diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ed83277..002738a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -39,7 +39,6 @@ repos:
     hooks:
       - id: pydocstyle
         files: ^src/
-        args: ["--add-ignore=D107,D105"]
         additional_dependencies:
           - toml
   - repo: https://github.com/pre-commit/pre-commit
diff --git a/poetry.lock b/poetry.lock
index a8cdc72..ed39594 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
 
 [[package]]
 name = "adani"
@@ -49,13 +49,13 @@ files = [
 
 [[package]]
 name = "astroid"
-version = "3.3.5"
+version = "3.3.8"
 description = "An abstract syntax tree for Python with inference support."
 optional = false
 python-versions = ">=3.9.0"
 files = [
-    {file = "astroid-3.3.5-py3-none-any.whl", hash = "sha256:a9d1c946ada25098d790e079ba2a1b112157278f3fb7e718ae6a9252f5835dc8"},
-    {file = "astroid-3.3.5.tar.gz", hash = "sha256:5cfc40ae9f68311075d27ef68a4841bdc5cc7f6cf86671b49f00607d30188e2d"},
+    {file = "astroid-3.3.8-py3-none-any.whl", hash = "sha256:187ccc0c248bfbba564826c26f070494f7bc964fd286b6d9fff4420e55de828c"},
+    {file = "astroid-3.3.8.tar.gz", hash = "sha256:a88c7994f914a4ea8572fac479459f4955eeccc877be3f2d959a33273b0cf40b"},
 ]
 
 [package.dependencies]
@@ -77,13 +77,13 @@ dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "certifi"
-version = "2024.8.30"
+version = "2024.12.14"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
-    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
+    {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
+    {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
 ]
 
 [[package]]
@@ -167,127 +167,114 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.0"
+version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
-python-versions = ">=3.7.0"
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 [package.dependencies]
@@ -320,73 +307,73 @@ test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"]
 
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 [package.dependencies]
@@ -534,13 +521,13 @@ colors = ["colorama (>=0.4.6)"]
 
 [[package]]
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-version = "3.1.4"
+version = "3.1.5"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
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 ]
 
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@@ -767,6 +754,26 @@ files = [
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 ]
 
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+description = "The NNPDF data repository"
+optional = true
+python-versions = "<4.0,>=3.9"
+files = [
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+
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+pandas = "*"
+"ruamel.yaml" = "*"
+validobj = "*"
+
+[package.extras]
+filter = ["openpyxl", "scipy", "uproot"]
+
 [[package]]
 name = "numba"
 version = "0.59.1"
@@ -1125,13 +1132,13 @@ cffi = ">=1.16.0"
 
 [[package]]
 name = "pygments"
-version = "2.18.0"
+version = "2.19.1"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.extras]
@@ -1139,17 +1146,17 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pylint"
-version = "3.3.1"
+version = "3.3.3"
 description = "python code static checker"
 optional = false
 python-versions = ">=3.9.0"
 files = [
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 [package.dependencies]
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+astroid = ">=3.3.8,<=3.4.0-dev0"
 colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
 dill = [
     {version = ">=0.2", markers = "python_version < \"3.11\""},
@@ -1333,6 +1340,74 @@ pygments = ">=2.6.0,<3.0.0"
 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<8.0.0)"]
 
+[[package]]
+name = "ruamel-yaml"
+version = "0.18.10"
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 version = "1.13.1"
@@ -1377,33 +1452,33 @@ test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "po
 
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+version = "75.8.0"
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 [[package]]
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+    {file = "tomli-2.2.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:286f0ca2ffeeb5b9bd4fcc8d6c330534323ec51b2f52da063b11c502da16f30c"},
+    {file = "tomli-2.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a92ef1a44547e894e2a17d24e7557a5e85a9e1d0048b0b5e7541f76c5032cb13"},
+    {file = "tomli-2.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9316dc65bed1684c9a98ee68759ceaed29d229e985297003e494aa825ebb0281"},
+    {file = "tomli-2.2.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e85e99945e688e32d5a35c1ff38ed0b3f41f43fad8df0bdf79f72b2ba7bc5272"},
+    {file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ac065718db92ca818f8d6141b5f66369833d4a80a9d74435a268c52bdfa73140"},
+    {file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:d920f33822747519673ee656a4b6ac33e382eca9d331c87770faa3eef562aeb2"},
+    {file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a198f10c4d1b1375d7687bc25294306e551bf1abfa4eace6650070a5c1ae2744"},
+    {file = "tomli-2.2.1-cp313-cp313-win32.whl", hash = "sha256:d3f5614314d758649ab2ab3a62d4f2004c825922f9e370b29416484086b264ec"},
+    {file = "tomli-2.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:a38aa0308e754b0e3c67e344754dff64999ff9b513e691d0e786265c93583c69"},
+    {file = "tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc"},
+    {file = "tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff"},
 ]
 
 [[package]]
@@ -1622,20 +1727,20 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.67.0"
+version = "4.67.1"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.67.0-py3-none-any.whl", hash = "sha256:0cd8af9d56911acab92182e88d763100d4788bdf421d251616040cc4d44863be"},
-    {file = "tqdm-4.67.0.tar.gz", hash = "sha256:fe5a6f95e6fe0b9755e9469b77b9c3cf850048224ecaa8293d7d2d31f97d869a"},
+    {file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"},
+    {file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
-dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
 discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
@@ -1665,13 +1770,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.2.3"
+version = "2.3.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
-    {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
+    {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
+    {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
 ]
 
 [package.extras]
@@ -1680,6 +1785,21 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "validobj"
+version = "1.2"
+description = "Validobj gives you valid objects"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "validobj-1.2-py3-none-any.whl", hash = "sha256:a674ff4ae20476ff6839dc55d4d71a229a7588a639bbebdfba2cf944fad4b352"},
+    {file = "validobj-1.2.tar.gz", hash = "sha256:bb03f632ed740220d6ce53cc447dbed023129e26d307c28163c41935ffa270d0"},
+]
+
+[package.extras]
+doc = ["ruamel.yaml", "sphinx", "sphinx_rtd_theme"]
+test = ["hypothesis", "pytest", "pytest-cov"]
+
 [[package]]
 name = "yadism"
 version = "0.13.5"
@@ -1726,7 +1846,7 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 type = ["pytest-mypy"]
 
 [extras]
-complete = ["eko", "yadism"]
+complete = ["eko", "nnpdf-data", "yadism"]
 constraints = []
 dis = ["yadism"]
 vrap = ["eko"]
@@ -1734,4 +1854,4 @@ vrap = ["eko"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "7e8f8ab76b56f4897f4ba7ed8941e868d09e6a0a8f80a5566df0d8e53d83906c"
+content-hash = "62fbbab9c2c94bd1ccc4d3db3ae6bfb8bfcfba7952fd46cbf86acd1ca4064d54"
diff --git a/pyproject.toml b/pyproject.toml
index 0a25615..88f7abb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,12 +38,13 @@ appdirs = "^1.4.4"
 tomli = "^2.0.1"
 yadism = { extras = ["box"], version = "^0.13.5", optional=true, markers = "python_version < '3.13'" }
 eko = { extras = ["box"], version = "^0.14.2", optional=true, markers = "python_version < '3.13'" }
+nnpdf-data = { version = "*", optional = true}
 
 [tool.poetry.extras]
 dis = ["yadism"]
 vrap = ["eko"]
 constraints = ["dis", "vrap"] # integrability + positivity
-complete = ["yadism", "eko"]
+complete = ["yadism", "eko", "nnpdf-data"]
 
 [tool.poetry.group.docs]
 optional = true
@@ -118,4 +119,4 @@ max-line-length = 100
 max-args = 10
 
 [tool.pydocstyle]
-add_ignore = ['D105', 'D107']
+add_ignore = ['D401','D105', 'D107']
diff --git a/src/pinefarm/cli/__init__.py b/src/pinefarm/cli/__init__.py
index a45d93f..9fbfab6 100644
--- a/src/pinefarm/cli/__init__.py
+++ b/src/pinefarm/cli/__init__.py
@@ -1,4 +1,4 @@
 """Provide CLI."""
 
-from . import configs, info, install, list, merge, run, update
+from . import autogen, configs, info, install, list, merge, run, update
 from ._base import command
diff --git a/src/pinefarm/cli/autogen.py b/src/pinefarm/cli/autogen.py
new file mode 100644
index 0000000..cd92f77
--- /dev/null
+++ b/src/pinefarm/cli/autogen.py
@@ -0,0 +1,41 @@
+"""Autogenerate pinecards from NNPDF metadata."""
+
+import click
+import rich
+
+from .. import configs
+from ..external.nnlojet import generate_pinecard_from_nnpdf
+from ._base import command
+
+
+@command.command("autogen")
+@click.argument("dataset", nargs=1)
+@click.option(
+    "--target",
+    help="Target program. Currently only NNLOJET supported",
+    default="NNLOJET",
+)
+@click.option(
+    "--name", help="Name of the pinecard (NNLOJET_<name>), defaults to name=dataset"
+)
+@click.option(
+    "--select-obs",
+    help="Observables to select from the NNPDF available kinematics (max. 2D distributions)",
+    type=str,
+    nargs=2,
+)
+def runcards(dataset, target, select_obs=None, name=None):
+    """Generate a runcard from an NNPDF dataset."""
+    if name is None:
+        name = dataset.upper()
+
+    output = configs.configs["paths"]["runcards"] / f"{target}_{name}"
+
+    if target == "NNLOJET":
+        output_runcards = generate_pinecard_from_nnpdf(
+            dataset, output_path=output, observables=select_obs
+        )
+
+    rich.print("Pinecards written to: ")
+    rich.print("    " + "\n".join(str(i) for i in output_runcards))
+    rich.print("metadata.txt might be empty or incomplete, please modifiy it manually")
diff --git a/src/pinefarm/cli/install.py b/src/pinefarm/cli/install.py
index c54bb7f..3efebc4 100644
--- a/src/pinefarm/cli/install.py
+++ b/src/pinefarm/cli/install.py
@@ -40,3 +40,10 @@ def lhapdf():
     """Install LHAPDF."""
     install.update_environ()
     install.lhapdf()
+
+
+@subcommand.command()
+def nnlojet():
+    """Install NNLOJET."""
+    install.update_environ()
+    install.nnlojet()
diff --git a/src/pinefarm/cli/run.py b/src/pinefarm/cli/run.py
index 16e44c9..d16b316 100644
--- a/src/pinefarm/cli/run.py
+++ b/src/pinefarm/cli/run.py
@@ -1,6 +1,7 @@
 """Compute a dataset and compare using a given PDF."""
 
 import pathlib
+import sys
 import time
 
 import click
@@ -16,7 +17,8 @@
 @click.argument("dataset")
 @click.argument("theory-path", type=click.Path(exists=True))
 @click.option("--pdf", default="NNPDF31_nlo_as_0118_luxqed")
-def subcommand(dataset, theory_path, pdf):
+@click.option("--dry", is_flag=True, help="Don't execute the underlying code")
+def subcommand(dataset, theory_path, pdf, dry):
     """Compute a dataset and compare using a given PDF.
 
     Given a DATASET name and a THEORY-PATH, a runcard is executed with the
@@ -25,46 +27,58 @@ def subcommand(dataset, theory_path, pdf):
     The given PDF (default: `NNPDF31_nlo_as_0118_luxqed`) will be used to
     compare original results with PineAPPL interpolation.
 
+    Parameters
+    ----------
+        dataset: str
+            dataset name
+        theory: dict
+            theory dictionary
+        pdf: str
+            pdf name
     """
     # read theory card from file
     with open(theory_path) as f:
         theory_card = yaml.safe_load(f)
-    main(dataset, theory_card, pdf)
-
-
-def main(dataset, theory, pdf):
-    """Compute a dataset and compare using a given PDF.
-
-    Parameters
-    ----------
-    dataset : str
-        dataset name
-    theory : dict
-        theory dictionary
-    pdf : str
-        pdf name
+        # Fix (possible) problems with CKM matrix loading
+        if isinstance(theory_card.get("CKM"), str):
+            theory_card["CKM"] = [float(i) for i in theory_card["CKM"].split()]
 
-    """
     dataset = pathlib.Path(dataset).name
     timestamp = None
 
     if "-" in dataset:
+        dataset_raw, timestamp = dataset.rsplit("-", 1)
         try:
-            dataset, timestamp = dataset.split("-")
+            # Check whether the timestamp is really an integer
+            _ = int(timestamp)
+            dataset = dataset_raw
         except ValueError:
-            raise ValueError(
-                f"'{dataset}' not valid. '-' is only allowed once,"
-                " to separate dataset name from timestamp."
-            )
+            timestamp = None
 
     rich.print(dataset)
 
-    datainfo = info.label(dataset)
+    try:
+        datainfo = info.label(dataset)
+    except UnboundLocalError as e:
+        raise UnboundLocalError(f"Runcard {dataset} could not be found") from e
 
     rich.print(f"Computing [{datainfo.color}]{dataset}[/]...")
-    runner = datainfo.external(dataset, theory, pdf, timestamp=timestamp)
+    runner = datainfo.external(dataset, theory_card, pdf, timestamp=timestamp)
 
     install_reqs(runner, pdf)
+
+    # Run the preparation step of the runner (if any)
+    runner_stop = runner.preparation()
+    if dry or runner_stop:
+        rich.print(
+            f"""Running in dry mode, exiting now.
+The preparation step can be found in:
+    {runner.dest}"""
+        )
+        sys.exit(0)
+
+    ###### <this part will eventually go to -prepare->
+
     run_dataset(runner)
 
 
diff --git a/src/pinefarm/external/__init__.py b/src/pinefarm/external/__init__.py
index 4b08212..9844e93 100644
--- a/src/pinefarm/external/__init__.py
+++ b/src/pinefarm/external/__init__.py
@@ -25,6 +25,15 @@ def decide_external_tool(dsname: str):
     color:
         color code of the interface
     """
+    # The decisions are usually based on the existence of a `.yaml` file with a specific name
+    # or a prefix in the pinecard
+
+    if dsname.startswith("NNLOJET"):
+        from .nnlojet import NNLOJET
+
+        return NNLOJET, "blue"
+
+    # DIS with yadism
     if (configs["paths"]["runcards"] / dsname / "observable.yaml").exists():
         from . import yad  # pylint: disable=import-outside-toplevel
 
diff --git a/src/pinefarm/external/interface.py b/src/pinefarm/external/interface.py
index a3fa89a..cfd5b07 100644
--- a/src/pinefarm/external/interface.py
+++ b/src/pinefarm/external/interface.py
@@ -70,6 +70,10 @@ def install():
         # Everybody needs LHAPDF unless explicitly skipped
         _ = install.lhapdf()
 
+    def preparation(self):
+        """Run the preparation method of the runner."""
+        return False
+
     @abc.abstractmethod
     def run(self):
         """Execute the program."""
diff --git a/src/pinefarm/external/nnlojet/__init__.py b/src/pinefarm/external/nnlojet/__init__.py
new file mode 100644
index 0000000..8926432
--- /dev/null
+++ b/src/pinefarm/external/nnlojet/__init__.py
@@ -0,0 +1,4 @@
+"""NNLOJET interface."""
+
+from .nnpdf_interface import generate_pinecard_from_nnpdf
+from .runner import NNLOJET
diff --git a/src/pinefarm/external/nnlojet/nnpdf_interface.py b/src/pinefarm/external/nnlojet/nnpdf_interface.py
new file mode 100755
index 0000000..ca7efb4
--- /dev/null
+++ b/src/pinefarm/external/nnlojet/nnpdf_interface.py
@@ -0,0 +1,382 @@
+"""
+Read up all important information form an NNPDF dataset.
+
+The selector generation is very flaky and is (mostly) up to the user for now.
+
+Some examples:
+
+```yaml
+    - histogram with selectors
+    histograms:
+        name: test
+        observable: ptz
+        bins: [10, 20, 40]
+        extra_selectors:
+            - "reject abs_ylp min = 1.37 max = 1.52"
+            - "reject abs_ylm min = 1.37 max = 1.52"
+```
+"""
+
+from copy import deepcopy
+
+import numpy as np
+from nnpdf_data import load_dataset_metadata
+from ruamel.yaml import YAML, CommentedMap
+
+# set-up the yaml reader
+yaml = YAML(pure=True)
+yaml.default_flow_style = False
+yaml.indent(sequence=4, offset=2, mapping=4)
+
+HISTOGRAM_VARIABLES = {"y", "etay", "eta", "pT", "pT2", "M2"}
+
+
+def _legacy_nnpdf_translation(df, proc_type):
+    """When reading variables with k1/k2/k3 tries to figure out to which variables it corresponds."""
+    from validphys.filters import KIN_LABEL  # pylint: disable=E0401
+
+    new_vars = list(KIN_LABEL[proc_type])
+    # Reorganize a bit the names to avoid extra problems
+    if "M_ll" in new_vars:
+        new_vars[new_vars.index("M_ll2")] = "M2"
+    df.columns = df.columns.set_levels(new_vars, level=0)
+
+
+def _df_to_bins(dataframe):
+    """NNPDF kin dataframe to list of bins.
+
+    Convert a dataframe containing min/mid/max for some kin variable
+    into a list of bins as NNLOJET understands it.
+    """
+    # If the NNPDF dataset has been implemented recently
+    # we will have min/max
+    # otherwise we have only mid and have to trick this
+    if np.allclose(dataframe["min"], dataframe["max"]):
+        # Fake min/max and hope for the best
+        mid_points = dataframe["mid"].values
+        shifts = np.diff(mid_points) / 2.0
+        bins = mid_points[:-1] + shifts
+        # Assume that the shift in the first and last points
+        # is the same as the second and next-to-last
+        fpo = [mid_points[0] - shifts[0]]
+        lpo = [mid_points[-1] + shifts[-1]]
+        return np.concatenate([fpo, bins, lpo])
+
+    # Couple maxs and mins, assuming no overlap between bins...
+    all_bins = np.concatenate([dataframe["min"], dataframe["max"]])
+    return np.unique(all_bins)
+
+
+def _1d_histogram(kin_df, hist_var):
+    """Prepare the histogram for a 1d distribution."""
+    histo_bins = _df_to_bins(kin_df[hist_var])
+
+    if hist_var == "pT2":
+        histo_bins = np.sqrt(histo_bins)
+        hist_var = "pT"
+
+    if hist_var == "M2":
+        histo_bins = np.sqrt(histo_bins)
+        hist_var = "M"
+
+    # Don't do more than 3 decimals
+    histo_bins = np.round(histo_bins, decimals=3).tolist()
+
+    return {"name": hist_var, "observable": hist_var, "bins": histo_bins}
+
+
+def _nnlojet_observable(observable, process):
+    """Try to automatically understand the NNLOJET observables given the NNPDF process and obs."""
+    observable = observable.lower()
+    if observable in ("eta", "y", "etay"):
+        if process.upper().startswith("Z"):
+            return "yz"
+        if process.upper().startswith("WP") and not process.upper().endswith("J"):
+            return "ylp"
+        if process.upper().startswith("WM") and not process.upper().endswith("J"):
+            return "ylm"
+    if observable == "pt":
+        if process.upper().startswith("Z"):
+            return "ptz"
+        if process.upper().startswith("W"):
+            return "ptw"
+    if observable == "m" and process.upper().startswith("Z"):
+        return "mll"
+    if observable == "m2":
+        print("\033[91m [WARNING] \033[0m Changed M2 to M in the selectors")
+        return "mll"
+
+    raise ValueError(f"Observable {observable} not recognized for process {process}")
+
+
+def _generate_metadata(arxiv, hepdata, nnpdf_name, output):
+    """Generate a minimal ``metadata.txt`` file."""
+    empty_fields = [
+        "description",
+        "x1_label",
+        "x1_label_tex",
+        "x2_label",
+        "x2_label_tex",
+        "x2_unit",
+        "y_label",
+        "y_label_tex",
+        "y_unit",
+    ]
+    ret = f"""arxiv={arxiv}
+hepdata={hepdata}
+nnpdf_id={nnpdf_name}
+"""
+    ret += "=\n".join(empty_fields) + "="
+    output.write_text(ret)
+
+
+def select_selectors(experiment, process):
+    """A selection of default selectors to be selected depending on the selected experiment.
+
+    The experiment defines the cuts to be applied to each variable.
+    The process defines the name of the variables in NNLOJET
+    """
+    cuts = {
+        "rapidity": (None, None),
+        "pt": (20.0, None),
+        "inv_mass": (None, None),
+        "mt": (None, None),
+    }
+
+    variables = {"rapidity": [], "pt": [], "inv_mass": [], "mt": []}
+
+    if experiment == "LHCB":
+        cuts["rapidity"] = (2.0, 4.5)
+        cuts["inv_mass"] = (60.0, 120.0)
+    elif experiment == "ATLAS":
+        cuts["rapidity"] = (0.0, 2.5)
+        cuts["inv_mass"] = (66.0, 116.0)
+        cuts["pt"] = (25.0, None)
+        cuts["mt"] = (50.0, None)
+    elif experiment == "CMS":
+        cuts["rapidity"] = (0.0, 2.4)
+        cuts["inv_mass"] = (60.0, 120.0)
+        cuts["pt"] = (35.0, None)
+    else:
+        raise NotImplementedError(f"Selectors for {experiment=} not implemented")
+
+    if process.startswith("Z"):
+        variables["rapidity"] += ["yz", "abs_ylp", "abs_ylm"]
+        variables["inv_mass"].append("mll")
+        variables["pt"].append("ptl2")
+    elif process.startswith("W"):
+        w_sign = process[1].lower()
+        variables["rapidity"].append(f"abs_yl{w_sign}")
+        variables["pt"].append(f"ptl{w_sign}")
+        variables["mt"].append("mt")
+
+    selector_options = []
+
+    for cut_type, (cut_min, cut_max) in cuts.items():
+
+        if cut_min is None and cut_max is None:
+            continue
+
+        for variable in variables[cut_type]:
+            tmp = {"observable": variable}
+            if cut_min is not None:
+                tmp["min"] = cut_min
+            if cut_max is not None:
+                tmp["max"] = cut_max
+
+            selector_options.append(tmp)
+
+    return selector_options
+
+
+def _generate_nnlojet_pinecard(runname, process, energy, experiment, histograms):
+    """Generate a pinecard for NNLOJET runs from an NNPDF dataset."""
+    selectors = select_selectors(experiment, process)
+    histograms = deepcopy(histograms)
+
+    if process.startswith("Z0"):
+        process = process.replace("Z0", "Z")
+
+    # Digest the histogram variable
+    for histo in histograms:
+        ob = histo["observable"]
+        histo["observable"] = _nnlojet_observable(ob, process)
+
+    ret = {
+        "runname": runname,
+        "process": {"proc": process, "sqrts": energy},
+        "pdf": "NNPDF40_nnlo_as_01180",
+        "techcut": 1e-7,
+        "histograms": histograms,
+        "multi_channel": 0,
+        "channels": {
+            "LO": "LO",
+            "R": "R",
+            "V": "V",
+            "RR": "RR",
+            "RV": "RV",
+            "VV": "VV",
+        },
+        "parameters": {
+            "MASS[Z]": 91.1876,
+            "MASS[W]": 80.379,
+            "WIDTH[Z]": 2.4952,
+            "WIDTH[W]": 2.085,
+            "SCHEME[alpha]": "Gmu",
+            "GF": "1.1663787d-5",
+        },
+        "selectors": selectors,
+    }
+    return ret
+
+
+def generate_pinecard_from_nnpdf(
+    nnpdf_dataset, scale="etz", output_path=".", observables=None
+):
+    """Generate a NNLOJET pinecard from an NNPDF dataset.
+
+    Takes as input an NNPDF dataset, which will be loaded with the
+    nnpdf_data package.
+
+    If a list of observables is provided, only those in the list will be loaded
+    from the dataframe.
+    """
+    metadata = load_dataset_metadata(nnpdf_dataset)
+    kin_df = metadata.load_kinematics(drop_minmax=False)
+
+    if observables is not None:
+        # If a list of observables is provided, select them from the dataframe
+        # list in case they enter as a tuple
+        kin_df = kin_df.loc[:, list(observables)]
+
+    # Change some variable names:
+    kin_df.rename(columns={"m_ll2": "M2"}, inplace=True)
+    output_runcards = []
+
+    # Put all the information we might need in nicely organized variables
+    process = metadata.process
+    energy = metadata.cm_energy
+    experiment = metadata.experiment
+
+    # Now use the kinematic information to generate histograms
+    kin_variables = kin_df.columns.get_level_values(0)
+
+    # Is it legacy?
+    if "k1" in kin_variables:
+        # Time to translate!
+        _legacy_nnpdf_translation(kin_df, metadata.process_type)
+        kin_variables = kin_df.columns.get_level_values(0)
+
+    hist_vars = list(HISTOGRAM_VARIABLES.intersection(kin_variables))
+
+    # Now preprocess the variables according to the process type
+
+    # Drell Yan
+    if process in ("WPWM", "Z0", "DY"):
+        # Special case: is a histogram binned by invariant mass?
+        if "M2" in hist_vars:
+            if len(kin_df["M2"]["mid"].unique()) == 1:
+                hist_vars.remove("M2")
+
+    # Create the histogram depending on whether this is a 1D or 2D distribution (or total)
+    histograms = None
+
+    if len(hist_vars) == 1:
+        histograms = [_1d_histogram(kin_df, hist_vars[0])]
+    elif len(hist_vars) == 2:
+
+        # Let's see whether we know how to do this 2D distribution
+        if "M2" in hist_vars:
+            svar = "M2"
+        elif "y" in hist_vars:
+            svar = "y"
+        else:
+            raise NotImplementedError(f"Don't know how to do this 2D: {hist_vars}")
+        hist_vars.remove(svar)
+
+        # 2D distributions can only be done when min-max is available, otherwise it's a mess
+        bounds_df = kin_df[svar]
+        if svar.endswith("2"):
+            bounds_df[["min", "max"]] = bounds_df[["min", "max"]].apply(
+                lambda x: np.sqrt(x)
+            )
+        bounds = bounds_df.drop_duplicates().values.tolist()
+
+        nnlojet_var = _nnlojet_observable(svar, process)
+        another_v = hist_vars[0]
+        histograms = []
+
+        for i, (bin_min, mid_val, bin_max) in enumerate(bounds):
+            idx = kin_df[svar]["mid"] == mid_val
+            tmp = _1d_histogram(kin_df[idx], another_v)
+            tmp["name"] = f"{another_v}_bin_{i}"
+            tmp["extra_selectors"] = [
+                {
+                    "observable": f"{nnlojet_var}",
+                    "min": bin_min,
+                    "max": bin_max,
+                }
+            ]
+            histograms.append(tmp)
+    elif len(hist_vars) == 0 and metadata.process_type == "INC":
+        # inclusive cross section, just create a big enough histogram
+        histograms = [{"observable": "y", "bins": [-10.0, 10.0], "name": "tot"}]
+    else:
+        raise NotImplementedError(
+            "3D distributions not implemented or process not recognized"
+        )
+
+    is_normalized = metadata.theory.operation.lower() == "ratio"
+    if is_normalized:
+        print(
+            "\033[91m [WARNING] \033[0m This dataset is probably normalized, you might be missing the runcard for the fiducial cross section"
+        )
+
+    # Prepare the metadata for the data
+    hepdata = metadata._parent.hepdata.url
+    arxiv = metadata._parent.arXiv.url.split("/")[-1]
+    tables = metadata.tables
+    if not hepdata.startswith("https:"):
+        # Try to autoguess the doi
+        hepdata = f"https://doi.org/{hepdata}"
+
+    data_comment = f"arXiv number: {arxiv}, hepdata entry: {hepdata} (tables: {tables})"
+    print(
+        f"\033[91m [WARNING] \033[0m Remember to update he selection cuts in the runcard, see {hepdata}"
+    )
+
+    # For some NNPDF datasets, different processes/energies might be grouped together
+    processes = [process]
+    if process.startswith("WPWM"):
+        processes = [process.replace("WP", ""), process.replace("WM", "")]
+    if process == "DY":
+        processes = ["Z0", "WP", "WM"]
+
+    parent_folder = output_path.parent
+    base_name = output_path.name
+
+    output_runcards = []
+    for proc in processes:
+        runname = nnpdf_dataset.replace(process, proc)
+
+        ret = _generate_nnlojet_pinecard(runname, proc, energy, experiment, histograms)
+        ret["scales"] = {"mur": scale, "muf": scale}
+
+        # Beautify before dumping
+        data = CommentedMap(ret)
+        for key in ret:
+            data.yaml_set_comment_before_after_key(key, before="\n")
+        data.yaml_set_start_comment(data_comment)
+
+        opath = parent_folder / base_name.replace(process, proc)
+        opath.mkdir(exist_ok=True, parents=True)
+
+        runcard_name = (opath / runname.upper()).with_suffix(".yaml")
+        yaml.dump(data, runcard_name)
+        output_runcards.append(runcard_name)
+
+        _generate_metadata(
+            arxiv, hepdata, nnpdf_dataset, runcard_name.with_name("metadata.txt")
+        )
+
+    return output_runcards
diff --git a/src/pinefarm/external/nnlojet/runcardgen.py b/src/pinefarm/external/nnlojet/runcardgen.py
new file mode 100755
index 0000000..2dbb777
--- /dev/null
+++ b/src/pinefarm/external/nnlojet/runcardgen.py
@@ -0,0 +1,441 @@
+"""Autogeneration of NNLOJET runcards.
+
+Module for the autogeneration of NNLOJET runcards using as input
+yaml files containing the NNPDF dataset information.
+"""
+
+import logging
+from collections import defaultdict
+from dataclasses import dataclass, field
+from functools import cached_property
+from pathlib import Path
+
+import numpy as np
+from yaml import safe_load
+
+logger = logging.getLogger(__name__)
+
+HEADER = r"""
+!###############################################!
+!##                                           ##!
+!##     _  ___  ____   ____     ____________  ##!
+!##    / |/ / |/ / /  / __ \__ / / __/_  __/  ##!
+!##   /    /    / /__/ /_/ / // / _/  / /     ##!
+!##  /_/|_/_/|_/____/\____/\___/___/ /_/      ##!
+!##                                           ##!
+!##  Runcard for NNPDF datasets               ##!
+!###############################################!
+"""
+INDT = "  "  # indentation
+
+# Constants for the combine.ini config file
+COMBINE_OUTPUT_FOLDER = "combined"
+COMBINE_HEADER = f"""
+[Paths]
+raw_dir = .
+out_dir = {COMBINE_OUTPUT_FOLDER}
+
+"""
+COMBINE_FOOTER = """
+
+[Options]
+recursive = True
+weights = True
+trim = (3.5, 0.01)
+k-scan = (None, 3, 0.7)
+"""
+
+
+@dataclass
+class Histogram:
+    """Holds histogram information."""
+
+    name: str
+    observable: str
+    bins: list
+    extra_selectors: list = None
+    pineappl: bool = True
+
+    def to_str(self):  # noqa: D102
+        hstr = f"{INDT}{self.observable} > {self.name} {self.bins}"
+        if self.pineappl:
+            hstr += " grid=pine"
+
+        if self.extra_selectors is not None:
+            selectors = [Selector(**i) for i in self.extra_selectors]
+            hstr += f"\n{INDT}HISTOGRAM_SELECTORS\n{INDT*2}"
+            hstr += f"\n{INDT*2}".join(i.to_str() for i in selectors)
+            hstr += f"\n{INDT}END_HISTOGRAM_SELECTORS\n"
+        return hstr
+
+
+@dataclass
+class Selector:
+    """Holds selector information."""
+
+    observable: str
+    min: float = None
+    max: float = None
+
+    def to_str(self):  # noqa: D102
+        ret = f"{INDT}select {self.observable} "
+        if self.min is not None:
+            ret += f" min = {self.min}"
+        if self.max is not None:
+            ret += f" max = {self.max}"
+        return ret
+
+
+@dataclass
+class YamlLOJET:
+    """Definition of the yaml runcard for sending NNLOJET jobs."""
+
+    runname: str
+    process: dict
+    channels: dict
+    scales: dict = field(default_factory=dict)
+    parameters: dict = field(default_factory=dict)
+    selectors: list = None
+    histograms: list = None
+    multi_channel: int = 3
+    techcut: float = 1e-7
+    pdf: str = "NNPDF40_nnlo_as_01180"
+
+    def __post_init__(self):
+        self.histograms = [Histogram(**i) for i in self.histograms]
+        self.selectors = [Selector(**i) for i in self.selectors]
+
+    @cached_property
+    def channel_names_list(self):
+        """List of channels."""
+        return list(self.channels.keys())
+
+    def active_channels(self, active_channels=None):
+        """Digest active channels.
+
+        Loop over all channels in the yamlcard and check whether
+        it correspond to one of the channels in the list `active_channels`
+        e.g., if active_channels = [RR, RV], all RRa_n, RRb_n, and RV_n will be accepted
+        If active_channels is None, return the whole thing for [LO, R, V, RR, RV, VV]
+
+        Returns a dict  {channel_name: list_of_channels}
+        """
+        if active_channels is None:
+            return self.active_channels(["LO", "R", "V", "RR", "RV", "VV"])
+
+        active_channels = list(active_channels)
+
+        if "NLO" in active_channels:
+            active_channels.append("R")
+            active_channels.append("V")
+
+        if "NNLO" in active_channels:
+            active_channels.append("RR")
+            active_channels.append("RV")
+            active_channels.append("VV")
+
+        ret = defaultdict(list)
+
+        # Run over all channels
+        for channel in self.channel_names_list:
+            for level in active_channels:
+                level_prefix = f"{level}_"
+                if level == "RR":
+                    level_prefix = ("RR_", "RRa_", "RRb_")
+
+                if channel.startswith(level_prefix) or channel == level:
+                    ret[level].append(channel)
+
+        return ret
+
+    def get_channel_list(self, channel):
+        """Generate list of channels."""
+        return self.channels[channel]
+
+    @property
+    def process_name(self):
+        """Get process name."""
+        return self.process["proc"]
+
+    def selector_definitions(self):
+        """Get definition of selectors."""
+        return "\n".join(i.to_str() for i in self.selectors)
+
+    def histogram_definitions(self):
+        """Return a string with the definition of all the histograms.
+
+        In general the histogram is defined in the yaml file as a dict with:
+            - name
+              observable
+              bins
+              extra_selectors: dict
+        """
+        return "\n".join(i.to_str() for i in self.histograms)
+
+
+def parse_input_yaml(yaml_path):
+    """Parse the yaml runcard into a YamlLOJET object."""
+    if not yaml_path.exists():
+        raise FileNotFoundError(f"Yaml file {yaml_path} not found")
+    yaml_dict = safe_load(yaml_path.open("r"))
+    return YamlLOJET(**yaml_dict)
+
+
+def _fill_process(process):
+    """Fill process options."""
+    process_name = process["proc"]
+    sqrts = process["sqrts"]
+    """Fill process block given the metadata for the process"""
+    return f"""
+PROCESS  {process_name}
+  collider = pp  sqrts = {sqrts}
+  jet = none[0]
+  decay_type = 1
+END_PROCESS
+"""
+
+
+def _fill_run(runname, pdf, mode_line, techcut=1e-7, multi_channel=3):
+    """Fil run options."""
+    if multi_channel == 0:
+        multi_channel = ".false."
+    return f"""
+RUN  {runname.upper()}
+  PDF = {pdf}[0]
+  tcut = {techcut}
+  scale_coefficients = .true.
+  multi_channel = {multi_channel}
+  iseed = 1
+  {mode_line}
+END_RUN
+"""
+
+
+def _fill_parameters(theory_parameters):
+    """Fill physical parameters."""
+    parameters = {
+        "MASS[Z]": 91.1876,
+        "WIDTH[Z]": 2.4952,
+        "MASS[W]": 80.379,
+        "WIDTH[W]": 2.085,
+        "SCHEME[alpha]": "Gmu",
+        "GF": 1.1663787e-5,
+    }
+
+    parameters.update(theory_parameters)
+    ptext = "\n".join(f"{i} = {j}" for i, j in parameters.items())
+
+    return f"""
+PARAMETERS
+{ptext}
+END_PARAMETERS
+"""
+
+
+def _fill_selectors(metadata):
+    """Fill selectors."""
+    return f"""
+SELECTORS
+{metadata.selector_definitions()}
+END_SELECTORS
+"""
+
+
+def _fill_histograms(metadata, empty=True):
+    """Create the histograms from the declarative definition."""
+    if empty:
+        histogram_content = ""
+    else:
+        histogram_content = metadata.histogram_definitions()
+
+    return f"""
+HISTOGRAMS
+{histogram_content}
+END_HISTOGRAMS
+"""
+
+
+def _fill_scales(scales):
+    """Fill in scales."""
+    mur = scales.get("mur", 91.2)
+    muf = scales.get("muf", 91.2)
+
+    return f"""
+SCALES
+{INDT}mur = {mur}  muf = {muf}
+END_SCALES
+"""
+
+
+def region_str_generator(channel_name):
+    """Given the name of the channel, set up the region."""
+    order = channel_name.split("_", 1)[0]
+    if order.endswith("a"):
+        return "region = a"
+    elif order.endswith("b"):
+        return "region = b"
+    return ""
+
+
+def _fill_channels(channels, region_str=""):
+    """Fill channels."""
+    return f"""
+CHANNELS {region_str}
+  {channels}
+END_CHANNELS
+"""
+
+
+def generate_runcard(
+    metadata: YamlLOJET,
+    channel: str,
+    runcard_name: str = "runcard",
+    is_warmup: bool = False,
+    events: int = int(1e4),
+    iterations: int = 1,
+    output=Path("."),
+):
+    """Generate a NNLOJET runcard given the metadata of the run in the folder defined by che channel name.
+
+    The output path of the runcard will be ./channel/runcard_name_{warmup/production}.run.
+    """
+    channel_dir = output / channel
+    channel_dir.mkdir(exist_ok=True)
+
+    region_str = region_str_generator(channel)
+
+    if is_warmup:
+        mode_str = "warmup"
+    else:
+        mode_str = "production"
+        if iterations > 1:
+            raise ValueError("Only 1 iterations allowed in production")
+    mode_line = f"{mode_str} = {events}[{iterations}]"
+    channels = metadata.get_channel_list(channel)
+
+    pdf = (
+        metadata.pdf
+    )  # we can even check whether this needs to be installed before running!
+
+    runcard_path = channel_dir / f"{runcard_name}_{mode_str}.run"
+    runcard_text = HEADER
+
+    runcard_text += _fill_process(metadata.process)
+    runcard_text += _fill_run(
+        metadata.runname,
+        pdf,
+        mode_line,
+        techcut=metadata.techcut,
+        multi_channel=metadata.multi_channel,
+    )
+    runcard_text += _fill_parameters(metadata.parameters)
+    runcard_text += _fill_selectors(metadata)
+    runcard_text += _fill_histograms(metadata, empty=is_warmup)
+    runcard_text += _fill_scales(metadata.scales)
+    runcard_text += _fill_channels(channels, region_str=region_str)
+    runcard_path.write_text(runcard_text)
+
+    logger.info(f"Runcard written to {runcard_path}")
+
+    return runcard_path
+
+
+## combine.ini generation
+
+
+def _channel_selection(metadata, channels=None):
+    """Generate a selection of channels compatible with the metadata.
+
+    Run over all possible channels in the metadata and add them
+    to the combine script in the right combination whenever they are
+    selected in the arguments of this function.
+
+    Parameters
+    ----------
+        metadata: YamlLOJET
+            information from th e pinecard
+        channels: list(str)
+            list of channels to be run
+    """
+    all_levels = {i.split("_")[0] for i in metadata.channels.keys()}
+
+    lo = ["LO"]
+    nlo = ["R", "V"]
+    nnlo = ["RR", "RV", "VV", "RRa", "RRb"]
+
+    def is_allowed(l):
+        """Return false if the given level is not allowed."""
+        if channels is None:
+            return True
+        if l in channels:
+            return True
+        if l in ("RRa", "RRb") and ("RR" in channels):
+            return True
+        return False
+
+    ret = {}
+    # Now go over every level and include it in the combine.ini
+    # whenever it is both in channels and in the metadata
+    if add_lo := all_levels.intersection(lo):
+        if all(is_allowed(i) for i in add_lo):
+            ret["LO"] = list(add_lo)
+    if add_nlo := all_levels.intersection(nlo):
+        tmp = list(add_nlo.union(add_lo))
+        if all(is_allowed(i) for i in tmp):
+            ret["NLO"] = tmp
+    if add_nnlo := all_levels.intersection(nnlo):
+        tmp = list(add_nnlo.union(add_nlo).union(add_lo))
+        if all(is_allowed(i) for i in tmp):
+            ret["NNLO"] = tmp
+        # Add a exclusive NNLO level for debugging
+        if all(is_allowed(i) for i in add_nnlo):
+            ret["exclusive_nnlo"] = list(add_nnlo)
+
+    if not ret:
+        raise ValueError(f"No channel {channels} found in the pinecard")
+
+    return ret
+
+
+def _generate_channel_merging(metadata, combinations):
+    """Define how subchannels are to be merged.
+
+    Looking at metadata and the list of allowed channels, prepare
+    [Parts], [Merge] and [Final].
+    """
+    allowed_levels = set(np.concatenate(list(combinations.values())))
+    merge_dict = metadata.active_channels(active_channels=allowed_levels)
+
+    parts_list = []
+    merge_list = []
+    for level_name, channel_list in merge_dict.items():
+        parts_list.append("\n".join(channel_list))
+        merge_list.append(f"{level_name} = " + " + ".join(channel_list))
+
+    ret = "\n[Parts]\n" + "\n".join(parts_list)
+    ret += "\n\n[Merge]\n" + "\n".join(merge_list)
+    ret += "\n\n[Final]"
+
+    for order, levels in combinations.items():
+        if all(l in merge_dict for l in levels):
+            ret += f"\n{order} = " + " + ".join(levels)
+
+    return ret
+
+
+def generate_combine_ini(metadata, channels, output=Path(".")):
+    """Generate a NNLOJET combine config file."""
+    # Initialize the file
+    cini_text = COMBINE_HEADER
+
+    # Define the list of observables
+    obs_list = "\n".join([i.name for i in metadata.histograms])
+    cini_text += f"\n[Observables]\ncross\n{obs_list}\n"
+
+    # Define which (nnlojet) channels are neded and how they are merged
+    combinations = _channel_selection(metadata, channels)
+    cini_text += _generate_channel_merging(metadata, combinations)
+
+    cini_text += COMBINE_FOOTER
+
+    (output / "combine.ini").write_text(cini_text)
diff --git a/src/pinefarm/external/nnlojet/runner.py b/src/pinefarm/external/nnlojet/runner.py
new file mode 100644
index 0000000..f969b0e
--- /dev/null
+++ b/src/pinefarm/external/nnlojet/runner.py
@@ -0,0 +1,94 @@
+"""Provides a runner for NNLOJET."""
+
+from yaml import safe_load
+
+from .. import interface
+from .runcardgen import YamlLOJET, generate_combine_ini, generate_runcard
+
+# Reasonable default for warmup and production for DY
+_DEFAULTS = {
+    "warmup": {"iterations": 10, "events": int(5e6)},
+    "production": {"iterations": 1, "events": int(1e6)},
+}
+
+
+class NNLOJET(interface.External):
+    """Interface provider for NNLOJET."""
+
+    def __init__(self, pinecard, theorycard, *args, **kwargs):
+        super().__init__(pinecard, theorycard, *args, **kwargs)
+
+        pinecard = pinecard.replace("NNLOJET_", "")
+        yaml_card = (self.source / pinecard).with_suffix(".yaml")
+        # Save the yaml dictionary from the NNLOJET pinecard
+        self._yaml_dict = safe_load(yaml_card.open("r"))
+
+    def preparation(self):
+        """Run the preparation step for NNLOJET."""
+        # Update the yaml card according to the theory
+        params = self._yaml_dict["parameters"]
+
+        if self.theory.get("CKM", [1.0])[0] != 1.0:
+            params["CKM"] = "FULL"
+
+        translate = [("MZ", "MASS[Z]"), ("MW", "MASS[W]")]
+        for nnpdf_key, nnlojet_key in translate:
+            if nnpdf_key in self.theory:
+                params[nnlojet_key] = self.theory[nnpdf_key]
+
+        # Autodiscover scale if possible
+        if (scdict := self._yaml_dict.get("scales")) is not None:
+            for scale, key in scdict.items():
+                if isinstance(key, str) and key.upper() in self.theory:
+                    scdict[scale] = self.theory[key.upper()]
+
+        # Select channels according to PTO
+        order = self.theory.get("PTO")
+        channels = ["LO"]
+        if order > 0:
+            channels += ["R", "V"]
+        if order > 1:
+            channels += ["RR", "RV", "VV"]
+        if order > 2:
+            raise NotImplementedError("N3LO still not working")
+
+        pinedata = YamlLOJET(**self._yaml_dict)
+        # Given the allowed channel, generate the possible channel choices
+        active_channels = pinedata.active_channels(channels)
+
+        # Generate both the production and warmup runcards with reasonable defaults
+        self.dest.mkdir(exist_ok=True, parents=True)
+        for level_name, level_channels in active_channels.items():
+            print(f"Preparing {len(level_channels)} runcards for {level_name}")
+            for mode in ["warmup", "production"]:
+                nev = _DEFAULTS[mode]["events"]
+                nit = _DEFAULTS[mode]["iterations"]
+                for channel in level_channels:
+                    is_warmup = mode == "warmup"
+                    _ = generate_runcard(
+                        pinedata,
+                        channel,
+                        output=self.dest,
+                        is_warmup=is_warmup,
+                        events=nev,
+                        iterations=nit,
+                    )
+
+        generate_combine_ini(pinedata, active_channels, self.dest)
+        return True
+
+    def run(self):
+        """Run the corresponding NNLOJET runcard."""
+        raise NotImplementedError("NNLOJET running not implemented outside of dry mode")
+
+    def collect_versions(self) -> dict:
+        """NNLOJET version."""
+        return {"nnlojet_version": "secret"}
+
+    def generate_pineappl(self):
+        """Not implemented."""
+        print("Not yet")
+
+    def results(self):
+        """Not implemented."""
+        print("Good luck")
diff --git a/src/pinefarm/install.py b/src/pinefarm/install.py
index edcf890..4b9e3c0 100644
--- a/src/pinefarm/install.py
+++ b/src/pinefarm/install.py
@@ -363,3 +363,9 @@ def prepend(name, value):
     prepend("PATH", configs.configs["paths"]["bin"])
     prepend("LD_LIBRARY_PATH", lib)
     prepend("PKG_CONFIG_PATH", lib / "pkgconfig")
+
+
+def nnlojet():
+    """Installation of NNLOJET."""
+    print("Installation not supported yet")
+    return True
diff --git a/src/pinefarm/tools.py b/src/pinefarm/tools.py
index 3e2503f..0ed9752 100644
--- a/src/pinefarm/tools.py
+++ b/src/pinefarm/tools.py
@@ -32,7 +32,7 @@ def create_output_folder(name, theoryid):
         + "-"
         + name
         + "-"
-        + datetime.datetime.now().strftime("-%Y%m%d%H%M%S")
+        + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
     )
     target.mkdir(exist_ok=True, parents=True)
     return target