diff --git a/pipelines/README.md b/pipelines/README.md
new file mode 100644
index 00000000..920ca953
--- /dev/null
+++ b/pipelines/README.md
@@ -0,0 +1 @@
+# Pipelines
diff --git a/pipelines/build_model.py b/pipelines/build_pyrenew_model.py
similarity index 100%
rename from pipelines/build_model.py
rename to pipelines/build_pyrenew_model.py
diff --git a/pipelines/default_priors.py b/pipelines/default_priors.py
deleted file mode 100755
index 05c4a9f4..00000000
--- a/pipelines/default_priors.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import jax.numpy as jnp
-import numpyro.distributions as dist
-import pyrenew.transformation as transformation
-from numpyro.infer.reparam import LocScaleReparam
-from pyrenew.randomvariable import DistributionalVariable, TransformedVariable
-
-i0_first_obs_n_rv = DistributionalVariable(
-    "i0_first_obs_n_rv",
-    dist.Beta(1, 10),
-)
-
-initialization_rate_rv = DistributionalVariable(
-    "rate", dist.Normal(0, 0.01), reparam=LocScaleReparam(0)
-)
-
-r_logmean = jnp.log(1.2)
-r_logsd = jnp.log(jnp.sqrt(2))
-
-log_r_mu_intercept_rv = DistributionalVariable(
-    "log_r_mu_intercept_rv", dist.Normal(r_logmean, r_logsd)
-)
-
-eta_sd_rv = DistributionalVariable(
-    "eta_sd", dist.TruncatedNormal(0.15, 0.05, low=0)
-)
-
-autoreg_rt_rv = DistributionalVariable("autoreg_rt", dist.Beta(2, 40))
-
-
-inf_feedback_strength_rv = TransformedVariable(
-    "inf_feedback",
-    DistributionalVariable(
-        "inf_feedback_raw",
-        dist.LogNormal(jnp.log(50), jnp.log(1.5)),
-    ),
-    transforms=transformation.AffineTransform(loc=0, scale=-1),
-)
-# Could be reparameterized?
-
-p_ed_visit_mean_rv = DistributionalVariable(
-    "p_ed_visit_mean",
-    dist.Normal(
-        transformation.SigmoidTransform().inv(0.005),
-        0.3,
-    ),
-)  # logit scale
-
-
-p_ed_visit_w_sd_rv = DistributionalVariable(
-    "p_ed_visit_w_sd_sd", dist.TruncatedNormal(0, 0.01, low=0)
-)
-
-
-autoreg_p_ed_visit_rv = DistributionalVariable(
-    "autoreg_p_ed_visit_rv", dist.Beta(1, 100)
-)
-
-ed_visit_wday_effect_rv = TransformedVariable(
-    "ed_visit_wday_effect",
-    DistributionalVariable(
-        "ed_visit_wday_effect_raw",
-        dist.Dirichlet(jnp.array([5, 5, 5, 5, 5, 5, 5])),
-    ),
-    transformation.AffineTransform(loc=0, scale=7),
-)
-
-# Based on looking at some historical posteriors.
-phi_rv = DistributionalVariable("phi", dist.LogNormal(4, 1))
diff --git a/pipelines/render_webpage.R b/pipelines/diagnostic_report/render_diagnostic_report.R
similarity index 100%
rename from pipelines/render_webpage.R
rename to pipelines/diagnostic_report/render_diagnostic_report.R
diff --git a/pipelines/fit_model.py b/pipelines/fit_pyrenew_model.py
similarity index 97%
rename from pipelines/fit_model.py
rename to pipelines/fit_pyrenew_model.py
index cc2a731e..a63278db 100644
--- a/pipelines/fit_model.py
+++ b/pipelines/fit_pyrenew_model.py
@@ -4,7 +4,9 @@
 
 import jax
 import numpy as np
-from build_model import build_model_from_dir
+from build_pyrenew_model import (
+    build_model_from_dir,
+)
 
 
 def fit_and_save_model(
diff --git a/pipelines/forecast_state.py b/pipelines/forecast_state.py
index bcaa61f2..f2604305 100644
--- a/pipelines/forecast_state.py
+++ b/pipelines/forecast_state.py
@@ -11,13 +11,15 @@
 import tomli_w
 import tomllib
 from prep_data import process_and_save_state
+from prep_eval_data import save_eval_data
 from pygit2 import Repository
-from save_eval_data import save_eval_data
 
 numpyro.set_host_device_count(4)
 
-from fit_model import fit_and_save_model  # noqa
-from generate_predictive import generate_and_save_predictions  # noqa
+from fit_pyrenew_model import fit_and_save_model  # noqa
+from generate_predictive import (
+    generate_and_save_predictions,
+)  # noqa
 
 
 def record_git_info(model_run_dir: Path):
@@ -125,13 +127,13 @@ def convert_inferencedata_to_parquet(
     return None
 
 
-def postprocess_forecast(
+def plot_and_save_state_forecast(
     model_run_dir: Path, pyrenew_model_name: str, timeseries_model_name: str
 ) -> None:
     result = subprocess.run(
         [
             "Rscript",
-            "pipelines/postprocess_state_forecast.R",
+            "pipelines/plot_and_save_state_forecast.R",
             f"{model_run_dir}",
             "--pyrenew-model-name",
             f"{pyrenew_model_name}",
@@ -141,7 +143,7 @@ def postprocess_forecast(
         capture_output=True,
     )
     if result.returncode != 0:
-        raise RuntimeError(f"postprocess_forecast: {result.stderr}")
+        raise RuntimeError(f"plot_and_save_state_forecast: {result.stderr}")
     return None
 
 
@@ -159,17 +161,17 @@ def score_forecast(model_run_dir: Path) -> None:
     return None
 
 
-def render_webpage(model_run_dir: Path) -> None:
+def render_diagnostic_report(model_run_dir: Path) -> None:
     result = subprocess.run(
         [
             "Rscript",
-            "pipelines/render_webpage.R",
+            "pipelines/diagnostic_report/render_diagnostic_report.R",
             f"{model_run_dir}",
         ],
         capture_output=True,
     )
     if result.returncode != 0:
-        raise RuntimeError(f"render_webpage: {result.stderr}")
+        raise RuntimeError(f"render_diagnostic_report: {result.stderr}")
     return None
 
 
@@ -363,11 +365,11 @@ def main(
     logger.info("Conversion complete.")
 
     logger.info("Postprocessing forecast...")
-    postprocess_forecast(model_run_dir, "pyrenew_e", "timeseries_e")
+    plot_and_save_state_forecast(model_run_dir, "pyrenew_e", "timeseries_e")
     logger.info("Postprocessing complete.")
 
     logger.info("Rendering webpage...")
-    render_webpage(model_run_dir)
+    render_diagnostic_report(model_run_dir)
     logger.info("Rendering complete.")
 
     if score:
diff --git a/pipelines/generate_predictive.py b/pipelines/generate_predictive.py
index 773a5503..fb3a5c0d 100644
--- a/pipelines/generate_predictive.py
+++ b/pipelines/generate_predictive.py
@@ -3,7 +3,9 @@
 from pathlib import Path
 
 import arviz as az
-from build_model import build_model_from_dir
+from build_pyrenew_model import (
+    build_model_from_dir,
+)
 
 
 def generate_and_save_predictions(
diff --git a/pipelines/create_observed_data_tables.py b/pipelines/hubverse_create_observed_data_tables.py
similarity index 98%
rename from pipelines/create_observed_data_tables.py
rename to pipelines/hubverse_create_observed_data_tables.py
index 16ad780c..660e613f 100644
--- a/pipelines/create_observed_data_tables.py
+++ b/pipelines/hubverse_create_observed_data_tables.py
@@ -5,7 +5,10 @@
 
 import epiweeks
 import polars as pl
-from prep_data import aggregate_facility_level_nssp_to_state, get_state_pop_df
+from prep_data import (
+    aggregate_facility_level_nssp_to_state,
+    get_state_pop_df,
+)
 
 
 def save_observed_data_tables(
diff --git a/pipelines/create_hubverse_table.R b/pipelines/hubverse_create_table.R
similarity index 100%
rename from pipelines/create_hubverse_table.R
rename to pipelines/hubverse_create_table.R
diff --git a/pipelines/pull_state_timeseries.py b/pipelines/hubverse_make_obs_time_series_for_viz.py
similarity index 100%
rename from pipelines/pull_state_timeseries.py
rename to pipelines/hubverse_make_obs_time_series_for_viz.py
diff --git a/pipelines/score_hubverse.R b/pipelines/hubverse_score.R
similarity index 100%
rename from pipelines/score_hubverse.R
rename to pipelines/hubverse_score.R
diff --git a/pipelines/postprocess_state_forecast.R b/pipelines/plot_and_save_state_forecast.R
similarity index 100%
rename from pipelines/postprocess_state_forecast.R
rename to pipelines/plot_and_save_state_forecast.R
diff --git a/pipelines/plot_parameters.R b/pipelines/plot_parameters.R
deleted file mode 100644
index 5219bacf..00000000
--- a/pipelines/plot_parameters.R
+++ /dev/null
@@ -1,31 +0,0 @@
-library(dplyr)
-library(ggplot2)
-library(tidybayes)
-library(ggdist)
-library(tibble)
-
-pathogen <- "influenza"
-job_path <- ""
-state <- "US"
-
-fit <- arrow::read_parquet(fs::path(job_path,
-  "model_runs",
-  state,
-  "mcmc_tidy",
-  "pyrenew_posterior",
-  ext = "parquet"
-)) |>
-  tibble()
-
-
-inf_feedback <- fit |>
-  spread_draws(inf_feedback_raw, p_ed_visit_mean) |>
-  ggplot(aes(
-    x = inf_feedback_raw,
-    y = p_ed_visit_mean,
-    group = .draw
-  )) +
-  geom_point() +
-  scale_x_continuous(transform = "log10") +
-  scale_y_continuous(transform = "logit") +
-  theme_minimal()
diff --git a/pipelines/save_eval_data.py b/pipelines/prep_eval_data.py
similarity index 94%
rename from pipelines/save_eval_data.py
rename to pipelines/prep_eval_data.py
index 6a85389b..eddec0f5 100644
--- a/pipelines/save_eval_data.py
+++ b/pipelines/prep_eval_data.py
@@ -3,7 +3,10 @@
 from pathlib import Path
 
 import polars as pl
-from prep_data import get_state_pop_df, process_state_level_data
+from prep_data import (
+    get_state_pop_df,
+    process_state_level_data,
+)
 
 
 def save_eval_data(
diff --git a/pipelines/postprocess_scoring.R b/pipelines/summarize_visualize_scores.R
similarity index 100%
rename from pipelines/postprocess_scoring.R
rename to pipelines/summarize_visualize_scores.R