",
"params": {
"share": 60
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}
],
"max_points": 42
},
{
"key": "pr_acceptance",
"name": "PR acceptance",
"detail": "656/771 decided PRs merged",
"points": 25.5,
"status": "partial",
"details": [
{
"code": "decided_prs_merged",
"params": {
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"decided": 771
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}
],
"max_points": 30
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{
"key": "newcomer_pr_acceptance",
"name": "Newcomer PR acceptance",
"detail": "no first-time contributor's PR decided in 30d",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_newcomer_prs",
"params": {
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}
],
"max_points": 13
},
{
"key": "openssf_scorecard_code_review",
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"detail": "all changesets reviewed",
"points": 15,
"status": "met",
"details": [],
"max_points": 15
}
]
},
{
"key": "stewardship",
"band": "good",
"name": "Ownership & stewardship",
"note": null,
"notes": [],
"value": 72,
"inputs": {
"followers": 270,
"owner_type": "Organization",
"is_verified": null,
"owner_login": "fastmachinelearning",
"public_repos": 61,
"account_age_days": 3198
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"components": [
{
"key": "ownership_backing",
"name": "Ownership backing",
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"points": 30,
"status": "met",
"details": [
{
"code": "owner_organization",
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}
],
"max_points": 30
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{
"key": "verified_domain",
"name": "Verified domain",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "owner_reach",
"name": "Owner reach",
"detail": "270 followers of fastmachinelearning",
"points": 17.5,
"status": "partial",
"details": [
{
"code": "owner_followers",
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"login": "fastmachinelearning"
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}
],
"max_points": 25
},
{
"key": "track_record",
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"detail": "61 public repos, account ~8 yr old",
"points": 25,
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"details": [
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"code": "public_repos",
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},
{
"code": "account_age_years",
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]
},
{
"key": "package_maintenance",
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"note": null,
"notes": [],
"value": 100,
"inputs": {
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"components": [
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"code": "packages_published",
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}
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{
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"status": "met",
"details": [
{
"code": "publish_recency",
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}
],
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},
{
"key": "version_history",
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{
"code": "published_versions",
"params": {
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}
],
"max_points": 20
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{
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],
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]
}
],
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},
{
"key": "engineering",
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"weight": 0.19,
"metrics": [
{
"key": "engineering_practices",
"band": "excellent",
"name": "Engineering practices",
"note": null,
"notes": [],
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"inputs": {
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"has_tests": true,
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"components": [
{
"key": "ci_workflows",
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"detail": "5 workflow(s)",
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"details": [
{
"code": "ci_workflows",
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}
],
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{
"key": "tests_present",
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"detail": null,
"points": 24,
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"details": [],
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},
{
"key": "linter_config",
"name": "Linter config",
"detail": null,
"points": 16,
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{
"key": "pre_commit_hooks",
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},
{
"key": "editorconfig",
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{
"key": "openssf_scorecard_ci_tests",
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"status": "partial",
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]
},
{
"key": "documentation",
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"note": null,
"notes": [],
"value": 100,
"inputs": {
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"keras",
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"components": [
{
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},
{
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{
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{
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"points": 10,
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"details": [],
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{
"key": "topics",
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}
],
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{
"key": "wiki",
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"points": 10,
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"details": [],
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}
]
}
],
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},
{
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"weight": 0.16,
"metrics": [
{
"key": "security_posture",
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"notes": [
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"code": "excluded_no_data",
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"components": [
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}
},
{
"code": "weights_renormalized",
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}
],
"value": 55,
"inputs": {
"source": "openssf_scorecard",
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"status": "met",
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{
"key": "branch_protection",
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"points": 0,
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{
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{
"key": "cii_best_practices",
"name": "CII-Best-Practices",
"detail": "no effort to earn an OpenSSF best practices badge detected",
"points": 0,
"status": "missed",
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},
{
"key": "code_review",
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},
{
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"details": [],
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},
{
"key": "dangerous_workflow",
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"detail": "dangerous workflow patterns detected",
"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "dependency_update_tool",
"name": "Dependency-Update-Tool",
"detail": "update tool detected",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "fuzzing",
"name": "Fuzzing",
"detail": "project is not fuzzed",
"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "license",
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"detail": "license file detected",
"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "18 commit(s) and 0 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 0",
"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "sast",
"name": "SAST",
"detail": "SAST tool is not run on all commits -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
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},
{
"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": "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": "0 existing vulnerabilities detected",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
}
]
},
{
"key": "dependency_advisories",
"band": "exceptional",
"name": "Dependency advisories",
"note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:hls4ml@1.3.0 runtime dependency closure — what installing the published package pulls in — 5 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:hls4ml@1.3.0",
"assessed": 5
}
},
{
"code": "advisories_reachability",
"params": {}
}
],
"value": 100,
"inputs": {
"source": "osv",
"advisories": 0,
"affected_packages": 0,
"assessed_packages": 5,
"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": "exceptional",
"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": 5,
"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": "exceptional",
"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"
],
"commit_weight_rule": {
"min_commits": 50,
"min_commit_share": 0.1
},
"review_only_matches": 0,
"below_threshold_exposures": [],
"assessed_self_published_locations": 5
},
"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": "moderate",
"name": "AI Readiness",
"value": 50,
"weight": 0.04,
"metrics": [
{
"key": "ai_agent_context",
"band": "weak",
"name": "Agent context & guidance",
"note": null,
"notes": [],
"value": 37,
"inputs": {
"has_llms_txt": false,
"legible_history_share": 0.688,
"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": "55 of 80 human commits state their intent (structured subject or explanatory body)",
"points": 36.7,
"status": "partial",
"details": [
{
"code": "legible_history",
"params": {
"legible": 55,
"sampled": 80
}
}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "moderate",
"name": "Verify loop (build / test / typecheck)",
"note": null,
"notes": [],
"value": 59,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [],
"has_dockerfile": false,
"typed_language": false,
"bootstrap_files": [
"docs/Makefile",
"hls4ml/templates/quartus/Makefile"
],
"has_devcontainer": false,
"has_linter_config": true,
"typecheck_configs": [],
"agent_commit_share": 0,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0.01
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": "docs/Makefile, hls4ml/templates/quartus/Makefile",
"points": 18,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "docs/Makefile, hls4ml/templates/quartus/Makefile"
}
}
],
"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": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
"name": "Demonstrated agent practice",
"detail": "no agent-authored commits among the last 100",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_authored_commits",
"params": {
"sampled": 100
}
}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "1 of the last 100 commits are automated dependency updates",
"points": 8,
"status": "met",
"details": [
{
"code": "dependency_bot_commits",
"params": {
"count": 1,
"sampled": 100
}
}
],
"max_points": 8
},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"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": 261465,
"source_files_sampled": 644,
"oversized_source_files": 7
},
"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": "7/644 source files over 60KB",
"points": 54.4,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 644,
"oversized": 7
}
}
],
"max_points": 55
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight: agent tooling is a real maintenance signal, but its absence must never gate the top of the scale (calibration saturates at raw 91, so 100/100 remains reachable with AI Readiness at zero)."
}
],
"classification": {
"top": [
"library"
],
"labels": [
"library"
],
"scores": {
"library": 6
},
"primary": "library",
"evidence": [
{
"tier": "distribution",
"label": "library",
"source": "registry:pypi",
"weight": 6
}
],
"artifacts": [],
"confidence": "medium",
"host_extension": false,
"runs_as_process": false,
"consumed_by_code": true
},
"metrics_version": "2.5.0"
},
"warnings": [
"Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token"
],
"report_type": "repository",
"generated_at": "2026-07-29T00:57:59.624569Z",
"schema_version": "0.27.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/f/fastmachinelearning/hls4ml.svg",
"full_name": "fastmachinelearning/hls4ml",
"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 v2.5.0, Schema v0.27.0 — vollständige Methodik · Metriken-Wiki.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.