diff --git a/.github/scripts/regenerate-readme.sh b/.github/scripts/regenerate-readme.sh
index a9b12f8e..f0127451 100755
--- a/.github/scripts/regenerate-readme.sh
+++ b/.github/scripts/regenerate-readme.sh
@@ -68,41 +68,39 @@ done
 
 # Available Skills table — emit structured rows, aggregate by name, then format.
 # TSV columns (tab-separated):
-#   sort_key | name | description | skill_count | source_cell | version_cell | is_manual
+#   sort_key | name | description | skills_cell | is_manual
+# `skills_cell` is a backtick-quoted, comma-separated list of skill catalog
+# directories, each rendered as a markdown link to skills/<dir>/. Replaces
+# the prior Source + Version columns; Source links pointed at only the
+# primary skill (misleading for multi-skill products) and Version cells
+# rendered as em dashes whenever VERSIONS_FILE was unpopulated.
 SKILLS_ROWS=/tmp/skills-rows.tsv
 truncate -s 0 "$SKILLS_ROWS"
 
 for i in $kept_indices; do
   name=$(yq -r ".components[$i].name" "$CONFIG")
   description=$(yq -r ".components[$i].description" "$CONFIG" | tr -d '\n' | sed 's/  */ /g; s/^ //; s/ $//')
-  repo=$(yq -r ".components[$i].repo" "$CONFIG")
-  ref=$(yq -r ".components[$i].ref // \"main\"" "$CONFIG")
-  primary_path=$(yq -r ".components[$i].skills[0].path" "$CONFIG")
-  primary_path=${primary_path%/}
 
-  skill_count=$(component_skill_count "$i")
-
-  slug=$(echo "$name" | tr 'A-Z ' 'a-z-')
-  version_cell="—"
-  if [ -s "$VERSIONS_FILE" ]; then
-    if version_line=$(grep "^${slug}|" "$VERSIONS_FILE"); then
-      IFS='|' read -r _ short_sha full_sha date sha_repo <<< "$version_line"
-      version_cell="[\`${short_sha}\`](https://github.com/${sha_repo}/commit/${full_sha}) · ${date}"
+  # Build the comma-separated skills list: one link per catalog_dir that
+  # currently has at least one verified SKILL.md in the catalog.
+  skills_cell=""
+  while read -r catalog_dir; do
+    if [ -d "skills/$catalog_dir" ] && [ -n "$(find "skills/$catalog_dir" -name SKILL.md -type f 2>/dev/null | head -1)" ]; then
+      skills_cell="${skills_cell}[\`${catalog_dir}\`](skills/${catalog_dir}), "
     fi
-  fi
-
-  source_cell="[Source](https://github.com/${repo}/tree/${ref}/${primary_path})"
+  done < <(yq -r ".components[$i].skills[].catalog_dir" "$CONFIG")
+  skills_cell=${skills_cell%, }
 
   sort_key=$(echo "$name" | tr 'A-Z' 'a-z')
-  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 \
+  printf '%s\t%s\t%s\t%s\t%d\n' \
+    "$sort_key" "$name" "$description" "$skills_cell" 0 \
     >> "$SKILLS_ROWS"
 done
 
 # TEMPORARY — remove after Computex 2026. Append rows for manually-staged
-# products (no upstream sync); Source and Version cells render as em dashes.
-# These bypass the kept_indices verified-skills filter intentionally — they
-# live in this catalog only as a stopgap until their upstream goes public.
+# products (no upstream sync). These bypass the kept_indices verified-skills
+# filter intentionally — they live in this catalog only as a stopgap until
+# their upstream goes public.
 manual_count=0
 if [ -f "$MANUAL_CONFIG" ]; then
   manual_count=$(yq '.components | length' "$MANUAL_CONFIG")
@@ -111,50 +109,50 @@ if [ -f "$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")
-    skill_count=0
+    skills_cell=""
     for j in $(seq 0 $((dir_count - 1))); do
       d=$(yq -r ".components[$i].catalog_dirs[$j]" "$MANUAL_CONFIG")
       if [ -d "skills/$d" ]; then
-        cnt=$(find "skills/$d" -name SKILL.md -type f 2>/dev/null | wc -l | tr -d ' ')
-        skill_count=$((skill_count + cnt))
+        skills_cell="${skills_cell}[\`${d}\`](skills/${d}), "
       fi
     done
+    skills_cell=${skills_cell%, }
 
     sort_key=$(echo "$name" | tr 'A-Z' 'a-z')
-    printf '%s\t%s\t%s\t%d\t%s\t%s\t%d\n' \
-      "$sort_key" "$name" "$description" "$skill_count" "—" "—" 1 \
+    printf '%s\t%s\t%s\t%s\t%d\n' \
+      "$sort_key" "$name" "$description" "$skills_cell" 1 \
       >> "$SKILLS_ROWS"
   done
 fi
 
-# 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).
+# Aggregation pass: group rows by sort_key (lowercase name). Concatenate
+# skill links when synced + manual entries share the same product name
+# (e.g., Physical AI = 5 manual + 2 synced — one row, all 7 listed).
+# Prefer the synced row's description over the manual row's.
 {
-  echo "| Product | Description | Skills | Source | Version |"
-  echo "|---------|-------------|:------:|--------|---------|"
+  echo "| Product | Description | Skills |"
+  echo "|---------|-------------|--------|"
   sort -t$'\t' -k1,1 "$SKILLS_ROWS" | awk -F'\t' '
     {
-      sk = $1; name = $2; desc = $3; cnt = $4 + 0
-      src = $5; ver = $6; man = $7 + 0
+      sk = $1; name = $2; desc = $3; skills = $4; man = $5 + 0
       if (!(sk in seen)) {
         seen[sk] = 1
         order[++n] = sk
         s_name[sk] = name
         s_desc[sk] = desc
-        s_count[sk] = cnt
-        s_src[sk] = src
-        s_ver[sk] = ver
+        s_skills[sk] = skills
         s_man[sk] = man
       } else {
-        # Sum skill count across all entries that share this name.
-        s_count[sk] += cnt
-        # Prefer non-manual entry for display name, description, source, version.
+        # Merge skill lists across entries sharing this name.
+        if (s_skills[sk] == "" || s_skills[sk] == "—") {
+          s_skills[sk] = skills
+        } else if (skills != "" && skills != "—") {
+          s_skills[sk] = s_skills[sk] ", " skills
+        }
+        # Prefer non-manual entry for display name and description.
         if (man == 0 && s_man[sk] == 1) {
           s_name[sk] = name
           s_desc[sk] = desc
-          s_src[sk] = src
-          s_ver[sk] = ver
           s_man[sk] = 0
         }
       }
@@ -162,8 +160,8 @@ fi
     END {
       for (i = 1; i <= n; i++) {
         sk = order[i]
-        printf "| **%s** | %s | %d | %s | %s |\n", \
-          s_name[sk], s_desc[sk], s_count[sk], s_src[sk], s_ver[sk]
+        printf "| **%s** | %s | %s |\n", \
+          s_name[sk], s_desc[sk], s_skills[sk]
       }
     }
   '
diff --git a/README.md b/README.md
index 704a4002..59306595 100644
--- a/README.md
+++ b/README.md
@@ -95,35 +95,35 @@ For non-interactive installs, global installs, agent-specific installs, updates,
 ## Skill Catalog
 
 <!-- skills-table-start -->
-| 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) | — |
-| **Nemotron Speech** | Deploy and operate NVIDIA Nemotron Speech (Riva) NIMs — ASR, TTS, and NMT, cloud-hosted via build.nvidia.com or self-hosted on your own GPU. | 1 | [Source](https://github.com/nvidia-riva/Nemotron-speech-skills/tree/main/skills/nemotron-speech) | — |
-| **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/vss-ask-video) | — |
+| Product | Description | Skills |
+|---------|-------------|--------|
+| **AIQ** | NVIDIA AI-Q Blueprint - deploy local AI-Q services and run shallow or deep research workflows as agent skills. | [`aiq-research`](skills/aiq-research), [`aiq-deploy`](skills/aiq-deploy) |
+| **CUDA-Q** | CUDA Quantum — onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications. | [`cudaq-guide`](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. | [`accelerated-computing-cudf`](skills/accelerated-computing-cudf) |
+| **cuOpt** | GPU-accelerated optimization — vehicle routing, linear programming, quadratic programming, installation, server deployment, and developer tools. | [`cuopt-developer`](skills/cuopt-developer), [`cuopt-install`](skills/cuopt-install), [`cuopt-numerical-optimization-api-c`](skills/cuopt-numerical-optimization-api-c), [`cuopt-numerical-optimization-api-cli`](skills/cuopt-numerical-optimization-api-cli), [`cuopt-numerical-optimization-api-python`](skills/cuopt-numerical-optimization-api-python), [`cuopt-numerical-optimization-formulation`](skills/cuopt-numerical-optimization-formulation), [`cuopt-routing-api-python`](skills/cuopt-routing-api-python), [`cuopt-routing-formulation`](skills/cuopt-routing-formulation), [`cuopt-server-api-python`](skills/cuopt-server-api-python), [`cuopt-server-common`](skills/cuopt-server-common), [`cuopt-skill-evolution`](skills/cuopt-skill-evolution), [`cuopt-user-rules`](skills/cuopt-user-rules) |
+| **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 | [`cupynumeric-hdf5`](skills/cupynumeric-hdf5), [`cupynumeric-install`](skills/cupynumeric-install), [`cupynumeric-migration-readiness`](skills/cupynumeric-migration-readiness), [`cupynumeric-parallel-data-load`](skills/cupynumeric-parallel-data-load) |
+| **DALI** | GPU-accelerated data loading and processing with NVIDIA DALI. | [`dali-dynamic-mode`](skills/dali-dynamic-mode) |
+| **DeepStream** | Agentic skills for guided DeepStream development. | [`deepstream-dev`](skills/deepstream-dev), [`deepstream-import-vision-model`](skills/deepstream-import-vision-model) |
+| **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. | [`dynamo-interconnect-check`](skills/dynamo-interconnect-check), [`dynamo-recipe-runner`](skills/dynamo-recipe-runner), [`dynamo-router-starter`](skills/dynamo-router-starter), [`dynamo-troubleshoot`](skills/dynamo-troubleshoot) |
+| **Earth2Studio** | Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows. | [`earth2studio-data-fetch`](skills/earth2studio-data-fetch), [`earth2studio-deterministic-forecast`](skills/earth2studio-deterministic-forecast), [`earth2studio-discover`](skills/earth2studio-discover), [`earth2studio-install`](skills/earth2studio-install) |
+| **Holoscan SDK** | Install and set up the Holoscan SDK on any platform (container, Debian, Python, Conda, or source). | [`holoscan-install-debian`](skills/holoscan-install-debian), [`holoscan-install-source`](skills/holoscan-install-source), [`holoscan-install-wheel`](skills/holoscan-install-wheel), [`holoscan-install-conda`](skills/holoscan-install-conda), [`holoscan-install-container`](skills/holoscan-install-container), [`holoscan-setup`](skills/holoscan-setup) |
+| **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. | [`dicom-metadata-extract`](skills/dicom-metadata-extract), [`dicom-series-preflight`](skills/dicom-series-preflight), [`dicom-series-to-volume`](skills/dicom-series-to-volume), [`nv-generate-ct-rflow`](skills/nv-generate-ct-rflow), [`nv-generate-mr`](skills/nv-generate-mr), [`nv-generate-mr-brain`](skills/nv-generate-mr-brain), [`nv-generate-mr-brain-finetune`](skills/nv-generate-mr-brain-finetune), [`nv-generate-vae-finetune`](skills/nv-generate-vae-finetune), [`nv-reason-cxr`](skills/nv-reason-cxr), [`nv-segment-ct`](skills/nv-segment-ct), [`nv-segment-ct-finetune`](skills/nv-segment-ct-finetune), [`nv-segment-ctmr`](skills/nv-segment-ctmr) |
+| **Megatron-Core** | Large-scale distributed training — model parallelism, pipeline parallelism, and mixed precision. | [`mcore-create-issue`](skills/mcore-create-issue), [`mcore-linting-and-formatting`](skills/mcore-linting-and-formatting), [`mcore-run-on-slurm`](skills/mcore-run-on-slurm), [`mcore-split-pr`](skills/mcore-split-pr), [`mcore-testing`](skills/mcore-testing) |
+| **NeMo AutoModel** | NeMo AutoModel - PyTorch-native distributed training for LLMs/VLMs with Hugging Face support, recipes, launchers, and validation workflows. | [`nemo-automodel-distributed-training`](skills/nemo-automodel-distributed-training), [`nemo-automodel-launcher-config`](skills/nemo-automodel-launcher-config), [`nemo-automodel-model-onboarding`](skills/nemo-automodel-model-onboarding), [`nemo-automodel-recipe-development`](skills/nemo-automodel-recipe-development) |
+| **NeMo MBridge** | NeMo MBridge - PyTorch-native bridge between Hugging Face and Megatron-Core for checkpoint conversion, training recipes, and NVIDIA GPU performance workflows. | [`nemo-mbridge-mlm-bridge-training`](skills/nemo-mbridge-mlm-bridge-training), [`nemo-mbridge-multi-node-slurm`](skills/nemo-mbridge-multi-node-slurm), [`nemo-mbridge-perf-activation-recompute`](skills/nemo-mbridge-perf-activation-recompute), [`nemo-mbridge-perf-cpu-offloading`](skills/nemo-mbridge-perf-cpu-offloading), [`nemo-mbridge-perf-cuda-graphs`](skills/nemo-mbridge-perf-cuda-graphs), [`nemo-mbridge-perf-expert-parallel-overlap`](skills/nemo-mbridge-perf-expert-parallel-overlap), [`nemo-mbridge-perf-hierarchical-context-parallel`](skills/nemo-mbridge-perf-hierarchical-context-parallel), [`nemo-mbridge-perf-megatron-fsdp`](skills/nemo-mbridge-perf-megatron-fsdp), [`nemo-mbridge-perf-memory-tuning`](skills/nemo-mbridge-perf-memory-tuning), [`nemo-mbridge-perf-moe-comm-overlap`](skills/nemo-mbridge-perf-moe-comm-overlap), [`nemo-mbridge-perf-moe-dispatcher-selection`](skills/nemo-mbridge-perf-moe-dispatcher-selection), [`nemo-mbridge-perf-moe-hardware-configs`](skills/nemo-mbridge-perf-moe-hardware-configs), [`nemo-mbridge-perf-moe-long-context`](skills/nemo-mbridge-perf-moe-long-context), [`nemo-mbridge-perf-moe-optimization-workflow`](skills/nemo-mbridge-perf-moe-optimization-workflow), [`nemo-mbridge-perf-moe-vlm-training`](skills/nemo-mbridge-perf-moe-vlm-training), [`nemo-mbridge-perf-parallelism-strategies`](skills/nemo-mbridge-perf-parallelism-strategies), [`nemo-mbridge-perf-sequence-packing`](skills/nemo-mbridge-perf-sequence-packing), [`nemo-mbridge-perf-tp-dp-comm-overlap`](skills/nemo-mbridge-perf-tp-dp-comm-overlap), [`nemo-mbridge-recipe-recommender`](skills/nemo-mbridge-recipe-recommender), [`nemo-mbridge-resiliency`](skills/nemo-mbridge-resiliency) |
+| **NeMo Platform** | NeMo Platform brings NVIDIA NeMo libraries together under one CLI, Python SDK, and web UI | [`nemo-evaluator-plugin`](skills/nemo-evaluator-plugin), [`nemo-data-designer-plugin`](skills/nemo-data-designer-plugin) |
+| **NeMo Retriever** | NeMo Retriever - deploy NeMo Retriever Library locally, extract information from corpus of data, and answer questions against the corpus. | [`nemo-retriever`](skills/nemo-retriever) |
+| **NeMo-RL** | RLHF training on Ray — GRPO, DPO, and SFT for LLMs and VLMs with FSDP2 and Megatron-Core. | [`NeMo-RL`](skills/NeMo-RL) |
+| **NemoClaw** | Secure agent sandboxing — run OpenClaw inside NVIDIA OpenShell with managed inference, policy management, remote deployment, sandbox monitoring. | [`nemoclaw-user-agent-skills`](skills/nemoclaw-user-agent-skills), [`nemoclaw-user-configure-inference`](skills/nemoclaw-user-configure-inference), [`nemoclaw-user-configure-security`](skills/nemoclaw-user-configure-security), [`nemoclaw-user-deploy-remote`](skills/nemoclaw-user-deploy-remote), [`nemoclaw-user-get-started`](skills/nemoclaw-user-get-started), [`nemoclaw-user-manage-policy`](skills/nemoclaw-user-manage-policy), [`nemoclaw-user-manage-sandboxes`](skills/nemoclaw-user-manage-sandboxes), [`nemoclaw-user-monitor-sandbox`](skills/nemoclaw-user-monitor-sandbox), [`nemoclaw-user-overview`](skills/nemoclaw-user-overview), [`nemoclaw-user-reference`](skills/nemoclaw-user-reference) |
+| **Nemotron** | Author end-to-end model development, customization, evaluation, and deployment pipelines using the NVIDIA AI stack. | [`nemotron-customize`](skills/nemotron-customize), [`nemotron-retrieval-recipes`](skills/nemotron-retrieval-recipes) |
+| **Nemotron Speech** | Deploy and operate NVIDIA Nemotron Speech (Riva) NIMs — ASR, TTS, and NMT, cloud-hosted via build.nvidia.com or self-hosted on your own GPU. | [`nemotron-speech`](skills/nemotron-speech) |
+| **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. | [`digital-health-clinical-asr-setup`](skills/digital-health-clinical-asr-setup), [`digital-health-clinical-asr-build`](skills/digital-health-clinical-asr-build), [`digital-health-clinical-asr-eval`](skills/digital-health-clinical-asr-eval), [`digital-health-clinical-asr-finetune`](skills/digital-health-clinical-asr-finetune) |
+| **Physical AI** | Generate labeled synthetic training data for physical-AI inspection and perception models. | [`physical-ai-defect-image-generation`](skills/physical-ai-defect-image-generation), [`physical-ai-video-data-augmentation`](skills/physical-ai-video-data-augmentation), [`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) |
+| **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. | [`physicsnemo-discover`](skills/physicsnemo-discover) |
+| **RAG Blueprint** | RAG pipeline — deploy, configure, troubleshoot, and manage retrieval augmented generation with Docker Compose or Helm. | [`rag-blueprint`](skills/rag-blueprint), [`rag-eval`](skills/rag-eval), [`rag-perf`](skills/rag-perf) |
+| **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. | [`skill-card-generator`](skills/skill-card-generator) |
+| **TileGym** | Tile-based GPU programming — adding new kernels, cross-framework conversion, and performance optimization. | [`tilegym-adding-cutile-kernel`](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. | [`vss-ask-video`](skills/vss-ask-video), [`vss-deploy-dense-captioning`](skills/vss-deploy-dense-captioning), [`vss-deploy-detection-tracking-2d`](skills/vss-deploy-detection-tracking-2d), [`vss-deploy-detection-tracking-3d`](skills/vss-deploy-detection-tracking-3d), [`vss-deploy-profile`](skills/vss-deploy-profile), [`vss-deploy-video-embedding`](skills/vss-deploy-video-embedding), [`vss-generate-video-calibration`](skills/vss-generate-video-calibration), [`vss-generate-video-report`](skills/vss-generate-video-report), [`vss-manage-alerts`](skills/vss-manage-alerts), [`vss-manage-video-io-storage`](skills/vss-manage-video-io-storage), [`vss-query-analytics`](skills/vss-query-analytics), [`vss-search-archive`](skills/vss-search-archive), [`vss-setup-behavior-analytics`](skills/vss-setup-behavior-analytics), [`vss-setup-video-analytics-api`](skills/vss-setup-video-analytics-api), [`vss-summarize-video`](skills/vss-summarize-video) |
 <!-- skills-table-end -->
 
 ---