diff --git a/skills/cuopt-numerical-optimization-api/BENCHMARK.md b/skills/cuopt-numerical-optimization-api/BENCHMARK.md
index afa4d393b..a39992fb1 100644
--- a/skills/cuopt-numerical-optimization-api/BENCHMARK.md
+++ b/skills/cuopt-numerical-optimization-api/BENCHMARK.md
@@ -1,81 +1,101 @@
-# Evaluation Report
+# Skill Benchmark: cuopt-numerical-optimization-api
-Evaluation of the `cuopt-numerical-optimization-api` skill before publication through NVSkills-Eval.
+> ✅ **Overall verdict: PASS — Recommended for publication**
-This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the skill. The goal is to document whether the skill is safe, discoverable, effective, and useful for agents before it is published for broader workflow use.
+## Publication Recommendation
+
+Recommended for publication based on the completed evaluation evidence in this report.
-## Evaluation Summary
+## Evaluation Metadata
- Skill: `cuopt-numerical-optimization-api`
-- Evaluation date: 2026-07-02
-- NVSkills-Eval profile: `external`
-- Environment: `astra-sandbox`
-- Dataset: 9 evaluation tasks
+- Evaluation date: 2026-08-05
+- Evaluator version: `1.0.0`
+- Agents: Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`), Codex (`openai/openai/gpt-5.5`)
+- Tasks: 9 evaluation tasks (9 positive)
+- Dataset digest: `sha256:f385c69ce235035e8e5cbfd91a8d3245b75473f0bf0d694451e988e439f6e9e1` (skill-evaluator-dataset-snapshot/1)
- Attempts per task: 1
-- Pass threshold: 50%
-- Overall verdict: PASS
+- Environment: `k8s-sandbox`
+- Tier 3 evidence: required for publication
-## Agents Used
+Each task attempt ran in its own isolated sandbox pod.
-- `claude-code`
-- `codex`
+## What This Report Answers
-## Metrics Used
+The three-tier evaluation checks whether the skill:
-Reported benchmark dimensions:
+- is safe to use;
+- produces correct answers;
+- is discovered and activated when needed;
+- helps the agent complete the user's goal and expected workflow; and
+- avoids wasted skill and tool usage.
-- Security: checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access.
-- Correctness: checks whether the agent follows the expected workflow and produces the correct final output.
-- Discoverability: checks whether the agent loads the skill when relevant and avoids using it when irrelevant.
-- Effectiveness: checks whether the agent performs measurably better with the skill than without it.
-- Efficiency: checks whether the agent uses fewer tokens and avoids redundant work.
+## Results at a Glance
-Underlying evaluation signals used in this run:
+| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
+|---|---:|---:|
+| Overall | 59% → 97% (+39 points) | 61% → 93% (+32 points) |
+| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
+| Correctness | 87% → 98% (+11 points) | 91% → 100% (+9 points) |
+| Discoverability | 28% → 100% (+72 points) | 44% → 92% (+48 points) |
+| Effectiveness | 69% → 89% (+20 points) | 68% → 81% (+12 points) |
+| Efficiency | 10% → 100% (+90 points) | 0% → 90% (+90 points) |
-- `security` (Security): checks for unsafe operations, secret leakage, and unauthorized access.
-- `skill_execution` (Skill Execution): verifies that the agent loaded the expected skill and workflow.
-- `skill_efficiency` (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage.
-- `accuracy` (Accuracy): grades final-answer correctness against the reference answer.
-- `goal_accuracy` (Goal Accuracy): checks whether the overall user task completed successfully.
-- `behavior_check` (Behavior Check): verifies expected behavior steps, including safety expectations.
-- `token_efficiency` (Token Efficiency): compares token usage with and without the skill.
+**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.
-## Test Tasks
+Example: `47% → 92% (+45 points)` means the skill-assisted run scored 92%, 45 percentage points above its 47% no-skill baseline.
-The benchmark dataset contained 9 evaluation tasks:
+## Tier Status
-- Positive tasks: 9 tasks where the skill was expected to activate.
-- Negative tasks: 0 tasks where no skill was expected.
-- Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred.
+| Tier | Purpose | Status | Evidence |
+|---|---|---|---|
+| Tier 1 | Static validation | **PASSED WITH OBSERVATIONS** | 1 validator(s); 4 finding(s) |
+| Tier 2 | Semantic deduplication | **NOT RUN** | No result was recorded |
+| Tier 3 | Live agent evaluation | **PASS** | 2 agent(s); 9 task(s) |
-Task composition is derived from the evaluation dataset when possible. Entries with `expected_skill` set are treated as positive skill-activation cases, while entries with `expected_skill: null` are treated as negative activation cases.
+## Findings and Observations
-## Results
+
+Show detailed findings and successful checks
-| Dimension | Num | `claude-code` | `codex` |
-|---|---:|---:|---:|
-| Security | 8 | 100% (+0%) | 100% (+0%) |
-| Correctness | 8 | 76% (+33%) | 70% (+12%) |
-| Discoverability | 8 | 67% (+58%) | 51% (+31%) |
-| Effectiveness | 8 | 73% (+20%) | 69% (+7%) |
-| Efficiency | 8 | 71% (+46%) | 55% (+28%) |
+- **MEDIUM** SCHEMA/frontmatter_field_placement: Root field 'version' is ignored; use 'metadata.version' (`skills/cuopt-numerical-optimization-api/SKILL.md`)
+- **MEDIUM** SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (`skills/cuopt-numerical-optimization-api/SKILL.md`)
+- **MEDIUM** SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (`skills/cuopt-numerical-optimization-api/SKILL.md`)
+- **LOW** SCHEMA/author_format: Author must be of the form 'Name ' (`skills/cuopt-numerical-optimization-api/SKILL.md`)
-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
+## Scoring Methodology
-Tier 1 validation passed with observations. NVSkills-Eval ran 1 checks and found 3 total findings.
+
+Show dimension definitions, source signals, and thresholds
-Top findings:
+| Dimension | Question | Scored signals |
+|---|---|---|
+| Security | Is it safe to use? | `security` (100%) |
+| Correctness | Is the answer correct? | `accuracy` (100%) |
+| Discoverability | Was the right skill loaded when needed? | `skill_execution` (100%) |
+| Effectiveness | Did the skill help complete the task? | `goal_accuracy` (50%) + `behavior_check` (50%) |
+| Efficiency | Did it avoid wasted tool or skill usage? | `skill_efficiency` (100%) |
-- MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (`skills/cuopt-numerical-optimization-api/SKILL.md`)
-- MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (`skills/cuopt-numerical-optimization-api/SKILL.md`)
-- LOW SCHEMA/author_format: Author must be of the form 'Name ' (`skills/cuopt-numerical-optimization-api/SKILL.md`)
+- Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
+- Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
+- Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
+- The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
+- Effectiveness is the equal-weight mean of goal completion (`goal_accuracy`) and expected workflow adherence (`behavior_check`).
+- Token efficiency is a separate report-only signal. It does not change a dimension score or the overall verdict.
-## Tier 2: Deduplication Summary
+Signals present in this run:
-This tier was not run or did not produce findings in this report.
+- `security` (Security): unsafe operations, secret leakage, and unauthorized access.
+- `skill_execution` (Skill Execution): whether the expected skill was found and executed.
+- `skill_efficiency` (Efficiency): routing quality, workspace-aware skill reads, and productive tool use.
+- `accuracy` (Accuracy): final-answer correctness against the reference answer.
+- `goal_accuracy` (Goal Accuracy): whether the user's goal was achieved.
+- `behavior_check` (Behavior Check): whether the expected workflow behavior was followed.
-## Publication Recommendation
+
+
+## Freshness
-The skill is suitable to proceed toward NVSkills-Eval publication based on this benchmark. Skill owners should keep this file with the skill and refresh it when the evaluation dataset, skill behavior, or target agents materially change.
+Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.
diff --git a/skills/cuopt-numerical-optimization-api/SKILL.md b/skills/cuopt-numerical-optimization-api/SKILL.md
index ef6111730..f3bf4b003 100644
--- a/skills/cuopt-numerical-optimization-api/SKILL.md
+++ b/skills/cuopt-numerical-optimization-api/SKILL.md
@@ -16,6 +16,7 @@ metadata:
---
+
# cuOpt Numerical Optimization API
Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver.
diff --git a/skills/cuopt-numerical-optimization-api/skill-card.md b/skills/cuopt-numerical-optimization-api/skill-card.md
index 840937a36..9bc98e360 100644
--- a/skills/cuopt-numerical-optimization-api/skill-card.md
+++ b/skills/cuopt-numerical-optimization-api/skill-card.md
@@ -1,5 +1,5 @@
## Description:
-Model and solve LP, MILP, and QP (beta) problems using NVIDIA cuOpt's GPU-accelerated solver across Python, C, and CLI interfaces.
+Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver via Python, C, and CLI interfaces.
This skill is ready for commercial/non-commercial use.
@@ -7,16 +7,16 @@ This skill is ready for commercial/non-commercial use.
NVIDIA
### License/Terms of Use:
-Apache-2.0
+Apache 2.0
## Use Case:
-Developers and engineers solving linear programming, mixed-integer linear programming, and quadratic programming problems using NVIDIA cuOpt's Python, C, or CLI interfaces.
+Developers and engineers solving linear programming, mixed-integer linear programming, and quadratic programming optimization problems using NVIDIA cuOpt across Python, C, and CLI interfaces.
### Deployment Geography for Use:
Global
## Requirements / Dependencies:
-**Requires API Key or External Credential:** [No]
-**Credential Type(s):** [None]
+**Requires API Key or External Credential:** [Not Specified]
+**Credential Type(s):** [None identified]
Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate.
@@ -34,47 +34,52 @@ Mitigation: Review and scan skill before deployment.
## Skill Output:
-**Output Type(s):** [Code, Shell commands, Configuration instructions]
-**Output Format:** [Markdown with inline code blocks (Python, C, bash)]
+**Output Type(s):** [Code, Shell commands, Configuration instructions, Analysis]
+**Output Format:** [Markdown with inline code blocks]
**Output Parameters:** [1D]
**Other Properties Related to Output:** [None]
## Evaluation Agents Used:
-- claude-code
-- codex
+- Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`)
+- Codex (`openai/openai/gpt-5.5`)
## Evaluation Tasks:
-Evaluated against 9 internal evaluation tasks (NVSkills-Eval external profile, astra-sandbox environment).
+9 evaluation tasks (9 positive) in isolated sandbox pods.
## Evaluation Metrics Used:
Reported benchmark dimensions:
-- Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access.
-- Correctness: Checks whether the agent follows the expected workflow and produces the correct final output.
-- Discoverability: Checks whether the agent loads the skill when relevant and avoids using it when irrelevant.
-- Effectiveness: Checks whether the agent performs measurably better with the skill than without it.
-- Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work.
+- Security: Whether the skill is safe to use (unsafe operations, secret leakage, unauthorized access).
+- Correctness: Whether the final answer is correct against the reference answer.
+- Discoverability: Whether the right skill was found and executed when needed.
+- Effectiveness: Whether the skill helps complete the user's goal and expected workflow.
+- Efficiency: Whether the skill avoids wasted tool or skill usage.
Underlying evaluation signals used in this run:
- `security`: Checks for unsafe operations, secret leakage, and unauthorized access.
-- `skill_execution`: Verifies that the agent loaded the expected skill and workflow.
-- `skill_efficiency`: Checks routing quality, decoy avoidance, and redundant tool usage.
-- `accuracy`: Grades final-answer correctness against the reference answer.
-- `goal_accuracy`: Checks whether the overall user task completed successfully.
-- `behavior_check`: Verifies expected behavior steps, including safety expectations.
-- `token_efficiency`: Compares token usage with and without the skill.
+- `skill_execution`: Whether the expected skill was found and executed.
+- `skill_efficiency`: Routing quality, workspace-aware skill reads, and productive tool use.
+- `accuracy`: Final-answer correctness against the reference answer.
+- `goal_accuracy`: Whether the user's goal was achieved.
+- `behavior_check`: Whether the expected workflow behavior was followed.
## Evaluation Results:
-| Dimension | Num | `claude-code` | `codex` |
-|---|---:|---:|---:|
-| Security | 8 | 100% (+0%) | 100% (+0%) |
-| Correctness | 8 | 76% (+33%) | 70% (+12%) |
-| Discoverability | 8 | 67% (+58%) | 51% (+31%) |
-| Effectiveness | 8 | 73% (+20%) | 69% (+7%) |
-| Efficiency | 8 | 71% (+46%) | 55% (+28%) |
+| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
+|---|---:|---:|
+| Overall | 59% → 97% (+39 points) | 61% → 93% (+32 points) |
+| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
+| Correctness | 87% → 98% (+11 points) | 91% → 100% (+9 points) |
+| Discoverability | 28% → 100% (+72 points) | 44% → 92% (+48 points) |
+| Effectiveness | 69% → 89% (+20 points) | 68% → 81% (+12 points) |
+| Efficiency | 10% → 100% (+90 points) | 0% → 90% (+90 points) |
+
+## Testing Completed:
+**[x] Agent Red-Teaming**
+**[ ] Network Security**
+**[ ] Product Security**
## Skill Version(s):
26.10.00 (source: frontmatter)
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index 8e842b31c..0064a9fc4 100644
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