Attach NVSkills validation signatures

Signed-off-by: nvskills-svc-account <svc-nvskills-signing@nvidia.com>

Author: svc-nvskills-signing

skills/nemo-mbridge-perf-vision-dp-over-cp/BENCHMARK.md

@@ -9,25 +9,25 @@ Recommended for publication based on the completed evaluation evidence in this r
 ## Evaluation Metadata
 
 - Skill: `nemo-mbridge-perf-vision-dp-over-cp`
-- Evaluation date: 2026-08-25
+- Evaluation date: 2026-08-27
 - Evaluator version: `1.3.2`
 - Agents: Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`), Codex (`openai/openai/gpt-5.5`)
 - Tasks: 3 evaluation tasks (2 positive, 1 negative)
-- Dataset digest: `sha256:2040e9e066e5932a1594234dfa6b37b8c793fdfc0c05a80edaa212b85b18e921` (skill-evaluator-dataset-snapshot/1)
+- Dataset digest: `sha256:1f686f2a6deb8b2b442751f0639aafb30c6e89513befad8532f4ef5754f59593` (skill-evaluator-dataset-snapshot/1)
 - Attempts per task: 1
-- Environment: `local`
+- Environment: `k8s-sandbox`
 - Tier 3 evidence: required for publication
 
-Tasks ran on the trusted local host; local mode is not sandboxed.
+Each task attempt ran in its own isolated sandbox pod.
 
 ## Execution and Provenance
 
 - Validation status: `passed`
 - Report generation: `complete`
 - Evaluator version: `1.3.2`
-- Git commit: `0117bc2e3e54da4244a656466526c5b1b5a559ea`
+- Git commit: `eda36f54e38357fe24c321a2ffae85589de9bd48`
 - Content type: requested `auto`, detected `skill`
-- Container image: `gitlab-master.nvidia.com:5005/nvcarps/ci-group/nvcarps-ci/skillevaluator-ci:sha-0117bc2e3e54da4244a656466526c5b1b5a559ea`
+- Container image: `gitlab-master.nvidia.com:5005/nvcarps/ci-group/nvcarps-ci/skillevaluator-ci:sha-eda36f54e38357fe24c321a2ffae85589de9bd48`
 - Container image digest: `not recorded`
 - Tier 3: requested `true`, executed `true`, status `succeeded`
 
@@ -45,12 +45,12 @@ The three-tier evaluation checks whether the skill:
 
 | Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
 |---|---:|---:|
-| Overall | 71% → 98% (+27 points) | 56% → 94% (+39 points) |
+| Overall | 67% → 100% (+32 points) | 59% → 95% (+36 points) |
 | Security | 100% → 100% (±0 points) | 67% → 100% (+33 points) |
-| Correctness | 27% → 93% (+67 points) | 60% → 93% (+33 points) |
-| Discoverability | 100% → 100% (±0 points) | 58% → 96% (+38 points) |
-| Effectiveness | 30% → 98% (+68 points) | 40% → 81% (+41 points) |
-| Efficiency | 98% → 100% (+2 points) | 53% → 100% (+47 points) |
+| Correctness | 47% → 100% (+53 points) | 60% → 93% (+33 points) |
+| Discoverability | 83% → 100% (+17 points) | 58% → 96% (+38 points) |
+| Effectiveness | 34% → 99% (+65 points) | 50% → 88% (+38 points) |
+| Efficiency | 73% → 100% (+27 points) | 62% → 100% (+38 points) |
 
 **How to read this table:** baseline is the same task attempted without the target skill. Uplift is `skill score - baseline score`, shown in percentage points.
 

skills/nemo-mbridge-perf-vision-dp-over-cp/skill-card.md

@@ -9,7 +9,7 @@ NVIDIA <br>
 ### License/Terms of Use: <br>
 Apache 2.0 <br>
 ## Use Case: <br>
-Developers and engineers optimizing Vision-Language Model (VLM) training throughput and memory usage when running with context parallelism greater than 1 in Megatron-Bridge. <br>
+Developers and engineers optimizing VLM training performance who need to reduce peak memory and improve throughput by sharding the vision encoder's image inputs across context-parallel ranks in Megatron-Bridge. <br>
 
 ### Deployment Geography for Use: <br>
 Global <br>
@@ -25,15 +25,14 @@ Risk: Review before execution as proposals could introduce incorrect or misleadi
 Mitigation: Review and scan skill before deployment. <br>
 
 ## Reference(s): <br>
-- [Megatron-Bridge Performance Tuning Guide](docs/performance-guide.md) <br>
-- [Nemotron Omni Modeling Implementation](src/megatron/bridge/models/nemotron_omni/modeling_nemotron_omni.py) <br>
-- [MCore Context Parallel Helpers](3rdparty/Megatron-LM/megatron/core/models/multimodal/context_parallel.py) <br>
-- [Megatron-Bridge Documentation](https://docs.nvidia.com/nemo/megatron-bridge/latest/) <br>
+- [Megatron Bridge Documentation](https://docs.nvidia.com/nemo/megatron-bridge/latest/) <br>
+- [SKILL.md](SKILL.md) <br>
+- [card.yaml](card.yaml) <br>
 
 
 ## Skill Output: <br>
-**Output Type(s):** [Configuration instructions, Analysis, Shell commands] <br>
-**Output Format:** [Markdown with inline code blocks] <br>
+**Output Type(s):** [Configuration instructions, Analysis] <br>
+**Output Format:** [Markdown with inline Python code blocks] <br>
 **Output Parameters:** [1D] <br>
 **Other Properties Related to Output:** [None] <br>
 
@@ -44,35 +43,35 @@ Mitigation: Review and scan skill before deployment. <br>
 
 
 ## Evaluation Tasks: <br>
-Evaluated against 3 evaluation tasks (2 positive, 1 negative) from a curated skill-evaluator dataset. <br>
+Evaluated against 3 evaluation tasks (2 positive, 1 negative) in isolated k8s-sandbox pods with 1 attempt per task. <br>
 
 ## Evaluation Metrics Used: <br>
 Reported benchmark dimensions: <br>
-- Security: Whether the skill is safe to use, checking for unsafe operations, secret leakage, and unauthorized access. <br>
-- Correctness: Whether the skill produces correct answers against the reference answer. <br>
+- Security: Whether the skill is safe to use (unsafe operations, secret leakage, unauthorized access). <br>
+- Correctness: Whether the skill produces correct answers against the reference. <br>
 - Discoverability: Whether the right skill is loaded and activated when needed. <br>
-- Effectiveness: Whether the skill helps the agent complete the user's goal and expected workflow (equal-weight mean of goal completion and behavior adherence). <br>
-- Efficiency: Whether the skill avoids wasted tool or skill usage, measuring routing quality and productive tool use. <br>
+- Effectiveness: Whether the skill helps complete the user's goal and expected workflow (goal completion + behavior adherence). <br>
+- Efficiency: Whether the skill avoids wasted tool or skill usage (routing quality and productive tool use). <br>
 
 Underlying evaluation signals used in this run: <br>
-- `security`: Checks for unsafe operations, secret leakage, and unauthorized access. <br>
-- `accuracy`: Final-answer correctness against the reference answer. <br>
+- `security`: Unsafe operations, secret leakage, and unauthorized access. <br>
 - `skill_execution`: Whether the expected skill was found and executed. <br>
+- `skill_efficiency`: Routing quality, workspace-aware skill reads, and productive tool use. <br>
+- `accuracy`: Final-answer correctness against the reference answer. <br>
 - `goal_accuracy`: Whether the user's goal was achieved. <br>
 - `behavior_check`: Whether the expected workflow behavior was followed. <br>
-- `skill_efficiency`: Routing quality, workspace-aware skill reads, and productive tool use. <br>
 
 
 
 ## Evaluation Results: <br>
 | Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
 |---|---:|---:|
-| Overall | 71% → 98% (+27 points) | 56% → 94% (+39 points) |
+| Overall | 67% → 100% (+32 points) | 59% → 95% (+36 points) |
 | Security | 100% → 100% (±0 points) | 67% → 100% (+33 points) |
-| Correctness | 27% → 93% (+67 points) | 60% → 93% (+33 points) |
-| Discoverability | 100% → 100% (±0 points) | 58% → 96% (+38 points) |
-| Effectiveness | 30% → 98% (+68 points) | 40% → 81% (+41 points) |
-| Efficiency | 98% → 100% (+2 points) | 53% → 100% (+47 points) |
+| Correctness | 47% → 100% (+53 points) | 60% → 93% (+33 points) |
+| Discoverability | 83% → 100% (+17 points) | 58% → 96% (+38 points) |
+| Effectiveness | 34% → 99% (+65 points) | 50% → 88% (+38 points) |
+| Efficiency | 73% → 100% (+27 points) | 62% → 100% (+38 points) |
 
 ## Testing Completed: <br>
 **[x] Agent Red-Teaming** <br>

skills/nemo-mbridge-perf-vision-dp-over-cp/skill.oms.sig

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