diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index eccc6f2e..b1dce9c3 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -10,7 +10,7 @@ jobs:
       with:
         submodules: recursive
     - name: Formatting
-      uses: github/super-linter@v4
+      uses: github/super-linter@v5
       env:
         VALIDATE_ALL_CODEBASE: false
         DEFAULT_BRANCH: main
@@ -142,7 +142,7 @@ jobs:
       - dry-run
     steps:
       - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: 3.11
       - run: pip install bgcflow_wrapper
diff --git a/workflow/envs/bgcflow_notes.yaml b/workflow/envs/bgcflow_notes.yaml
index b63b10b5..c1961d7b 100644
--- a/workflow/envs/bgcflow_notes.yaml
+++ b/workflow/envs/bgcflow_notes.yaml
@@ -19,7 +19,7 @@ dependencies:
   - xlrd >= 1.0.0
   - altair
   - itables
-  - scikit-learn
+  - scikit-learn==1.4.0
   - pyarrow
   - pygraphviz
   - pip
diff --git a/workflow/notebook/fastani.py.ipynb b/workflow/notebook/fastani.py.ipynb
index 0705abd6..650702f8 100644
--- a/workflow/notebook/fastani.py.ipynb
+++ b/workflow/notebook/fastani.py.ipynb
@@ -202,7 +202,7 @@
     "else:\n",
     "    top_clusters = 12\n",
     "\n",
-    "Agg_hc = AgglomerativeClustering(n_clusters = n_clusters, affinity = 'euclidean', linkage = 'ward')\n",
+    "Agg_hc = AgglomerativeClustering(n_clusters = n_clusters, metric = 'euclidean', linkage = 'ward')\n",
     "y_hc = Agg_hc.fit_predict(df_fastani_corr)\n",
     "color_set3 = ['#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3','#fdb462','#b3de69','#fccde5','#d9d9d9','#bc80bd','#ccebc5','#ffed6f']\n",
     "\n",
diff --git a/workflow/notebook/mash.py.ipynb b/workflow/notebook/mash.py.ipynb
index 914211b5..714e9b65 100644
--- a/workflow/notebook/mash.py.ipynb
+++ b/workflow/notebook/mash.py.ipynb
@@ -202,7 +202,7 @@
     "else:\n",
     "    top_clusters = 12\n",
     "\n",
-    "Agg_hc = AgglomerativeClustering(n_clusters = n_clusters, affinity = 'euclidean', linkage = 'ward')\n",
+    "Agg_hc = AgglomerativeClustering(n_clusters = n_clusters, metric = 'euclidean', linkage = 'ward')\n",
     "y_hc = Agg_hc.fit_predict(df_mash_corr)\n",
     "color_set3 = ['#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3','#fdb462','#b3de69','#fccde5','#d9d9d9','#bc80bd','#ccebc5','#ffed6f']\n",
     "\n",
diff --git a/workflow/rules/eggnog.smk b/workflow/rules/eggnog.smk
index 460445ab..00518b29 100644
--- a/workflow/rules/eggnog.smk
+++ b/workflow/rules/eggnog.smk
@@ -20,13 +20,15 @@ rule eggnog:
         dmnd="resources/eggnog_db/bacteria.dmnd",
     output:
         eggnog_dir=directory("data/interim/eggnog/{strains}/"),
+        tempdir=temp(directory("data/interim/eggnog/tmp/{strains}"))
     conda:
         "../envs/eggnog.yaml"
-    threads: 4
+    threads: 8
     log:
         "logs/eggnog/eggnog/eggnog-{strains}.log",
     shell:
         """
         mkdir -p {output.eggnog_dir}
-        emapper.py -i {input.faa} --decorate_gff "yes" --excel --cpu {threads} -o {wildcards.strains} --output_dir {output.eggnog_dir} --data_dir {input.eggnog_db} &>> {log}
+        mkdir -p {output.tempdir}
+        emapper.py -i {input.faa} --decorate_gff "yes" --excel --cpu {threads} -o {wildcards.strains} --output_dir {output.eggnog_dir} --data_dir {input.eggnog_db} --temp_dir {output.tempdir} &>> {log}
         """