ksugahar/Radia erreicht einen Gesundheitsindex von 53 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Vitality (83/100) ab, am schwächsten bei Community & Adoption (17/100). Zuletzt heute aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.
53
gesamt / 100
Mittel
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.
53
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.
4 Follower3 öffentliche Reposseit Jan. 2019Kindai University
Dieses Repository gehört einem persönlichen Konto. Ein Projekt mit nur einem Eigentümer trägt ein höheres Kontinuitätsrisiko als ein organisationsgetragenes.
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.
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GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
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Weitere Details
Stern- und Fork-Verlauf 0 ★ / 2 ⇿
0Sterne
2Forks
3Releases
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 0Minor 1Patch 0
OpenSSF Scorecard 5.1 / 10
5.1Gesamtwert
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 · 2026-07-28 03:59 UTC
Der aufgelöste Abhängigkeitssatz konnte für diesen Bericht nicht erhoben werden: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
JSON-Rohbericht maschinenlesbar
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"body": "… solve\n\nAdds the missing verification layer for the coupled HDiv-MMM x HCurl-VIM\nsystem (friction-1 of the topology-optimisation handover):\n\n1. independent block reconstruction [A_M K; K^H Z(s)] from the public\n parts (magnetic_operator / coupling / eddy_impedance / magnetic_rhs /\n eddy_rhs) mu\n[…]\nerged at\nrho = 1e-9 / 0.25 / 0.81 / 1.21 with the monolithic residual <= 6.6e-16\nthroughout. Full eddy-hybrid suite green (72 tests) alongside.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"body": "Record every restart of train_y_admittance_urn (restart index, seed, final\nloss, fit loss, active-basis count) in model.restart_summaries so\nmulti-start scatter -- the initial-condition-dependence evidence -- is\nsaved into result JSONs instead of living only in console output.\n(Implementation by cod\n[…]\nion: restore the accidentally merged\ntest_ideal_reactance_bases_are_pure_and_lossless function definition and\ndrop a dead restart_log variable.)\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"body": "…rors)\n\nSugahara proposal: the S-domain loss is nearly blind to Re(Z) wherever the\nresponse is reactance dominated, although Re spans ~7 decades on the PCB\ncoil. loss_mode=\"log_components\" fits log(Re Z) and arcsinh(Im Z / x0)\nindependently, so both components are matched in relative terms over the\n[…]\nomain RMSE\nremains the manuscript comparison metric.\n\nTests: exact residual values on known perturbations, and a training smoke\nin the new mode.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"body": "Allows the voltage-divider (CLN single-stage) topology to use the\nresonance-rich 34-basis dictionary. Motivated by the stacked-series\nnegative result: summing sections of the same dictionary in Z adds no\nrepresentable directions once one section is at its local optimum\n(PCB/NL87 gains ~0), so the structural gain of the ladder comes from the\ndivider composition Z_even + (Z_odd || R), not from series addition.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"body": "…nce floor\n\nTwo review findings (Sugahara/codex 2026-07-27):\n\n1. The RMS basis normalisation was recomputed on whatever frequency grid was\n passed for evaluation, so the same gates described a slightly different\n circuit on a dense audit grid than on the training grid -- arbitrary-grid\n predic\n[…]\nsivity guarantee stronger\n than any sampled dense-grid audit. Default 0 keeps the legacy model.\n Locked by a lossless-only dictionary test.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"body": "Direction change (Sugahara): stop stacking CLN stages and grow the single\npassive parallel dictionary instead -- the peeled residues are not\npositive-real, so deeper stages fit an unfittable target, while dictionary\ngrowth keeps the raw (positive-real) measurement as the target.\n\nThe CPE exponents a\n[…]\ne nonnegative gate carries the scale, Re(Y) = 0\n(lossless, marginally passive).\n\nTest: exact column values, losslessness, and parameter summary.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"body": "Sugahara review: warm-starting only fixes the initial point; the real\ndefect was the objective swap -- stage n+1 trained against the peeled\nlocal tail (its own S-domain reference) while the network is judged on\nthe whole-ladder fit of the measured response. A stage could therefore\nimprove locally a\n[…]\n rule remains the\nbackstop.\n\nTest: two-stage smoke exercising the frozen-outer-chain composition with\nthe default global objective + warm start.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"body": "…ead branch\n\nThe stage n+1 composite seed used to refit the peeled tail from scratch,\nwhich discards the previous stage lookahead branch -- the one branch that\nwas optimised through the whole-ladder loss. On the PCB data that made the\nreplacement start worse than its predecessor (seed 5.6e-3 vs the\n[…]\nn stopping rule remains the backstop.\n\nTest: warm-started seed at 0 epochs reproduces the donor branch on a\ntarget with a different y_ref scale.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"body": "…r CLN peeling\n\nSugahara direction: enumerate the 8 series-parallel two-terminal RLC\ntopologies and add the damped anti-resonance families the dictionary lacked.\nThe paper 22-basis set only carries the series-RLC (admittance-peak)\nresonator, so a self-resonant impedance peak such as the Sato PCB coi\n[…]\nrk.\n\nTests: 34 URN tests incl. 26-basis dictionary shape/passivity, the\ncoil-antiresonance admittance valley, and the recorded global-RMSE lock.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"headline": "feat(urn): anti-resonance bases + global degradation stopping rule fo…",
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"body": "…wise trust region\n\nPromote the SA/RM-2026 attention-free URN work and harden the CLN peeling\nstage division:\n\n- y_admittance_urn: 22-basis Y-domain URN (S-domain Huber loss, output\n ablation); complex_smooth_l1 gains an optional per-sample weight.\n- cln_peeling_urn: paired even/odd CLN peeling R_n\n[…]\n\n- README + radia-mcp urn knowledge rewritten to the current paired-split\n API (the older start_domain description no longer matched the code).\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>",
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"headline": "feat(urn): attention-free Y-base URN + paired CLN peeling with stage-…",
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"body": "Extend the clebsch_hodograph knowledge topic with the field-plane\n(Chaplygin) design verification promoted to\nvalidation_test/clebsch_legendre/:\n\n- Legendre potential chi = H.r - Psi (coordinates = first derivatives)\n and the B-radial form that feeds measured mu_s(B) with no table\n inversion.\n- En\n[…]\nicard fakes a design error).\n- Honest scope: design direction on a known hodograph domain only; the\n analysis direction remains open research.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "docs(electromagnet): record verified Chaplygin design direction",
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"body": "Promote the saturable hodograph design verification from scratch into\nvalidation_test/clebsch_legendre/ with golden bands and committed result\nsidecars.\n\n- verify_chi_modesum_solver.py: Legendre-potential (chi = H.r - Psi)\n mode-sum machinery vs closed forms. Radial solver rel err < 1e-5 at\n M=401\n[…]\ndates the DESIGN direction on a known hodograph domain; the\nanalysis direction (unknown hodograph image of a fixed pole) remains\nopen research.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "test(clebsch): verify Chaplygin field-plane design end-to-end",
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"body": "Add to verified_recipe: the run-the-goldens-first debug workflow, and an\nexplicit HONEST LIMITS block so the measured claims are not extrapolated\npast their tested class:\n\n- both goldens use a direct solver; iterative solution of the MIXED A-Phi\n system (BDDC on fesA*fesV) is not locked by any gold\n[…]\nlved one\n- the goldens validate the FEM formulation layer only; CLN / Cauer-ladder\n extraction is a separate layer with its own open questions\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"body": "…e-order lock\n\nExtends the A-Phi + Periodic Kelvin golden to the production configuration:\na coaxial circular filament loop (b=0.6 m, z0=0.5 m, I=1 kA) as a genuine\ndecaying Biot-Savart source - the coil + workpiece pattern. The solvers are\nimported from the uniform golden (now parameterized by A_s \n[…]\na debug-signature table (0.45% p-saturated = A-only; ~2% = curve 1;\n -33% = source dropped in kext; +33% = differentiated 0-form Convention B)\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "test(kelvin): reduced A-Phi with a real coil source at p=2 + the curv…",
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"body": "…lback / A-Phi results\n\nPromote the three 2026-07 Kelvin goldens into the docs layer as one executed,\nresult-bearing notebook, and register it in the MCP knowledge.\n\ndocs/kelvin/kelvin_exterior_source_and_aphi.ipynb (executed, 6/6 code cells,\n0 errors) re-runs the locked test modules live rather tha\n[…]\nthe notebook as the rendered walkthrough; module docs-map lists it\n- KELVIN_TRANSFORMATION.md 7.9: A-Phi p/h measured tables + notebook pointer\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "docs(kelvin): executed notebook for the exterior-source / twisted-pul…",
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"oid": "41829df7cb0425755f53981fbb9272535951f65d",
"body": "…d's floor\n\nAnswers \"does the A-Phi Kelvin transformation work at p=2\" by measurement.\nConducting sphere (a/delta = 2) in a uniform AC field, analytic Smythe moment,\ntwo-sphere Periodic Kelvin, every verified_recipe element (nograds -> Periodic,\ngauge reg on non-Kelvin, bonus_intorder=4), one mesh, \n[…]\nmcp verified_recipe: new section \"p=2 and beyond: use A-Phi, not the\n plain A-method\" with the measured tables and the A-Phi weak-form snippet\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
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"headline": "fix(release): exclude backup binaries from uploads",
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"inputs": {
"cap": null,
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],
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{
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"weight": 0.18,
"metrics": [
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"code": "stars",
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}
],
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{
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"points": 0,
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"details": [
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"code": "forks",
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"count": 2
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}
],
"max_points": 25
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{
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]
},
{
"key": "community_health",
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"note": null,
"notes": [],
"value": 19,
"inputs": {
"has_readme": false,
"has_license": false,
"has_contributing": false,
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{
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"code": "license_custom",
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{
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}
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{
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"status": "partial",
"details": [
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}
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{
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{
"key": "openssf_scorecard_contributors",
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"points": 3,
"status": "partial",
"details": [],
"max_points": 10
}
]
},
{
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"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
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]
}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 24,
"inputs": {
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"issue_closed_ratio": null,
"closed_unmerged_prs": 2
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"components": [
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{
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"status": "partial",
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"code": "decided_prs_merged",
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{
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"points": 0,
"status": "missed",
"details": [],
"max_points": 15
}
]
},
{
"key": "stewardship",
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"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
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}
},
{
"code": "weights_renormalized",
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}
],
"value": 39,
"inputs": {
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"owner_type": "User",
"is_verified": null,
"owner_login": "ksugahar",
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"account_age_days": 2758
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"components": [
{
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"details": [
{
"code": "owner_personal",
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}
],
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},
{
"key": "verified_domain",
"name": "Verified domain",
"detail": "not applicable to user accounts",
"points": 0,
"status": "excluded",
"details": [
{
"code": "not_applicable_to_user_accounts",
"params": {}
}
],
"max_points": 20
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{
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"points": 5,
"status": "partial",
"details": [
{
"code": "owner_followers",
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"login": "ksugahar"
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}
],
"max_points": 25
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{
"key": "track_record",
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"points": 16.4,
"status": "partial",
"details": [
{
"code": "public_repos",
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"count": 3
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},
{
"code": "account_age_years",
"params": {
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}
],
"max_points": 25
}
]
},
{
"key": "package_maintenance",
"band": "excellent",
"name": "Package maintenance",
"note": null,
"notes": [],
"value": 100,
"inputs": {
"packages": [
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"ecosystems": "pypi",
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"components": [
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"key": "published_resolvable",
"name": "Published & resolvable",
"detail": "1 package(s) on pypi",
"points": 25,
"status": "met",
"details": [
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"code": "packages_published",
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"ecosystems": "pypi"
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}
],
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{
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"code": "publish_recency",
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"days": 0
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}
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{
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"points": 20,
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"details": [
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"code": "published_versions",
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"count": 165
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}
],
"max_points": 20
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{
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"details": [
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"code": "package_not_deprecated",
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],
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}
]
}
],
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},
{
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"weight": 0.2,
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"key": "engineering_practices",
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],
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"inputs": {
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"details": [
{
"code": "ci_workflows",
"params": {
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}
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{
"key": "tests_present",
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{
"key": "linter_config",
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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": "excluded",
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"code": "no_data",
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],
"max_points": 20
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]
},
{
"key": "documentation",
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"inputs": {
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{
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"points": 0,
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{
"key": "wiki",
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"detail": null,
"points": 10,
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"details": [],
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]
}
],
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{
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"weight": 0.16,
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},
{
"code": "weights_renormalized",
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],
"value": 52,
"inputs": {
"source": "openssf_scorecard",
"checks_evaluated": 17,
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"checks_inconclusive": 1,
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"components": [
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"points": 6.8,
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{
"key": "branch_protection",
"name": "Branch-Protection",
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{
"key": "ci_tests",
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"code": "no_data",
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],
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{
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"name": "CII-Best-Practices",
"detail": "no effort to earn an OpenSSF best practices badge detected",
"points": 0,
"status": "missed",
"details": [],
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{
"key": "code_review",
"name": "Code-Review",
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{
"key": "contributors",
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"points": 0.8,
"status": "partial",
"details": [],
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},
{
"key": "dangerous_workflow",
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"detail": "no dangerous workflow patterns detected",
"points": 10,
"status": "met",
"details": [],
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},
{
"key": "dependency_update_tool",
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"points": 7.5,
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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": 2.2,
"status": "partial",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "30 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",
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"detail": "packaging workflow detected",
"points": 5,
"status": "met",
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{
"key": "pinned_dependencies",
"name": "Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 1",
"points": 0.5,
"status": "partial",
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},
{
"key": "sast",
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"detail": "no SAST tool detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "security_policy",
"name": "Security-Policy",
"detail": "security policy file detected",
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"status": "met",
"details": [],
"max_points": 5
},
{
"key": "signed_releases",
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"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "token_permissions",
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"points": 0,
"status": "missed",
"details": [],
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{
"key": "vulnerabilities",
"name": "Vulnerabilities",
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"status": "met",
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"max_points": 7.5
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]
},
{
"key": "high_risk_jurisdiction_exposure",
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"notes": [
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"code": "jurisdiction_evidence_limits",
"params": {}
}
],
"value": 100,
"inputs": {
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"exposures": [],
"policy_countries": [
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"review_only_matches": 0,
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"components": [
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"key": "policy_exposure_multiplier",
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"details": [
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"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",
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"name": "AI Readiness",
"value": 57,
"weight": 0,
"metrics": [
{
"key": "ai_agent_context",
"band": "moderate",
"name": "Agent context & guidance",
"note": null,
"notes": [],
"value": 69,
"inputs": {
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"legible_history_share": 0.45,
"agent_instruction_files": [
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"agent_instruction_max_bytes": 228528
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"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": "AGENTS.md, CLAUDE.md",
"points": 45,
"status": "met",
"details": [
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"code": "file_list",
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}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "legible_commit_history",
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"status": "partial",
"details": [
{
"code": "legible_history",
"params": {
"legible": 45,
"sampled": 100
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}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "at_risk",
"name": "Verify loop (build / test / typecheck)",
"note": null,
"notes": [],
"value": 49,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [],
"has_dockerfile": false,
"typed_language": false,
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Verwandte Einträge
Geprüfte Repositories, die Katalog-Tags oder Ökosysteme mit ksugahar/Radia teilen. Nebeneinander lesen →
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.
