{
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      "name": "CAN Classifier — 2D classification",
      "description": "Convolutional auto-encoder-based 2D classification of particle stacks. Reads .mrcs + .star produced by the stack-maker plugin; emits class averages + per-particle assignments + FRC curves.\n",
      "category": "two_d_classification",
      "backend_id": "can-classifier",
      "author": "Magellon Team",
      "license": "MIT",
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    {
      "plugin_id": "ctf",
      "name": "CTFFIND4 — CTF estimation",
      "description": "CTF estimation via CTFFIND4. Consumes micrographs from the bus, shells out to the CTFFIND4 binary inside the container, writes the star file under the data plane.\n",
      "category": "ctf",
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      "author": "Magellon Team",
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      "plugin_id": "fft",
      "name": "FFT — magnitude spectrum",
      "description": "Computes the magnitude spectrum FFT of an input MRC image and writes the result as a PNG. Pure-Python via numpy; no external binaries required. Pairs with the Magellon CTF and MotionCor plugins as the third \"always there\" image-processing surface.\n",
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      "backend_id": "fft",
      "author": "Magellon Team",
      "license": "MIT",
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    {
      "plugin_id": "magellon-sample-plugin",
      "name": "Magellon Sample Plugin",
      "description": "Reference fixture for end-to-end install-pipeline testing. Pure Python with zero runtime dependencies — installs in seconds via uv. Not intended for real cryo-EM workloads; use FFT, CTF, MotionCor archives for that.\n",
      "category": "fft",
      "backend_id": "magellon-sample",
      "author": "Magellon Team",
      "license": "MIT",
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    },
    {
      "plugin_id": "motioncor",
      "name": "MotionCor2 — beam-induced motion correction",
      "description": "MotionCor2-based beam-induced motion correction for cryo-EM movies. Pins ~11 GB host RAM; production deployments need at least a g4dn.2xlarge per the operator sizing notes.\n",
      "category": "motioncor",
      "backend_id": "motioncor2",
      "author": "Magellon Team",
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    {
      "plugin_id": "ptolemy",
      "name": "Ptolemy — square + hole detection",
      "description": "Ptolemy ONNX models for low-mag square detection + med-mag hole detection. One plugin process serves both categories via separate consumer registrations; manifest declares the square-detection surface as the public face.\n",
      "category": "square_detection",
      "backend_id": "ptolemy",
      "author": "Magellon Team",
      "license": "MIT",
      "tier": "verified",
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      "plugin_id": "stack-maker",
      "name": "Stack Maker — particle extraction",
      "description": "Extracts boxed particles from a micrograph given picker coordinates; emits RELION-style .mrcs + .star + companion .json. Pure-numpy compute; no external binaries.\n",
      "category": "particle_extraction",
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      "author": "Magellon Team",
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      "tier": "verified",
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    {
      "plugin_id": "template-picker",
      "name": "Template Picker — particle picking",
      "description": "FFT-correlation template-matching particle picker. External broker version of the in-process pp/template_picker; deploy this when picker scaling is needed across multiple workers. Two backends now serve PARTICLE_PICKING — operators pick which one to dispatch to.\n",
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      "backend_id": "template-picker",
      "author": "Magellon Team",
      "license": "MIT",
      "tier": "verified",
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    },
    {
      "plugin_id": "topaz",
      "name": "Topaz — particle picking + denoise",
      "description": "Topaz CNN-based particle picking (and the companion micrograph denoiser). Two categories share one plugin process — backend_id reflects the picking surface; the denoise category is served by the same image via a second consumer registration.\n",
      "category": "topaz_particle_picking",
      "backend_id": "topaz",
      "author": "Magellon Team",
      "license": "MIT",
      "tier": "verified",
      "tags": [
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        "particle-picking",
        "denoise",
        "cnn",
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