skills: consolidate three numerical-optimization API skills into one

.claude-plugin/marketplace.json

@@ -33,22 +33,10 @@
       "description": "Numerical optimization (LP, MILP, QP) — concepts, problem-text parsing, and formulation patterns. What LP, MILP, and QP are, required formulation questions, modeling elements, common patterns, and how to parse problem statements (parameters, constraints, decisions, objective). Domain concepts; no API or interface."
     },
     {
-      "name": "cuopt-numerical-optimization-api-python",
-      "source": "./skills/cuopt-numerical-optimization-api-python",
+      "name": "cuopt-numerical-optimization-api",
+      "source": "./skills/cuopt-numerical-optimization-api",
       "skills": "./",
-      "description": "Solve LP, MILP, and QP (beta) with the Python API. Use when the user asks about optimization with linear or quadratic objectives, linear constraints, integer variables, scheduling, resource allocation, facility location, production planning, portfolio optimization, or least squares."
-    },
-    {
-      "name": "cuopt-numerical-optimization-api-c",
-      "source": "./skills/cuopt-numerical-optimization-api-c",
-      "skills": "./",
-      "description": "LP, MILP, and QP (beta) with cuOpt — C API only. Use when the user is embedding LP, MILP, or QP in C/C++."
-    },
-    {
-      "name": "cuopt-numerical-optimization-api-cli",
-      "source": "./skills/cuopt-numerical-optimization-api-cli",
-      "skills": "./",
-      "description": "LP, MILP, and QP (beta) with cuOpt — CLI only (MPS files, cuopt_cli). Use when the user is solving LP, MILP, or QP from MPS via command line."
+      "description": "LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface."
     },
     {
       "name": "cuopt-multi-objective-exploration",

AGENTS.md

@@ -18,10 +18,8 @@ AI agent skills for NVIDIA cuOpt optimization engine. Skills live in **`skills/`
 ### Installation
 - `skills/cuopt-install/` — User install for Python, C, and server (pip, conda, Docker, verification). For building cuOpt from source, see `skills/cuopt-developer/`.
 
-### API (implementation; one interface per skill)
-- `skills/cuopt-numerical-optimization-api-python/` (LP, MILP, QP)
-- `skills/cuopt-numerical-optimization-api-c/` (LP, MILP, QP)
-- `skills/cuopt-numerical-optimization-api-cli/` (LP, MILP, QP)
+### API (implementation)
+- `skills/cuopt-numerical-optimization-api/` (LP, MILP, QP — Python, C, CLI; interface-specific details in `references/`)
 - `skills/cuopt-routing-api-python/`
 - `skills/cuopt-server-api-python/` (deploy + client)
 

docs/cuopt/source/convex-features.rst

@@ -39,6 +39,10 @@ The convex optimization solvers for Linear Programming (LP), Quadratic Programmi
        - ✓
        -
        -
+     * - Pyomo
+       - ✓
+       -
+       -
 
 .. note::
    QCQP/SOCP support is currently in **beta**, and is only supported in CVXPY among modeling languages. We hope to add support for QCQP/SOCP in other modeling languages soon.

docs/cuopt/source/introduction.rst

@@ -137,6 +137,8 @@ cuOpt supports the following APIs:
    - `GAMS <https://www.gams.com/>`_
    - `PuLP <https://pypi.org/project/PuLP/>`_
    - `JuMP <https://github.com/jump-dev/cuOpt.jl>`_
+   - `Pyomo <https://www.pyomo.org/>`_
+   - `CVXPY <https://www.cvxpy.org/>`_
 
 
 ==================================

docs/cuopt/source/milp-features.rst

@@ -19,6 +19,7 @@ The MILP solver can be accessed in the following ways:
    - GAMS
    - PuLP
    - JuMP
+   - Pyomo
 
 - **C API**: A native C API that provides direct low-level access to cuOpt's MILP solver, enabling integration into any application or system that can interface with C.
 

docs/cuopt/source/thirdparty_modeling_languages/index.rst

@@ -28,3 +28,15 @@ JuMP Support
 
 JuMP can be used with near zero code changes: simply switch to cuOpt as a solver to solve linear and mixed-integer programming problems.
 Please refer to the `JuMP documentation <https://github.com/jump-dev/cuOpt.jl>`_ for more information.
+
+--------------------------
+Pyomo Support
+--------------------------
+
+Pyomo models can be used with near zero code changes via cuOpt's direct solver interface: simply select cuOpt as the solver to solve linear and mixed-integer programming problems. Please refer to the `Pyomo documentation <https://www.pyomo.org/>`_ for more information.
+
+--------------------------
+CVXPY Support
+--------------------------
+
+CVXPY can be used with near zero code changes: simply select cuOpt as the solver to solve linear and quadratic programs, as well as QCQP/SOCP problems (beta). Please refer to the `CVXPY documentation <https://www.cvxpy.org/>`_ for more information.

skills/cuopt-multi-objective-exploration/BENCHMARK.md

@@ -7,7 +7,7 @@ This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the s
 ## Evaluation Summary
 
 - Skill: `cuopt-multi-objective-exploration`
-- Evaluation date: 2026-06-29
+- Evaluation date: 2026-07-02
 - NVSkills-Eval profile: `external`
 - Environment: `astra-sandbox`
 - Dataset: 5 evaluation tasks
@@ -55,10 +55,10 @@ Task composition is derived from the evaluation dataset when possible. Entries w
 | Dimension | Num | `claude-code` | `codex` |
 |---|---:|---:|---:|
 | Security | 5 | 100% (+0%) | 100% (+0%) |
-| Correctness | 5 | 90% (+0%) | 87% (-8%) |
-| Discoverability | 5 | 78% (-2%) | 73% (-22%) |
-| Effectiveness | 5 | 85% (-6%) | 85% (+2%) |
-| Efficiency | 5 | 75% (-3%) | 71% (-21%) |
+| Correctness | 5 | 90% (+54%) | 68% (+15%) |
+| Discoverability | 5 | 80% (+60%) | 75% (+50%) |
+| Effectiveness | 5 | 92% (+40%) | 63% (-1%) |
+| Efficiency | 5 | 80% (+40%) | 76% (+35%) |
 
 Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available.
 

skills/cuopt-multi-objective-exploration/SKILL.md

@@ -116,8 +116,8 @@ sort the survivors to form the frontier
 
 Practical notes:
 
-- **Warm-start LP sweeps.** For an LP frontier, carry the previous solve's PDLP warmstart data into the next to cut solve time. Per cuOpt this is **LP-only**: a MILP solve doesn't take a PDLP warmstart (you can optionally seed a MIP start instead). See `cuopt-numerical-optimization-api-python` for the calls.
-- **Cap each MILP solve.** Set a per-solve time limit on MILP sweeps (see `cuopt-numerical-optimization-api-python`) — a sweep is many solves, and branch-and-bound can over-spend certifying optimality past a tiny gap, while cuOpt sets no limit by default and won't warn. Report the points as optimal *to the gap you set*, not certified optimal.
+- **Warm-start LP sweeps.** For an LP frontier, carry the previous solve's PDLP warmstart data into the next to cut solve time. Per cuOpt this is **LP-only**: a MILP solve doesn't take a PDLP warmstart (you can optionally seed a MIP start instead). See `cuopt-numerical-optimization-api` for the calls.
+- **Cap each MILP solve.** Set a per-solve time limit on MILP sweeps (see `cuopt-numerical-optimization-api`) — a sweep is many solves, and branch-and-bound can over-spend certifying optimality past a tiny gap, while cuOpt sets no limit by default and won't warn. Report the points as optimal *to the gap you set*, not certified optimal.
 - **Filter dominated points.** A correct sweep can still emit dominated points (especially weighted-sum near the hull, or MILP). Drop them; they are not part of the frontier.
 - **Resolution is a budget.** Curve fidelity trades against solve count. Start coarse to see the shape, then refine the grid only where the curve bends.
 - **Spend the budget where the slope changes (LP/QP).** Because the ε-constraint dual is the frontier's local slope, compare it across solved points: where it barely changes, the curve is nearly straight — interpolate rather than add solves; where it jumps by more than the solve tolerance, the frontier bends between those points — refine there (smaller differences are solver noise, not curvature). This concentrates solves where the curve actually bends instead of spreading them over a uniform grid. On MILP, judge where to refine from the gaps between primal objective values instead.
@@ -134,5 +134,5 @@ Practical notes:
 
 This skill is solver- and interface-agnostic. The per-solve mechanics — building the objective, adding the ε constraints, passing a warm start, reading status — live in the API skills:
 
-- `cuopt-numerical-optimization-api-python` / `-api-c` / `-api-cli` — LP, MILP, QP solves.
+- `cuopt-numerical-optimization-api` — LP, MILP, QP solves (Python, C, CLI).
 - `cuopt-routing-api-python` — the same frontier workflow applies to routing tradeoffs (distance vs. vehicles vs. time).

skills/cuopt-multi-objective-exploration/skill-card.md

@@ -9,14 +9,14 @@ NVIDIA <br>
 ### License/Terms of Use: <br>
 Apache-2.0 <br>
 ## Use Case: <br>
-Developers and engineers exploring tradeoffs between competing objectives in optimization problems, using cuOpt to trace the Pareto frontier and interpret exchange rates rather than collapsing to a single weighted answer. <br>
+Developers and engineers exploring multi-objective tradeoffs use this skill to orchestrate repeated cuOpt solves and trace Pareto frontiers, enabling informed tradeoff decisions rather than single-objective optima. <br>
 
 ### Deployment Geography for Use: <br>
 Global <br>
 
 ## Requirements / Dependencies: <br>
 **Requires API Key or External Credential:** [Not Specified] <br>
-**Credential Type(s):** [None identified] <br>  
+**Credential Type(s):** [None identified] <br>
 
 Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>
 
@@ -31,7 +31,7 @@ Mitigation: Review and scan skill before deployment. <br>
 
 ## Skill Output: <br>
 **Output Type(s):** [Analysis, Configuration instructions] <br>
-**Output Format:** [Markdown with inline formulations and solver-call patterns] <br>
+**Output Format:** [Markdown with structured frontier tables and tradeoff commentary] <br>
 **Output Parameters:** [1D] <br>
 **Other Properties Related to Output:** [None] <br>
 
@@ -42,7 +42,7 @@ Mitigation: Review and scan skill before deployment. <br>
 
 
 ## Evaluation Tasks: <br>
-Evaluated against 5 internal evaluation tasks (4 positive skill-activation, 1 negative). <br>
+Evaluated against 5 internal tasks (4 positive skill-activation cases, 1 negative activation case) in the astra-sandbox environment using NVSkills-Eval external profile. <br>
 
 ## Evaluation Metrics Used: <br>
 Reported benchmark dimensions: <br>
@@ -67,10 +67,10 @@ Underlying evaluation signals used in this run: <br>
 | Dimension | Num | `claude-code` | `codex` |
 |---|---:|---:|---:|
 | Security | 5 | 100% (+0%) | 100% (+0%) |
-| Correctness | 5 | 90% (+0%) | 87% (-8%) |
-| Discoverability | 5 | 78% (-2%) | 73% (-22%) |
-| Effectiveness | 5 | 85% (-6%) | 85% (+2%) |
-| Efficiency | 5 | 75% (-3%) | 71% (-21%) |
+| Correctness | 5 | 90% (+54%) | 68% (+15%) |
+| Discoverability | 5 | 80% (+60%) | 75% (+50%) |
+| Effectiveness | 5 | 92% (+40%) | 63% (-1%) |
+| Efficiency | 5 | 80% (+40%) | 76% (+35%) |
 
 ## Skill Version(s): <br>
 26.08.00 (source: frontmatter) <br>

skills/cuopt-multi-objective-exploration/skill.oms.sig

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