chore(skills): sync outdated skill

skills/nemo-data-designer-plugin/BENCHMARK.md

@@ -7,7 +7,7 @@ This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the s
 ## Evaluation Summary
 
 - Skill: `nemo-data-designer-plugin`
-- Evaluation date: 2026-05-30
+- Evaluation date: 2026-06-02
 - NVSkills-Eval profile: `external`
 - Environment: `local`
 - Dataset: 4 evaluation tasks
@@ -49,16 +49,16 @@ The benchmark included 4 recorded Tier 3 trials, but the source evaluation datas
 | Dimension | Num | `claude-code` | `codex` |
 |---|---:|---:|---:|
 | Security | 2 | 100% (+0%) | 100% (+0%) |
-| Correctness | 2 | 89% (+29%) | 95% (+5%) |
-| Discoverability | 2 | 59% (+9%) | 92% (+32%) |
-| Effectiveness | 2 | 91% (+31%) | 87% (+1%) |
-| Efficiency | 2 | 44% (-6%) | 81% (+44%) |
+| Correctness | 2 | 97% (+0%) | 91% (+8%) |
+| Discoverability | 2 | 89% (+0%) | 82% (+2%) |
+| Effectiveness | 2 | 96% (+1%) | 91% (+26%) |
+| Efficiency | 2 | 73% (-0%) | 74% (-1%) |
 
 Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available.
 
 ## Tier 1: Static Validation Summary
 
-Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 16 total findings.
+Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 17 total findings.
 
 Top findings:
 

skills/nemo-data-designer-plugin/SKILL.md

@@ -108,4 +108,4 @@ def load_config_builder() -> dd.DataDesignerConfigBuilder:
     return config_builder
 ```
 
-Only include Pydantic models, custom generators, seed datasets, and extra dependencies when the task requires them. Prefer including `model_configs` when the dataset uses LLM columns — declaring it in the script keeps the config portable between local `run` and cluster `submit`, while the local YAML registry alternative only works for `run`.
+Only include Pydantic models, custom generators, seed datasets, and extra dependencies when the task requires them. Prefer including `model_configs` when the dataset uses LLM columns — declaring it in the script keeps the config portable between local `run` and cluster `submit`, while the local YAML registry alternative only works for `run`.

skills/nemo-data-designer-plugin/skill-card.md

@@ -7,9 +7,9 @@ This skill is ready for commercial/non-commercial use. <br>
 NVIDIA <br>
 
 ### License/Terms of Use: <br>
-Apache-2.0 <br>
+Apache 2.0 <br>
 ## Use Case: <br>
-Developers and engineers who need to create synthetic datasets, generate training data, or build data generation pipelines for AI/ML applications using the NeMo Platform. <br>
+Developers and engineers who need to create synthetic datasets, generate training data, or build data generation pipelines using the Data Designer library within the NeMo Platform. <br>
 
 ### Deployment Geography for Use: <br>
 Global <br>
@@ -27,9 +27,9 @@ Mitigation: Review and scan skill before deployment. <br>
 
 ## Skill Output: <br>
 **Output Type(s):** [Code, Files] <br>
-**Output Format:** [Python script with PEP 723 inline metadata and a load_config_builder() function] <br>
+**Output Format:** [Python script with PEP 723 inline metadata] <br>
 **Output Parameters:** [1D] <br>
-**Other Properties Related to Output:** [None] <br>
+**Other Properties Related to Output:** [Outputs a Python file with a load_config_builder() function returning a DataDesignerConfigBuilder] <br>
 
 ## Evaluation Agents Used: <br>
 - Claude Code (`claude-code`) <br>
@@ -38,7 +38,7 @@ Mitigation: Review and scan skill before deployment. <br>
 
 
 ## Evaluation Tasks: <br>
-Evaluated against 4 internal evaluation tasks with 2 attempts per task (pass threshold: 50%). <br>
+Evaluated against 4 internal evaluation tasks with 2 attempts per task; pass threshold 50%. <br>
 
 ## Evaluation Metrics Used: <br>
 Reported benchmark dimensions: <br>
@@ -63,13 +63,13 @@ Underlying evaluation signals used in this run: <br>
 | Dimension | Num | `claude-code` | `codex` |
 |---|---:|---:|---:|
 | Security | 2 | 100% (+0%) | 100% (+0%) |
-| Correctness | 2 | 89% (+29%) | 95% (+5%) |
-| Discoverability | 2 | 59% (+9%) | 92% (+32%) |
-| Effectiveness | 2 | 91% (+31%) | 87% (+1%) |
-| Efficiency | 2 | 44% (-6%) | 81% (+44%) |
+| Correctness | 2 | 97% (+0%) | 91% (+8%) |
+| Discoverability | 2 | 89% (+0%) | 82% (+2%) |
+| Effectiveness | 2 | 96% (+1%) | 91% (+26%) |
+| Efficiency | 2 | 73% (-0%) | 74% (-1%) |
 
 ## Skill Version(s): <br>
-0.1.1 (source: git tag) <br>
+0.1.0 (source: pyproject.toml) <br>
 
 ## Ethical Considerations: <br>
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

skills/nemo-data-designer-plugin/skill.oms.sig

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