Python · C++Licencia propia★ 10 estrellas⑂ 2 forksdesde may 2025forkVer en GitHub ↗
ksugahar/Radia tiene un índice de salud de 53 sobre 100, lo que lo sitúa en la banda Moderado. Su puntuación más alta es Vitality (83/100) y la más baja, Community & Adoption (17/100). Se actualizó por última vez hoy. Una sola persona concentra la mayor parte del trabajo reciente.
53
global / 100
Moderado
Índice de salud del software
Las métricas se agrupan en categorías ponderadas sobre una escala de 1 a 100. El resultado global parte de su media; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe el límite 49 (En riesgo). Preparación para IA queda fuera.
53
Excelente85-100Ejemplar; cumple prácticamente todos los criterios evaluados
Bueno70-84Saludable; carencias menores
Moderado50-69Aceptable con carencias notables; se recomienda revisión
En riesgo30-49Debilidades significativas; su adopción exige cautela
Crítico1-29Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
Perfil de puntuación
Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.
4 seguidores3 repositorios públicosdesde ene 2019Kindai University
Este repositorio pertenece a una cuenta personal. Un proyecto con un único propietario conlleva más riesgo de continuidad que uno respaldado por una organización.
¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Es una insignia independiente y experimental — peso 0,0, de modo que se presenta por separado y no afecta a la puntuación de salud global.
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Más detalle
Historial de estrellas y forks 0 ★ / 2 ⇿
0Estrellas
2Forks
3Versiones
Cuándo se añadió cada estrella y fork, recopilado de GitHub y agrupado por día. El crecimiento acumulado se sitúa justo encima de las adiciones diarias que lo componen, de modo que ambos se leen en conjunto: la acumulación orgánica sostenida no se parece en nada a un pico abrupto y efímero. Cuando esa diferencia es medible, se informa como autenticidad del crecimiento.
Mayor 0Menor 1Parche 0
OpenSSF Scorecard 5.1 / 10
5.1agregado
Evaluación de seguridad independiente y agnóstica en cuanto a herramientas, procedente del proyecto de código abierto OpenSSF Scorecard. Cada comprobación premia una práctica de seguridad, no la herramienta de un proveedor concreto. Las comprobaciones que Scorecard no pudo determinar se marcan como n/d y se excluyen de la puntuación de seguridad (nunca se cuentan como cero).Scorecard v5.5.0 · 2026-07-28 03:59 UTC
No fue posible recopilar el conjunto de dependencias resuelto para este informe: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
Informe JSON sin procesar legible por máquina
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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": "…ayer\n\nThe charge basis charged EVERY surface element as an exterior boundary\nface (single-sided sigma = M.n). On a conforming multi-region mesh\n(OCC Glue with a shared internal face) the internal surface elements\nformed a spurious charge sheet: uniform chi=100 on a two-region glued\nball read <Mz> \n[…]\n(uniform sanity band +\nphysical per-region void response). Impacted subset green: 48 tests\nacross all five dispatch paths and the solver level.\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": "Sugahara direction: grow model capacity by stacking frozen Y-URN sections\ninstead of peeling residuals. train_stacked_y_urn adds one section at a\ntime; earlier sections are frozen and the new section is trained against\nthe raw measured response through the composition\n\n series: Z = sum_k 1/Y_k \n[…]\nxposed.\n\nTests: series/parallel stacked smoke with strict-passivity check and the\nmonotone stack RMSE log; near-short initial-contribution lock.\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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"headline": "feat(urn): warm-start the CLN composite seed from the previous lookah…",
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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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"headline": "docs(mcp): state the honest limits of the A-Phi Kelvin goldens",
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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>",
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"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>",
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"headline": "test(kelvin): A-Phi + Periodic Kelvin golden at p=2 - and the A-metho…",
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"headline": "fix(release): exclude backup binaries from uploads",
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{
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}
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{
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}
],
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{
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"note": null,
"notes": [],
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{
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{
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"status": "partial",
"details": [
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{
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"status": "partial",
"details": [],
"max_points": 10
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]
},
{
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"notes": [
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"params": {
"components": [
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}
},
{
"code": "weights_renormalized",
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],
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"issue_closed_ratio": null,
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],
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{
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"status": "missed",
"details": [],
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]
},
{
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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",
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"owner_login": "ksugahar",
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"account_age_days": 2758
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"components": [
{
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"code": "owner_personal",
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}
],
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},
{
"key": "verified_domain",
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{
"code": "not_applicable_to_user_accounts",
"params": {}
}
],
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{
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"details": [
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"login": "ksugahar"
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}
],
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{
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"points": 16.4,
"status": "partial",
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{
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},
{
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}
],
"max_points": 25
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]
},
{
"key": "package_maintenance",
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"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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"code": "packages_published",
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}
],
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{
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{
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"details": [
{
"code": "published_versions",
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"count": 165
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}
],
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{
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"details": [
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],
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}
]
}
],
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},
{
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"weight": 0.2,
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"inputs": {
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"details": [
{
"code": "ci_workflows",
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}
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{
"key": "tests_present",
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{
"key": "linter_config",
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{
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{
"key": "editorconfig",
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{
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"code": "no_data",
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],
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]
},
{
"key": "documentation",
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"inputs": {
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"key": "wiki",
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}
],
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{
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},
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],
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"inputs": {
"source": "openssf_scorecard",
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"components": [
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{
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{
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"code": "no_data",
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],
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{
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"status": "missed",
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{
"key": "code_review",
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{
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"status": "partial",
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{
"key": "dangerous_workflow",
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},
{
"key": "dependency_update_tool",
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{
"key": "fuzzing",
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"status": "missed",
"details": [],
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},
{
"key": "license",
"name": "License",
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"status": "partial",
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},
{
"key": "maintained",
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"points": 7.5,
"status": "met",
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{
"key": "packaging",
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{
"key": "pinned_dependencies",
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"status": "partial",
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{
"key": "sast",
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"status": "missed",
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{
"key": "security_policy",
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{
"key": "signed_releases",
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"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
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{
"key": "token_permissions",
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{
"key": "vulnerabilities",
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]
},
{
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"notes": [
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"code": "jurisdiction_evidence_limits",
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],
"value": 100,
"inputs": {
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"components": [
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"code": "jurisdiction_no_match",
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}
],
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]
}
],
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},
{
"key": "ai_readiness",
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"name": "AI Readiness",
"value": 57,
"weight": 0,
"metrics": [
{
"key": "ai_agent_context",
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"note": null,
"notes": [],
"value": 69,
"inputs": {
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"agent_instruction_max_bytes": 228528
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"components": [
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"key": "agent_instructions",
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"status": "met",
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"code": "file_list",
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}
],
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{
"key": "machine_readable_docs_llms_txt",
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"points": 0,
"status": "missed",
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},
{
"key": "legible_commit_history",
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"status": "partial",
"details": [
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"code": "legible_history",
"params": {
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"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": {
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"has_tests": true,
"lockfiles": [],
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"components": [
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Registros relacionados
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Las puntuaciones son señales, no garantías. Reflejan prácticas públicamente visibles en GitHub; no son una auditoría de código ni una garantía de seguridad.
Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v1.13.0, esquema v0.27.0 — metodología completa · wiki de métricas.
