skills(cuopt-numerical-optimization-formulation): update for mathematical_optimization namespace refactor

skills/cuopt-numerical-optimization-formulation/BENCHMARK.md

@@ -7,7 +7,7 @@ This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the s
 ## Evaluation Summary
 
 - Skill: `cuopt-numerical-optimization-formulation`
-- Evaluation date: 2026-06-23
+- Evaluation date: 2026-06-26
 - NVSkills-Eval profile: `external`
 - Environment: `astra-sandbox`
 - Dataset: 3 evaluation tasks
@@ -55,10 +55,10 @@ Task composition is derived from the evaluation dataset when possible. Entries w
 | Dimension | Num | `claude-code` | `codex` |
 |---|---:|---:|---:|
 | Security | 3 | 100% (+0%) | 100% (+0%) |
-| Correctness | 3 | 83% (+33%) | 33% (-7%) |
-| Discoverability | 3 | 67% (+67%) | 0% (+0%) |
-| Effectiveness | 3 | 98% (+5%) | 67% (-11%) |
-| Efficiency | 3 | 72% (+45%) | 28% (-0%) |
+| Correctness | 3 | 67% (+23%) | 37% (+0%) |
+| Discoverability | 3 | 33% (+33%) | 0% (+0%) |
+| Effectiveness | 3 | 93% (+10%) | 71% (+1%) |
+| Efficiency | 3 | 49% (+22%) | 28% (-0%) |
 
 Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available.
 

skills/cuopt-numerical-optimization-formulation/SKILL.md

@@ -14,6 +14,7 @@ metadata:
 ---
 
 
+
 # Numerical Optimization Formulation
 
 Concepts and workflow for going from a problem description to a clear formulation across LP, MILP, and QP. No API code here.

skills/cuopt-numerical-optimization-formulation/skill-card.md

@@ -9,34 +9,40 @@ NVIDIA <br>
 ### License/Terms of Use: <br>
 Apache 2.0 <br>
 ## Use Case: <br>
-Developers and engineers use this skill to parse optimization problem descriptions and formulate them as LP, MILP, or QP models with correct parameters, constraints, decision variables, and objectives before solving with cuOpt. <br>
+Developers and engineers use this skill to parse natural-language optimization problem descriptions and formulate them as LP, MILP, or QP models with identified parameters, constraints, decision variables, and objectives. <br>
 
 ### Deployment Geography for Use: <br>
 Global <br>
 
+## Requirements / Dependencies: <br>
+**Requires API Key or External Credential:** [No] <br>
+**Credential Type(s):** [None] <br>  
+
+Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>
+
 ## Known Risks and Mitigations: <br>
 Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br>
 Mitigation: Review and scan skill before deployment. <br>
 
 ## Reference(s): <br>
 - [cuOpt User Guide](https://docs.nvidia.com/cuopt/user-guide/latest/introduction.html) <br>
-- [cuOpt Examples](https://github.com/NVIDIA/cuopt-examples) <br>
+- [cuopt-examples](https://github.com/NVIDIA/cuopt-examples) <br>
 
 
 ## Skill Output: <br>
-**Output Type(s):** [Analysis, Code] <br>
-**Output Format:** [Markdown with mathematical formulations] <br>
+**Output Type(s):** [Analysis, Formulation guidance] <br>
+**Output Format:** [Markdown] <br>
 **Output Parameters:** [1D] <br>
 **Other Properties Related to Output:** [None] <br>
 
 ## Evaluation Agents Used: <br>
-- `claude-code` <br>
-- `codex` <br>
+- claude-code <br>
+- codex <br>
 
 
 
 ## Evaluation Tasks: <br>
-Evaluated against 3 internal evaluation tasks (all positive skill-activation cases) via NVSkills-Eval external profile. <br>
+Evaluated against 3 internal evaluation tasks (all positive skill-activation cases). <br>
 
 ## Evaluation Metrics Used: <br>
 Reported benchmark dimensions: <br>
@@ -61,10 +67,10 @@ Underlying evaluation signals used in this run: <br>
 | Dimension | Num | `claude-code` | `codex` |
 |---|---:|---:|---:|
 | Security | 3 | 100% (+0%) | 100% (+0%) |
-| Correctness | 3 | 83% (+33%) | 33% (-7%) |
-| Discoverability | 3 | 67% (+67%) | 0% (+0%) |
-| Effectiveness | 3 | 98% (+5%) | 67% (-11%) |
-| Efficiency | 3 | 72% (+45%) | 28% (-0%) |
+| Correctness | 3 | 67% (+23%) | 37% (+0%) |
+| Discoverability | 3 | 33% (+33%) | 0% (+0%) |
+| Effectiveness | 3 | 93% (+10%) | 71% (+1%) |
+| Efficiency | 3 | 49% (+22%) | 28% (-0%) |
 
 ## Testing Completed: <br>
 **[x] Agent Red-Teaming** <br>

skills/cuopt-numerical-optimization-formulation/skill.oms.sig

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