diff --git a/.github/scripts/regenerate-readme.sh b/.github/scripts/regenerate-readme.sh
index ccfd334..4c56d5c 100755
--- a/.github/scripts/regenerate-readme.sh
+++ b/.github/scripts/regenerate-readme.sh
@@ -20,12 +20,11 @@
 #   Components that share the same display `name` are rendered as a single
 #   README row regardless of whether they come from components.d/ (synced)
 #   or manual-components.yml (manually-staged). When both sources contribute
-#   to the same name, catalog cells are concatenated, skill counts are
-#   summed, and the synced row's metadata (description, source, version)
-#   wins over the manual row's em-dash defaults. Enables one logical
-#   product to span multiple yml entries (e.g., the Physical AI product
-#   pulling some skills from a public sub-repo and keeping the rest in the
-#   internal-staged manual list).
+#   to the same name, skill counts are summed and the synced row's metadata
+#   (description, source, version) wins over the manual row's em-dash
+#   defaults. Enables one logical product to span multiple yml entries
+#   (e.g., the Physical AI product pulling some skills from a public
+#   sub-repo and keeping the rest in the internal-staged manual list).
 
 set -euo pipefail
 
@@ -69,7 +68,7 @@ done
 
 # Available Skills table — emit structured rows, aggregate by name, then format.
 # TSV columns (tab-separated):
-#   sort_key | name | description | skill_count | catalog_cell | source_cell | version_cell | is_manual
+#   sort_key | name | description | skill_count | source_cell | version_cell | is_manual
 SKILLS_ROWS=/tmp/skills-rows.tsv
 truncate -s 0 "$SKILLS_ROWS"
 
@@ -80,7 +79,6 @@ for i in $kept_indices; do
   ref=$(yq -r ".components[$i].ref // \"main\"" "$CONFIG")
   primary_path=$(yq -r ".components[$i].skills[0].path" "$CONFIG")
   primary_path=${primary_path%/}
-  primary_catalog=$(yq -r ".components[$i].skills[0].catalog_dir" "$CONFIG")
 
   skill_count=$(component_skill_count "$i")
 
@@ -93,12 +91,11 @@ for i in $kept_indices; do
     fi
   fi
 
-  catalog_cell="[\`skills/${primary_catalog}/\`](skills/${primary_catalog})"
   source_cell="[Source](https://github.com/${repo}/tree/${ref}/${primary_path})"
 
   sort_key=$(echo "$name" | tr 'A-Z' 'a-z')
-  printf '%s\t%s\t%s\t%d\t%s\t%s\t%s\t%d\n' \
-    "$sort_key" "$name" "$description" "$skill_count" "$catalog_cell" "$source_cell" "$version_cell" 0 \
+  printf '%s\t%s\t%s\t%d\t%s\t%s\t%d\n' \
+    "$sort_key" "$name" "$description" "$skill_count" "$source_cell" "$version_cell" 0 \
     >> "$SKILLS_ROWS"
 done
 
@@ -114,49 +111,44 @@ if [ -f "$MANUAL_CONFIG" ]; then
     description=$(yq -r ".components[$i].description" "$MANUAL_CONFIG" | tr -d '\n' | sed 's/  */ /g; s/^ //; s/ $//')
 
     dir_count=$(yq -r ".components[$i].catalog_dirs | length" "$MANUAL_CONFIG")
-    catalog_cell=""
     skill_count=0
     for j in $(seq 0 $((dir_count - 1))); do
       d=$(yq -r ".components[$i].catalog_dirs[$j]" "$MANUAL_CONFIG")
-      catalog_cell="${catalog_cell}[\`${d}/\`](skills/${d}) · "
       if [ -d "skills/$d" ]; then
         cnt=$(find "skills/$d" -name SKILL.md -type f 2>/dev/null | wc -l | tr -d ' ')
         skill_count=$((skill_count + cnt))
       fi
     done
-    catalog_cell=${catalog_cell% · }
 
     sort_key=$(echo "$name" | tr 'A-Z' 'a-z')
-    printf '%s\t%s\t%s\t%d\t%s\t%s\t%s\t%d\n' \
-      "$sort_key" "$name" "$description" "$skill_count" "$catalog_cell" "—" "—" 1 \
+    printf '%s\t%s\t%s\t%d\t%s\t%s\t%d\n' \
+      "$sort_key" "$name" "$description" "$skill_count" "—" "—" 1 \
       >> "$SKILLS_ROWS"
   done
 fi
 
-# Aggregation pass: group rows by sort_key (lowercase name), merge their
-# catalog cells, sum their skill counts, and prefer the synced row's
-# description / source / version cells (the manual ones default to em dash).
+# Aggregation pass: group rows by sort_key (lowercase name), sum their skill
+# counts, and prefer the synced row's description / source / version cells
+# (the manual ones default to em dash).
 {
-  echo "| Product | Description | Skills | Catalog | Source | Version |"
-  echo "|---------|-------------|:------:|---------|--------|---------|"
+  echo "| Product | Description | Skills | Source | Version |"
+  echo "|---------|-------------|:------:|--------|---------|"
   sort -t$'\t' -k1,1 "$SKILLS_ROWS" | awk -F'\t' '
     {
       sk = $1; name = $2; desc = $3; cnt = $4 + 0
-      cat = $5; src = $6; ver = $7; man = $8 + 0
+      src = $5; ver = $6; man = $7 + 0
       if (!(sk in seen)) {
         seen[sk] = 1
         order[++n] = sk
         s_name[sk] = name
         s_desc[sk] = desc
         s_count[sk] = cnt
-        s_cat[sk] = cat
         s_src[sk] = src
         s_ver[sk] = ver
         s_man[sk] = man
       } else {
-        # Sum skill count, concatenate catalog cells (synced first by sort order).
+        # Sum skill count across all entries that share this name.
         s_count[sk] += cnt
-        s_cat[sk] = s_cat[sk] " · " cat
         # Prefer non-manual entry for display name, description, source, version.
         if (man == 0 && s_man[sk] == 1) {
           s_name[sk] = name
@@ -170,8 +162,8 @@ fi
     END {
       for (i = 1; i <= n; i++) {
         sk = order[i]
-        printf "| **%s** | %s | %d | %s | %s | %s |\n", \
-          s_name[sk], s_desc[sk], s_count[sk], s_cat[sk], s_src[sk], s_ver[sk]
+        printf "| **%s** | %s | %d | %s | %s |\n", \
+          s_name[sk], s_desc[sk], s_count[sk], s_src[sk], s_ver[sk]
       }
     }
   '
@@ -222,9 +214,9 @@ if [ -f "$MANUAL_CONFIG" ]; then
 fi
 
 # Aggregation: prefer the synced row's link cells when both synced + manual
-# rows share the same name. The link cells aren't combined (unlike catalog
-# cells in the skills table) because there's one logical issues / discussions /
-# contributing / security link per product, not per skill.
+# rows share the same name. The link cells aren't combined because there's
+# one logical issues / discussions / contributing / security link per
+# product, not per skill.
 {
   echo "| Product | Issues | Discussions | Contributing | Security |"
   echo "|---------|--------|-------------|--------------|----------|"
diff --git a/README.md b/README.md
index f0d3843..9b68801 100644
--- a/README.md
+++ b/README.md
@@ -95,34 +95,34 @@ For non-interactive installs, global installs, agent-specific installs, updates,
 ## Skill Catalog
 
 <!-- skills-table-start -->
-| Product | Description | Skills | Catalog | Source | Version |
-|---------|-------------|:------:|---------|--------|---------|
-| **AIQ** | NVIDIA AI-Q Blueprint - deploy local AI-Q services and run shallow or deep research workflows as agent skills. | 2 | [`skills/aiq-research/`](skills/aiq-research) | [Source](https://github.com/NVIDIA-AI-Blueprints/aiq/tree/develop/skills/aiq-research) | [`9f573a2`](https://github.com/NVIDIA-AI-Blueprints/aiq/commit/9f573a258f6423c2a663e6acbb9e0d86b072409f) · 2026-05-29 |
-| **CUDA-Q** | CUDA Quantum — onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications. | 1 | [`skills/cudaq-guide/`](skills/cudaq-guide) | [Source](https://github.com/NVIDIA/cuda-quantum/tree/main/skills/cudaq-guide) | [`5a3f444`](https://github.com/NVIDIA/cuda-quantum/commit/5a3f44464c3ebfc8af21402b7c1a5985c1db5015) · 2026-05-31 |
-| **cuDF** | Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads. | 1 | [`skills/accelerated-computing-cudf/`](skills/accelerated-computing-cudf) | [Source](https://github.com/rapidsai/cudf/tree/main/skills/accelerated-computing-cudf) | [`ebb9da4`](https://github.com/rapidsai/cudf/commit/ebb9da4fe7a5d414b2bf6788bb7c4ff98676e501) · 2026-05-30 |
-| **cuOpt** | GPU-accelerated optimization — vehicle routing, linear programming, quadratic programming, installation, server deployment, and developer tools. | 12 | [`skills/cuopt-developer/`](skills/cuopt-developer) | [Source](https://github.com/NVIDIA/cuopt/tree/main/skills/cuopt-developer) | [`ffbe51a`](https://github.com/NVIDIA/cuopt/commit/ffbe51a3921fa36362ca0ebec9eece597a87b9d9) · 2026-05-30 |
-| **cuPyNumeric** | NumPy and SciPy on multi-node multi-GPU systems — skills to help with installing cuPyNumeric, migrating existing NumPy code, and doing parallel I/O | 4 | [`skills/cupynumeric-hdf5/`](skills/cupynumeric-hdf5) | [Source](https://github.com/nv-legate/cupynumeric/tree/main/skills/cupynumeric-hdf5) | [`e95f556`](https://github.com/nv-legate/cupynumeric/commit/e95f55666e4b3fecc7ded25ef3eb30aa7e9f5a6f) · 2026-05-30 |
-| **DALI** | GPU-accelerated data loading and processing with NVIDIA DALI. | 1 | [`skills/dali-dynamic-mode/`](skills/dali-dynamic-mode) | [Source](https://github.com/NVIDIA/DALI/tree/main/skills/dali-dynamic-mode) | [`b1a2cd9`](https://github.com/NVIDIA/DALI/commit/b1a2cd9fa66da8f4d07a2e71c12f5f8e8a0d0872) · 2026-05-29 |
-| **DeepStream** | Agentic skills for guided DeepStream development. | 2 | [`skills/deepstream-dev/`](skills/deepstream-dev) | [Source](https://github.com/NVIDIA-AI-IOT/DeepStream_Coding_Agent/tree/main/skills/deepstream-dev) | [`3daf16a`](https://github.com/NVIDIA-AI-IOT/DeepStream_Coding_Agent/commit/3daf16ae5168dd685608bb929fb4f5513f308045) · 2026-05-28 |
-| **Dynamo** | NVIDIA Dynamo deployment bring-up on Kubernetes — pick and deploy recipes, start router modes, validate disagg NIXL/UCX/NCCL interconnect, and triage day-2 failures. | 4 | [`skills/dynamo-interconnect-check/`](skills/dynamo-interconnect-check) | [Source](https://github.com/ai-dynamo/dynamo/tree/main/skills/dynamo-interconnect-check) | [`8ea44f3`](https://github.com/ai-dynamo/dynamo/commit/8ea44f3ab09dbe28a18fbc7d79bf562d36af2c8b) · 2026-05-31 |
-| **Earth2Studio** | Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows. | 4 | [`skills/earth2studio-data-fetch/`](skills/earth2studio-data-fetch) | [Source](https://github.com/NVIDIA/earth2studio/tree/main/skills/earth2studio-data-fetch) | [`6b6c666`](https://github.com/NVIDIA/earth2studio/commit/6b6c666405e1d453d34dce32ea33d7eed3734a99) · 2026-05-29 |
-| **Holoscan SDK** | Install and set up the Holoscan SDK on any platform (container, Debian, Python, Conda, or source). | 6 | [`skills/holoscan-install-debian/`](skills/holoscan-install-debian) | [Source](https://github.com/nvidia-holoscan/holoscan-sdk/tree/main/skills/holoscan-install-debian) | [`e3a702d`](https://github.com/nvidia-holoscan/holoscan-sdk/commit/e3a702d29ce0f51fe53108cc3f010f96b6f7384e) · 2026-05-31 |
-| **Medical AI Skills** | Agent-ready medical AI skills built on MONAI for DICOM handling, NVIDIA-hosted medical imaging model workflows, segmentation, synthesis, and evidence-oriented evaluation. | 12 | [`skills/dicom-metadata-extract/`](skills/dicom-metadata-extract) | [Source](https://github.com/NVIDIA-Medtech/medical-AI-skills/tree/dev/skills/dicom-metadata-extract) | [`6d88654`](https://github.com/NVIDIA-Medtech/medical-AI-skills/commit/6d886544853ad6a92e26a600be56d3e74d4d144b) · 2026-05-31 |
-| **Megatron-Core** | Large-scale distributed training — model parallelism, pipeline parallelism, and mixed precision. | 5 | [`skills/mcore-create-issue/`](skills/mcore-create-issue) | [Source](https://github.com/NVIDIA/Megatron-LM/tree/main/skills/mcore-create-issue) | [`378d81f`](https://github.com/NVIDIA/Megatron-LM/commit/378d81fbd32a3faa90726d0e59344f7893ce4b47) · 2026-05-30 |
-| **NeMo AutoModel** | NeMo AutoModel - PyTorch-native distributed training for LLMs/VLMs with Hugging Face support, recipes, launchers, and validation workflows. | 4 | [`skills/nemo-automodel-distributed-training/`](skills/nemo-automodel-distributed-training) | [Source](https://github.com/NVIDIA-NeMo/Automodel/tree/main/skills/nemo-automodel-distributed-training) | [`0d1b8ce`](https://github.com/NVIDIA-NeMo/Automodel/commit/0d1b8ce9ba32eb02709ca1916400ae648c7f8dc7) · 2026-05-31 |
-| **NeMo MBridge** | NeMo MBridge - PyTorch-native bridge between Hugging Face and Megatron-Core for checkpoint conversion, training recipes, and NVIDIA GPU performance workflows. | 20 | [`skills/nemo-mbridge-mlm-bridge-training/`](skills/nemo-mbridge-mlm-bridge-training) | [Source](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/skills/nemo-mbridge-mlm-bridge-training) | [`4d3e031`](https://github.com/NVIDIA-NeMo/Megatron-Bridge/commit/4d3e0315028b5dd591e6768c1c80f35f19002634) · 2026-05-31 |
-| **NeMo Platform** | NeMo Platform brings NVIDIA NeMo libraries together under one CLI, Python SDK, and web UI | 2 | [`skills/nemo-evaluator-plugin/`](skills/nemo-evaluator-plugin) | [Source](https://github.com/NVIDIA-NeMo/nemo-platform/tree/main/skills/nemo-evaluator-plugin) | [`c5069f4`](https://github.com/NVIDIA-NeMo/nemo-platform/commit/c5069f4a0e2cc720c8c3aac23e9112a062b4426d) · 2026-05-30 |
-| **NeMo Retriever** | NeMo Retriever - deploy NeMo Retriever Library locally, extract information from corpus of data, and answer questions against the corpus. | 1 | [`skills/nemo-retriever/`](skills/nemo-retriever) | [Source](https://github.com/NVIDIA/NeMo-Retriever/tree/main/skills/nemo-retriever) | [`1e594ac`](https://github.com/NVIDIA/NeMo-Retriever/commit/1e594ac5dbccec1d741dceec3dfa1e2cf18819bd) · 2026-05-31 |
-| **NeMo-RL** | RLHF training on Ray — GRPO, DPO, and SFT for LLMs and VLMs with FSDP2 and Megatron-Core. | 5 | [`skills/NeMo-RL/`](skills/NeMo-RL) | [Source](https://github.com/NVIDIA-NeMo/RL/tree/main/skills) | [`48b2cd2`](https://github.com/NVIDIA-NeMo/RL/commit/48b2cd2ac79c3cef4dbf9b4458a04d38eebc1d16) · 2026-05-30 |
-| **NemoClaw** | Secure agent sandboxing — run OpenClaw inside NVIDIA OpenShell with managed inference, policy management, remote deployment, sandbox monitoring. | 10 | [`skills/nemoclaw-user-agent-skills/`](skills/nemoclaw-user-agent-skills) | [Source](https://github.com/NVIDIA/NemoClaw/tree/main/skills/nemoclaw-user-agent-skills) | [`9641ce0`](https://github.com/NVIDIA/NemoClaw/commit/9641ce053a436670b61575ecc9a40b16d017ae3e) · 2026-05-31 |
-| **Nemotron** | Author end-to-end model development, customization, evaluation, and deployment pipelines using the NVIDIA AI stack. | 2 | [`skills/nemotron-customize/`](skills/nemotron-customize) | [Source](https://github.com/NVIDIA-NeMo/Nemotron/tree/main/skills/nemotron-customize) | [`306b2f1`](https://github.com/NVIDIA-NeMo/Nemotron/commit/306b2f1217e000b5972155c1f2b1ba6660c994bd) · 2026-05-29 |
-| **NVIDIA Digital Health Examples** | Agent skills for the clinical ASR evaluation flywheel — term curation, synthetic clinical-speech benchmark generation, KER (Keyword Error Rate) scoring, and fine-tune guidance. | 4 | [`skills/digital-health-clinical-asr-setup/`](skills/digital-health-clinical-asr-setup) | [Source](https://github.com/NVIDIA/digital-health-examples/tree/main/skills/digital-health-clinical-asr-setup) | [`404c5ca`](https://github.com/NVIDIA/digital-health-examples/commit/404c5ca905d73d2df46fcfe2490aace21659ecff) · 2026-05-28 |
-| **Physical AI** | Generate labeled synthetic training data for physical-AI inspection and perception models. | 7 | [`skills/physical-ai-defect-image-generation/`](skills/physical-ai-defect-image-generation) · [`omniverse-cad-to-simready/`](skills/omniverse-cad-to-simready) · [`omniverse-realtime-viewer/`](skills/omniverse-realtime-viewer) · [`omniverse-usd-performance-tuning/`](skills/omniverse-usd-performance-tuning) · [`physical-ai-infrastructure-setup-and-resilient-scaling/`](skills/physical-ai-infrastructure-setup-and-resilient-scaling) · [`physical-ai-neural-reconstruction/`](skills/physical-ai-neural-reconstruction) | [Source](https://github.com/NVIDIA/physical-ai-data-factory/tree/main/skills/physical-ai-defect-image-generation) | [`f858927`](https://github.com/NVIDIA/physical-ai-data-factory/commit/f858927dd4feaa2f060b7e027e1d29eaab5391fd) · 2026-05-31 |
-| **PhysicsNeMo** | NVIDIA PhysicsNeMo - Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods. | 1 | [`skills/physicsnemo-discover/`](skills/physicsnemo-discover) | [Source](https://github.com/NVIDIA/physicsnemo/tree/main/skills/physicsnemo-discover) | [`8e46db6`](https://github.com/NVIDIA/physicsnemo/commit/8e46db617f552e47bfac3f8e84e5665dd9a0780e) · 2026-05-29 |
-| **RAG Blueprint** | RAG pipeline — deploy, configure, troubleshoot, and manage retrieval augmented generation with Docker Compose or Helm. | 3 | [`skills/rag-blueprint/`](skills/rag-blueprint) | [Source](https://github.com/NVIDIA-AI-Blueprints/rag/tree/develop/skills/rag-blueprint) | [`bed5165`](https://github.com/NVIDIA-AI-Blueprints/rag/commit/bed5165f4a9d7ba11b46a765fc36bd46e911babd) · 2026-05-30 |
-| **Skill Card Generator** | Reads an agent skill's source files and produces a skill card plus a review table. Use when a skill directory exists and a governance card needs to be generated or updated. | 1 | [`skills/skill-card-generator/`](skills/skill-card-generator) | [Source](https://github.com/NVIDIA/Trustworthy-AI/tree/main/skills/skill-card-generator) | [`09b0d6b`](https://github.com/NVIDIA/Trustworthy-AI/commit/09b0d6b03ee587cc8672ad3133a4eb84aeadbd2a) · 2026-05-28 |
-| **TileGym** | Tile-based GPU programming — adding new kernels, cross-framework conversion, and performance optimization. | 1 | [`skills/tilegym-adding-cutile-kernel/`](skills/tilegym-adding-cutile-kernel) | [Source](https://github.com/NVIDIA/TileGym/tree/main/skills/tilegym-adding-cutile-kernel) | [`2bf003b`](https://github.com/NVIDIA/TileGym/commit/2bf003b049c04f45b222423d328cb51ba5cf2670) · 2026-05-29 |
-| **Video Search and Summarization** | VSS Blueprint — deploy profiles, search and summarize video, generate analysis reports, manage alerts and incidents, query VIOS sensors, and use the RTVI VLM microservice. | 15 | [`skills/video-search-and-summarization/`](skills/video-search-and-summarization) | [Source](https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills) | [`7f7b2c3`](https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/commit/7f7b2c3f84553baac4d68c23624c7d9bfbb8d7e7) · 2026-05-31 |
+| Product | Description | Skills | Source | Version |
+|---------|-------------|:------:|--------|---------|
+| **AIQ** | NVIDIA AI-Q Blueprint - deploy local AI-Q services and run shallow or deep research workflows as agent skills. | 2 | [Source](https://github.com/NVIDIA-AI-Blueprints/aiq/tree/develop/skills/aiq-research) | — |
+| **CUDA-Q** | CUDA Quantum — onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications. | 1 | [Source](https://github.com/NVIDIA/cuda-quantum/tree/main/skills/cudaq-guide) | — |
+| **cuDF** | Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads. | 1 | [Source](https://github.com/rapidsai/cudf/tree/main/skills/accelerated-computing-cudf) | — |
+| **cuOpt** | GPU-accelerated optimization — vehicle routing, linear programming, quadratic programming, installation, server deployment, and developer tools. | 12 | [Source](https://github.com/NVIDIA/cuopt/tree/main/skills/cuopt-developer) | — |
+| **cuPyNumeric** | NumPy and SciPy on multi-node multi-GPU systems — skills to help with installing cuPyNumeric, migrating existing NumPy code, and doing parallel I/O | 4 | [Source](https://github.com/nv-legate/cupynumeric/tree/main/skills/cupynumeric-hdf5) | — |
+| **DALI** | GPU-accelerated data loading and processing with NVIDIA DALI. | 1 | [Source](https://github.com/NVIDIA/DALI/tree/main/skills/dali-dynamic-mode) | — |
+| **DeepStream** | Agentic skills for guided DeepStream development. | 2 | [Source](https://github.com/NVIDIA-AI-IOT/DeepStream_Coding_Agent/tree/main/skills/deepstream-dev) | — |
+| **Dynamo** | NVIDIA Dynamo deployment bring-up on Kubernetes — pick and deploy recipes, start router modes, validate disagg NIXL/UCX/NCCL interconnect, and triage day-2 failures. | 4 | [Source](https://github.com/ai-dynamo/dynamo/tree/main/skills/dynamo-interconnect-check) | — |
+| **Earth2Studio** | Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows. | 4 | [Source](https://github.com/NVIDIA/earth2studio/tree/main/skills/earth2studio-data-fetch) | — |
+| **Holoscan SDK** | Install and set up the Holoscan SDK on any platform (container, Debian, Python, Conda, or source). | 6 | [Source](https://github.com/nvidia-holoscan/holoscan-sdk/tree/main/skills/holoscan-install-debian) | — |
+| **Medical AI Skills** | Agent-ready medical AI skills built on MONAI for DICOM handling, NVIDIA-hosted medical imaging model workflows, segmentation, synthesis, and evidence-oriented evaluation. | 12 | [Source](https://github.com/NVIDIA-Medtech/medical-AI-skills/tree/dev/skills/dicom-metadata-extract) | — |
+| **Megatron-Core** | Large-scale distributed training — model parallelism, pipeline parallelism, and mixed precision. | 5 | [Source](https://github.com/NVIDIA/Megatron-LM/tree/main/skills/mcore-create-issue) | — |
+| **NeMo AutoModel** | NeMo AutoModel - PyTorch-native distributed training for LLMs/VLMs with Hugging Face support, recipes, launchers, and validation workflows. | 4 | [Source](https://github.com/NVIDIA-NeMo/Automodel/tree/main/skills/nemo-automodel-distributed-training) | — |
+| **NeMo MBridge** | NeMo MBridge - PyTorch-native bridge between Hugging Face and Megatron-Core for checkpoint conversion, training recipes, and NVIDIA GPU performance workflows. | 20 | [Source](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/skills/nemo-mbridge-mlm-bridge-training) | — |
+| **NeMo Platform** | NeMo Platform brings NVIDIA NeMo libraries together under one CLI, Python SDK, and web UI | 2 | [Source](https://github.com/NVIDIA-NeMo/nemo-platform/tree/main/skills/nemo-evaluator-plugin) | — |
+| **NeMo Retriever** | NeMo Retriever - deploy NeMo Retriever Library locally, extract information from corpus of data, and answer questions against the corpus. | 1 | [Source](https://github.com/NVIDIA/NeMo-Retriever/tree/main/skills/nemo-retriever) | — |
+| **NeMo-RL** | RLHF training on Ray — GRPO, DPO, and SFT for LLMs and VLMs with FSDP2 and Megatron-Core. | 5 | [Source](https://github.com/NVIDIA-NeMo/RL/tree/main/skills) | — |
+| **NemoClaw** | Secure agent sandboxing — run OpenClaw inside NVIDIA OpenShell with managed inference, policy management, remote deployment, sandbox monitoring. | 10 | [Source](https://github.com/NVIDIA/NemoClaw/tree/main/skills/nemoclaw-user-agent-skills) | — |
+| **Nemotron** | Author end-to-end model development, customization, evaluation, and deployment pipelines using the NVIDIA AI stack. | 2 | [Source](https://github.com/NVIDIA-NeMo/Nemotron/tree/main/skills/nemotron-customize) | — |
+| **NVIDIA Digital Health Examples** | Agent skills for the clinical ASR evaluation flywheel — term curation, synthetic clinical-speech benchmark generation, KER (Keyword Error Rate) scoring, and fine-tune guidance. | 4 | [Source](https://github.com/NVIDIA/digital-health-examples/tree/main/skills/digital-health-clinical-asr-setup) | — |
+| **Physical AI** | Generate labeled synthetic training data for physical-AI inspection and perception models. | 7 | [Source](https://github.com/NVIDIA/physical-ai-data-factory/tree/main/skills/physical-ai-defect-image-generation) | — |
+| **PhysicsNeMo** | NVIDIA PhysicsNeMo - Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods. | 1 | [Source](https://github.com/NVIDIA/physicsnemo/tree/main/skills/physicsnemo-discover) | — |
+| **RAG Blueprint** | RAG pipeline — deploy, configure, troubleshoot, and manage retrieval augmented generation with Docker Compose or Helm. | 3 | [Source](https://github.com/NVIDIA-AI-Blueprints/rag/tree/develop/skills/rag-blueprint) | — |
+| **Skill Card Generator** | Reads an agent skill's source files and produces a skill card plus a review table. Use when a skill directory exists and a governance card needs to be generated or updated. | 1 | [Source](https://github.com/NVIDIA/Trustworthy-AI/tree/main/skills/skill-card-generator) | — |
+| **TileGym** | Tile-based GPU programming — adding new kernels, cross-framework conversion, and performance optimization. | 1 | [Source](https://github.com/NVIDIA/TileGym/tree/main/skills/tilegym-adding-cutile-kernel) | — |
+| **Video Search and Summarization** | VSS Blueprint — deploy profiles, search and summarize video, generate analysis reports, manage alerts and incidents, query VIOS sensors, and use the RTVI VLM microservice. | 15 | [Source](https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills) | — |
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