diff --git a/cookbook/assets/mst_network_tyk2.json b/cookbook/assets/mst_network_tyk2.json
new file mode 100644
index 0000000..a7254f5
--- /dev/null
+++ b/cookbook/assets/mst_network_tyk2.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/cookbook/repairing_broken_networks.ipynb b/cookbook/repairing_broken_networks.ipynb
new file mode 100644
index 0000000..7256849
--- /dev/null
+++ b/cookbook/repairing_broken_networks.ipynb
@@ -0,0 +1,853 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ca95f143-c048-462b-9cf3-9304842c0cba",
+   "metadata": {},
+   "source": [
+    "# Repairing a broken ligand network\n",
+    "\n",
+    "In a relative binding free energy (RBFE) campaign, some transformations may fail.\n",
+    "If enough edges fail, the ligand network can become disconnected and can no longer\n",
+    "rank all ligands in a single connected component.\n",
+    "\n",
+    "This cookbook follows the repair workflow end to end:\n",
+    "\n",
+    "1. **Identify failures** from campaign results and reconstruct the damaged planned network.\n",
+    "2. **Repair the ligand network**, either manually or by selecting new edges by score.\n",
+    "3. **Build replacement transformations** for the newly proposed repair edges."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef334d57",
+   "metadata": {},
+   "source": [
+    "## Fetch the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b5d4a134",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fetching /Users/hannahbaumann/.local/share/mamba/envs/openfe_env/lib/python3.12/site-packages/openfecli/tests/data/rbfe_results.tar.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "import locale\n",
+    "locale.getpreferredencoding = lambda: \"UTF-8\"  # colab hack; not needed locally\n",
+    "\n",
+    "# Fetch and extract the CLI tutorial results from Zenodo\n",
+    "!openfe fetch rbfe-tutorial-results\n",
+    "!tar -xf rbfe_results.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09d26ef5",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8d681408",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hannahbaumann/.local/share/mamba/envs/openfe_env/lib/python3.12/site-packages/pydantic/_internal/_generate_schema.py:2274: UnsupportedFieldAttributeWarning: The 'validate_default' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'validate_default' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
+      "  warnings.warn(\n",
+      "/Users/hannahbaumann/.local/share/mamba/envs/openfe_env/lib/python3.12/site-packages/pydantic/_internal/_generate_schema.py:2274: UnsupportedFieldAttributeWarning: The 'validate_default' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'validate_default' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import copy\n",
+    "import json\n",
+    "import pathlib\n",
+    "import warnings\n",
+    "from collections.abc import Callable, Iterable\n",
+    "\n",
+    "from rdkit import Chem\n",
+    "from gufe import AtomMapper, AtomMapping\n",
+    "from openfe import (\n",
+    "    AlchemicalNetwork,\n",
+    "    ChemicalSystem,\n",
+    "    LigandAtomMapping,\n",
+    "    LigandNetwork,\n",
+    "    ProteinComponent,\n",
+    "    SolventComponent,\n",
+    "    Transformation,\n",
+    ")\n",
+    "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n",
+    "from openfe.setup import KartografAtomMapper, lomap_scorers\n",
+    "from openfe.setup.ligand_network_planning import generate_network_from_names\n",
+    "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n",
+    "from gufe.tokenization import JSON_HANDLER\n",
+    "\n",
+    "from konnektor.network_planners import MstConcatenator\n",
+    "from konnektor.network_tools import connected_subnetworks, merge_two_networks, delete_transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd95fe5d-a486-4739-b01c-3518fdd67f4c",
+   "metadata": {},
+   "source": [
+    "## 1. Identify the failures\n",
+    "\n",
+    "We start from the **planned ligand network** and the campaign results.\n",
+    "\n",
+    "The results tell us which planned transformations completed successfully. We remove the\n",
+    "failed edges from the planned network. This preserves every planned ligand,\n",
+    "including ligands whose only transformation failed and therefore become isolated\n",
+    "nodes.\n",
+    "\n",
+    "The result is a `damaged_network`: the planned network after unsuccessful\n",
+    "transformations have been removed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89e33757",
+   "metadata": {},
+   "source": [
+    "### Load the planned network\n",
+    "\n",
+    "This is the connected network the campaign set out to run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8a702d3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "planned connected: True\n",
+      "planned ligands: 10\n",
+      "planned edges: 9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "planned_network = LigandNetwork.from_json(\"assets/mst_network_tyk2.json\")\n",
+    "\n",
+    "print(\"planned connected:\", planned_network.is_connected())\n",
+    "print(\"planned ligands:\", len(planned_network.nodes))\n",
+    "print(\"planned edges:\", len(planned_network.edges))\n",
+    "\n",
+    "plot_atommapping_network(planned_network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdf56bda",
+   "metadata": {},
+   "source": [
+    "### Parse the campaign results\n",
+    "\n",
+    "For this example we simulate two failed transformations by omitting both legs of\n",
+    "their result files before parsing:\n",
+    "\n",
+    "- `ejm_31 -- ejm_46`, which breaks a connection in the network;\n",
+    "- `ejm_31 -- ejm_48`, which was `ejm_48`'s only planned transformation.\n",
+    "\n",
+    "A transformation is considered successful only when both its complex and solvent\n",
+    "legs have usable results.\n",
+    "\n",
+    "While reading the results we also recover a reference complex system and the\n",
+    "protocol settings. These are not needed to repair the ligand network itself, but\n",
+    "will be used in Part 3 to construct replacement transformations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "647092f0-572a-413b-946c-5e45d2cde02b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _load_result_json(path):\n",
+    "    \"\"\"Load a usable result JSON, or return None.\"\"\"\n",
+    "    result = json.load(open(path, \"rb\"), cls=JSON_HANDLER.decoder)\n",
+    "\n",
+    "    if not isinstance(result, dict):\n",
+    "        return None\n",
+    "    if result.get(\"__qualname__\") in (\"AlchemicalNetwork\", \"Transformation\"):\n",
+    "        return None\n",
+    "    if \"unit_results\" not in result:\n",
+    "        return None\n",
+    "    if all(\"exception\" in unit for unit in result[\"unit_results\"].values()):\n",
+    "        return None\n",
+    "\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "def _units(result):\n",
+    "    data = result[\"protocol_result\"][\"data\"]\n",
+    "    return data[next(iter(data))]\n",
+    "\n",
+    "\n",
+    "def _extract_result_edge(result):\n",
+    "    \"\"\"Return (mapping, phase, stateA), or None if inputs are unavailable.\"\"\"\n",
+    "    units = _units(result)\n",
+    "    if not units or \"stateA\" not in units[0][\"inputs\"]:\n",
+    "        return None\n",
+    "\n",
+    "    inputs = units[0][\"inputs\"]\n",
+    "    state_a = ChemicalSystem.from_dict(inputs[\"stateA\"])\n",
+    "    mapping = LigandAtomMapping.from_dict(inputs[\"ligandmapping\"])\n",
+    "\n",
+    "    is_complex = any(\n",
+    "        isinstance(component, ProteinComponent)\n",
+    "        for component in state_a.components.values()\n",
+    "    )\n",
+    "\n",
+    "    return mapping, (\"complex\" if is_complex else \"solvent\"), state_a\n",
+    "\n",
+    "\n",
+    "def parse_results(result_files):\n",
+    "    \"\"\"Return successful ligand mappings and campaign information.\"\"\"\n",
+    "    phases_by_pair = {}\n",
+    "    mapping_by_pair = {}\n",
+    "    reference_complex_system = None\n",
+    "    reference_protocol = None\n",
+    "\n",
+    "    for path in result_files:\n",
+    "        result = _load_result_json(path)\n",
+    "        if result is None:\n",
+    "            continue\n",
+    "\n",
+    "        if reference_protocol is None:\n",
+    "            settings = _units(result)[0][\"inputs\"].get(\"settings\")\n",
+    "            reference_protocol = RelativeHybridTopologyProtocol(settings=settings)\n",
+    "\n",
+    "        parsed = _extract_result_edge(result)\n",
+    "        if parsed is None:\n",
+    "            continue\n",
+    "\n",
+    "        mapping, phase, state_a = parsed\n",
+    "        pair = frozenset((mapping.componentA.key, mapping.componentB.key))\n",
+    "\n",
+    "        phases_by_pair.setdefault(pair, set()).add(phase)\n",
+    "        mapping_by_pair[pair] = mapping\n",
+    "\n",
+    "        if phase == \"complex\" and reference_complex_system is None:\n",
+    "            reference_complex_system = state_a\n",
+    "\n",
+    "    successful_edges = [\n",
+    "        mapping_by_pair[pair]\n",
+    "        for pair, phases in phases_by_pair.items()\n",
+    "        if phases == {\"complex\", \"solvent\"}\n",
+    "    ]\n",
+    "\n",
+    "    return successful_edges, reference_complex_system, reference_protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "be375fe0-99b4-49c9-830a-6bdcd77a6b75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "found 54 result files\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hannahbaumann/.local/share/mamba/envs/openfe_env/lib/python3.12/site-packages/gufe/components/smallmoleculecomponent.py:304: UserWarning: The atom hybridization data was not found and has been set to unspecified. This can be fixed by recreating the SmallMoleculeComponent from the rdkit molecule after running sanitization.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "successful planned edges: 7\n",
+      "recovered protocol: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "RESULTS_DIR = pathlib.Path(\"results\")\n",
+    "\n",
+    "result_files = sorted(\n",
+    "    path\n",
+    "    for path in RESULTS_DIR.rglob(\"*.json\")\n",
+    "    if not any(part.startswith(\"shared_\") for part in path.parts)\n",
+    ")\n",
+    "print(f\"found {len(result_files)} result files\")\n",
+    "\n",
+    "# Simulate two failed transformations by dropping both legs from the result set.\n",
+    "DROP_EDGES = [\n",
+    "    (\"ejm_31\", \"ejm_46\"),\n",
+    "    (\"ejm_31\", \"ejm_48\"),\n",
+    "]\n",
+    "\n",
+    "result_files = [\n",
+    "    path\n",
+    "    for path in result_files\n",
+    "    if not any(a in path.name and b in path.name for a, b in DROP_EDGES)\n",
+    "]\n",
+    "\n",
+    "successful_edges, reference_complex_system, reference_protocol = parse_results(\n",
+    "    result_files\n",
+    ")\n",
+    "\n",
+    "print(\"successful planned edges:\", len(successful_edges))\n",
+    "print(\"recovered protocol:\", reference_protocol is not None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d43eca54-15c4-4cf3-96e7-5f244412fbaf",
+   "metadata": {},
+   "source": [
+    "### Remove the failed edges from the plan\n",
+    "\n",
+    "We compare the successful result mappings with the planned mappings to identify\n",
+    "the failed transformations. \n",
+    "\n",
+    "`delete_transformations` deliberately keeps all nodes from the planned network. Setting\n",
+    "`must_stay_connected=False` is important here: a broken network is exactly what\n",
+    "we expect to create before repairing it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "adf03470-6ea4-4f0a-8607-b8166746d23e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "failed edges: [('lig_ejm_31', 'lig_ejm_48'), ('lig_ejm_31', 'lig_ejm_46')]\n",
+      "damaged_network connected: False\n",
+      "damaged_network ligands: 10\n",
+      "damaged_network edges: 7\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def _edge_pair(edge):\n",
+    "    \"\"\"Return an unordered, serialization-stable ligand pair.\"\"\"\n",
+    "    return frozenset((edge.componentA.name, edge.componentB.name))\n",
+    "\n",
+    "successful_pairs = {_edge_pair(edge) for edge in successful_edges}\n",
+    "failed_edges = [\n",
+    "    edge\n",
+    "    for edge in planned_network.edges\n",
+    "    if _edge_pair(edge) not in successful_pairs\n",
+    "]\n",
+    "\n",
+    "print(\n",
+    "    \"failed edges:\",\n",
+    "    [(edge.componentA.name, edge.componentB.name) for edge in failed_edges],\n",
+    ")\n",
+    "\n",
+    "damaged_network = delete_transformation(\n",
+    "    planned_network,\n",
+    "    failed_edges,\n",
+    "    must_stay_connected=False,\n",
+    ")\n",
+    "\n",
+    "print(\"damaged_network connected:\", damaged_network.is_connected())\n",
+    "print(\"damaged_network ligands:\", len(damaged_network.nodes))\n",
+    "print(\"damaged_network edges:\", len(damaged_network.edges))\n",
+    "\n",
+    "plot_atommapping_network(damaged_network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ad134e2-8617-46d0-8be2-e9457279cc75",
+   "metadata": {},
+   "source": [
+    "## 2. Repair the ligand network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9b19391",
+   "metadata": {},
+   "source": [
+    "### Repair manually by ligand name\n",
+    "\n",
+    "If you already know which ligands should bridge the disconnected pieces, you can\n",
+    "propose those mappings directly.\n",
+    "\n",
+    "Here we choose one representative ligand from each fragment and connect the\n",
+    "fragments in a chain. In a real campaign, these choices would normally be based\n",
+    "on chemical judgement or inspection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a07a9acb-a5f5-4c08-931d-aa5c1fbe3e85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def repair_network_by_names(\n",
+    "    ligand_network: LigandNetwork,\n",
+    "    names: list[tuple[str, str]],\n",
+    "    mapper: AtomMapper,\n",
+    ") -> tuple[LigandNetwork, list[AtomMapping]]:\n",
+    "    \"\"\"Repair a ligand network using specified ligand pairs.\n",
+    "\n",
+    "    New mappings are generated between the ligand pairs specified by ``names``\n",
+    "    and added to ``ligand_network``.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    ligand_network : LigandNetwork\n",
+    "        The ligand network to repair.\n",
+    "    names : list[tuple[str, str]]\n",
+    "        Pairs of ligand names to connect. Each pair identifies the two ligands\n",
+    "        for which a new mapping should be generated.\n",
+    "    mapper : AtomMapper\n",
+    "        Atom mapper used to propose mappings between the specified ligand pairs.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    repaired_network : LigandNetwork\n",
+    "        The ligand network with the new mappings added.\n",
+    "    new_edges : list[AtomMapping]\n",
+    "        The new mappings added to the ligand network.\n",
+    "    \"\"\"\n",
+    "    repair_edges = generate_network_from_names(\n",
+    "        ligands=ligand_network.nodes,\n",
+    "        mapper=mapper,\n",
+    "        names=names,\n",
+    "    )\n",
+    "\n",
+    "    repaired_network = merge_two_networks(\n",
+    "        ligand_network,\n",
+    "        repair_edges,\n",
+    "    )\n",
+    "\n",
+    "    new_edges = set(repaired_network.edges) - set(ligand_network.edges)\n",
+    "\n",
+    "    return repaired_network, list(new_edges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bdecf5a2-2783-4062-8a91-c85c4ce12203",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hannahbaumann/.local/share/mamba/envs/openfe_env/lib/python3.12/site-packages/konnektor/network_planners/generators/explicit_network_generator.py:85: UserWarning: Generated network is not connected as a single network.\n",
+      "  warnings.warn(\"Generated network is not connected as a single network.\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mapper = KartografAtomMapper()\n",
+    "repaired_by_name, new_edges_by_name = repair_network_by_names(\n",
+    "    damaged_network,\n",
+    "    names=[\n",
+    "        (\"lig_ejm_31\", \"lig_ejm_46\"),\n",
+    "        (\"lig_ejm_31\", \"lig_ejm_48\"),\n",
+    "    ],\n",
+    "    mapper=mapper,\n",
+    ")\n",
+    "plot_atommapping_network(repaired_by_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "287fc9e5",
+   "metadata": {},
+   "source": [
+    "### Repair automatically by score\n",
+    "\n",
+    "Instead of choosing the bridges manually, we can let a `MstConcatenator` score\n",
+    "candidate mappings between the connected fragments and select the bridges needed\n",
+    "to reconnect them.\n",
+    "\n",
+    "The original planned edges are passed as `exclude_edges`. This prevents the\n",
+    "repair from proposing a ligand pair that was already attempted in the original\n",
+    "campaign, including the transformations that failed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a3ee1ea3-661e-4861-a5b9-6fbc49535204",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def repair_network_by_score(\n",
+    "    ligand_network: LigandNetwork,\n",
+    "    mapper: AtomMapper,\n",
+    "    scorer: Callable[[AtomMapping], float],\n",
+    "    exclude_edges: Iterable[AtomMapping] | None = None,\n",
+    ") -> tuple[LigandNetwork, list[AtomMapping]]:\n",
+    "    \"\"\"Repair a disconnected ligand network using scored mappings.\n",
+    "\n",
+    "    The connected subnetworks of ``ligand_network`` are reconnected using a\n",
+    "    minimum spanning tree. Candidate mappings between subnetworks are generated\n",
+    "    with ``mapper`` and ranked using ``scorer``.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    ligand_network : LigandNetwork\n",
+    "        The ligand network to repair.\n",
+    "    mapper : AtomMapper\n",
+    "        Atom mapper used to propose mappings between ligands in different\n",
+    "        connected subnetworks.\n",
+    "    scorer : Callable[[AtomMapping], float]\n",
+    "        Callable used to score proposed mappings.\n",
+    "    exclude_edges : Iterable[AtomMapping] | None, optional\n",
+    "        Mappings identifying ligand pairs that should not be used when\n",
+    "        reconnecting the network, by default None.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    repaired_network : LigandNetwork\n",
+    "        The repaired, connected ligand network.\n",
+    "    new_edges : list[AtomMapping]\n",
+    "        The new mappings added to reconnect the ligand network.\n",
+    "    \"\"\"\n",
+    "    subnetworks = connected_subnetworks(ligand_network)\n",
+    "\n",
+    "    concatenator = MstConcatenator(\n",
+    "        mappers=mapper,\n",
+    "        scorer=scorer,\n",
+    "    )\n",
+    "\n",
+    "    repaired_network = concatenator.concatenate_networks(\n",
+    "        subnetworks,\n",
+    "        exclude_edges=exclude_edges,\n",
+    "    )\n",
+    "\n",
+    "    new_edges = set(repaired_network.edges) - set(ligand_network.edges)\n",
+    "\n",
+    "    return repaired_network, list(new_edges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a76d5b25-e8c7-46a3-be5c-dfea6eb6e196",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mapper = KartografAtomMapper()\n",
+    "scorer = lomap_scorers.default_lomap_score\n",
+    "\n",
+    "repaired_by_score, new_edges_by_score = repair_network_by_score(\n",
+    "    damaged_network,\n",
+    "    mapper=mapper,\n",
+    "    scorer=scorer,\n",
+    "    exclude_edges=planned_network.edges,\n",
+    ")\n",
+    "plot_atommapping_network(repaired_by_score)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7d78c66-03d9-4ff7-9923-fe2b063df8d9",
+   "metadata": {},
+   "source": [
+    "At this stage the repaired ligand network is connected again, and `new_edges_by_score`\n",
+    "contains exactly the ligand mappings that still need to be simulated.\n",
+    "\n",
+    "Keeping this difference explicit is useful while exploring the API: a future\n",
+    "network-repair interface may want to expose both the repaired network and the\n",
+    "newly proposed edges, but this cookbook does not require a new result type."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c2fa5f7",
+   "metadata": {},
+   "source": [
+    "# 3. Build replacement transformations\n",
+    "\n",
+    "The network repair produced new `LigandAtomMapping` edges. To continue the RBFE\n",
+    "campaign, these mappings must be turned into runnable solvent and complex\n",
+    "`Transformation`s.\n",
+    "\n",
+    "We reuse the protein, solvent, cofactors, and protocol settings recovered from\n",
+    "the successful campaign results in Part 1. For a charge-changing mapping we add\n",
+    "the explicit charge-correction settings used by this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "69943267-eebf-4814-932c-c35014ec24cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _formal_charge_difference(mapping):\n",
+    "    charge_a = Chem.rdmolops.GetFormalCharge(mapping.componentA.to_rdkit())\n",
+    "    charge_b = Chem.rdmolops.GetFormalCharge(mapping.componentB.to_rdkit())\n",
+    "    return charge_a - charge_b\n",
+    "\n",
+    "\n",
+    "def _protocol_for(mapping, reference_protocol):\n",
+    "    \"\"\"Reuse campaign settings and add charge correction when required.\"\"\"\n",
+    "    if abs(_formal_charge_difference(mapping)) < 1e-3:\n",
+    "        return reference_protocol\n",
+    "\n",
+    "    from openff.units import unit\n",
+    "\n",
+    "    settings = copy.deepcopy(reference_protocol.settings)\n",
+    "    settings.alchemical_settings.explicit_charge_correction = True\n",
+    "    settings.simulation_settings.production_length = 20 * unit.nanosecond\n",
+    "    settings.simulation_settings.n_replicas = 22\n",
+    "    settings.lambda_settings.lambda_windows = 22\n",
+    "\n",
+    "    return RelativeHybridTopologyProtocol(settings=settings)\n",
+    "\n",
+    "\n",
+    "def _components_from_reference(reference_complex_system):\n",
+    "    protein = next(\n",
+    "        component\n",
+    "        for component in reference_complex_system.components.values()\n",
+    "        if isinstance(component, ProteinComponent)\n",
+    "    )\n",
+    "\n",
+    "    solvent = next(\n",
+    "        (\n",
+    "            component\n",
+    "            for component in reference_complex_system.components.values()\n",
+    "            if isinstance(component, SolventComponent)\n",
+    "        ),\n",
+    "        SolventComponent(),\n",
+    "    )\n",
+    "\n",
+    "    cofactors = {\n",
+    "        name: component\n",
+    "        for name, component in reference_complex_system.components.items()\n",
+    "        if name.startswith(\"cofactor\")\n",
+    "    }\n",
+    "\n",
+    "    return protein, solvent, cofactors\n",
+    "\n",
+    "\n",
+    "def build_alchemical_network(\n",
+    "    repair_edges,\n",
+    "    reference_complex_system,\n",
+    "    reference_protocol,\n",
+    "):\n",
+    "    \"\"\"Create solvent and complex transformations for repair mappings.\"\"\"\n",
+    "    protein, solvent, cofactors = _components_from_reference(\n",
+    "        reference_complex_system\n",
+    "    )\n",
+    "\n",
+    "    transformations = []\n",
+    "\n",
+    "    for mapping in repair_edges:\n",
+    "        protocol = _protocol_for(mapping, reference_protocol)\n",
+    "\n",
+    "        if protocol is not reference_protocol:\n",
+    "            warnings.warn(\n",
+    "                f\"charge-changing edge {mapping.componentA.name} -> \"\n",
+    "                f\"{mapping.componentB.name}; added explicit charge correction\"\n",
+    "            )\n",
+    "\n",
+    "        for leg in (\"solvent\", \"complex\"):\n",
+    "            state_a_components = {\n",
+    "                \"ligand\": mapping.componentA,\n",
+    "                \"solvent\": solvent,\n",
+    "            }\n",
+    "            state_b_components = {\n",
+    "                \"ligand\": mapping.componentB,\n",
+    "                \"solvent\": solvent,\n",
+    "            }\n",
+    "\n",
+    "            if leg == \"complex\":\n",
+    "                state_a_components[\"protein\"] = protein\n",
+    "                state_b_components[\"protein\"] = protein\n",
+    "                state_a_components.update(cofactors)\n",
+    "                state_b_components.update(cofactors)\n",
+    "\n",
+    "            transformations.append(\n",
+    "                Transformation(\n",
+    "                    stateA=ChemicalSystem(state_a_components),\n",
+    "                    stateB=ChemicalSystem(state_b_components),\n",
+    "                    mapping=mapping,\n",
+    "                    protocol=protocol,\n",
+    "                    name=(\n",
+    "                        f\"{leg}_\"\n",
+    "                        f\"{mapping.componentA.name}_\"\n",
+    "                        f\"{mapping.componentB.name}\"\n",
+    "                    ),\n",
+    "                )\n",
+    "            )\n",
+    "\n",
+    "    return AlchemicalNetwork(transformations)\n",
+    "\n",
+    "\n",
+    "def save_transformations(network, out_dir):\n",
+    "    out_dir = pathlib.Path(out_dir)\n",
+    "    transformations_dir = out_dir / \"transformations\"\n",
+    "    transformations_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "    with open(out_dir / \"alchemical_network.json\", \"w\") as file:\n",
+    "        json.dump(network.to_dict(), file, cls=JSON_HANDLER.encoder)\n",
+    "\n",
+    "    for transformation in network.edges:\n",
+    "        transformation.to_json(\n",
+    "            transformations_dir / f\"{transformation.name}.json\"\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "42ac44ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wrote 4 transformations\n"
+     ]
+    }
+   ],
+   "source": [
+    "alchemical_network = build_alchemical_network(\n",
+    "    new_edges_by_score,\n",
+    "    reference_complex_system,\n",
+    "    reference_protocol,\n",
+    ")\n",
+    "\n",
+    "save_transformations(\n",
+    "    alchemical_network,\n",
+    "    \"repair_transformations\",\n",
+    ")\n",
+    "\n",
+    "print(\"wrote\", len(alchemical_network.edges), \"transformations\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "667bfbfe-f5e4-4c2a-83dc-b528ad725f5e",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "The repair workflow is deliberately split into three layers:\n",
+    "\n",
+    "1. **Campaign results → failed planned edges**\n",
+    "2. **Damaged ligand network → new repair edges**\n",
+    "3. **New repair edges → runnable transformations**\n",
+    "\n",
+    "For now these steps remain explicit. That makes the cookbook useful both to users\n",
+    "who need to repair a campaign today and as a concrete example for deciding which\n",
+    "parts should eventually be wrapped by higher-level OpenFE APIs."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/environment.yaml b/environment.yaml
index 7bcdcab..f9a40a3 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -20,5 +20,5 @@ dependencies:
     - git+https://github.com/OpenFreeEnergy/openfe_analysis@v0.5.0 # Use conda-forge v0.5 after new openfe release updates pins on openfe-analysis
     - git+https://github.com/OpenFreeEnergy/cinnabar@0.6.0 # Use conda-forge v0.6.0 after new openfe release updates pins on cinnabar
     - git+https://github.com/OpenFreeEnergy/kartograf@v2.0.0  # Use conda-forge v2.0 after new openfe release updates pins on kartogra
-    - git+https://github.com/OpenFreeEnergy/konnektor@v0.4.0  # use conda-forge v0.4.0 after openfe conda-forge release
+    - git+https://github.com/OpenFreeEnergy/konnektor@avoid_edges_concatenator  # use conda-forge v0.4.0 after openfe conda-forge release
     - git+https://github.com/OpenFreeEnergy/Lomap@v3.3.0  # use conda-forge v3.3.0 after openfe conda-forge release