chore: trigger NVSkills CI for cuopt-numerical-optimization-api - #1674
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📝 WalkthroughWalkthroughThe PR refreshes the cuOpt numerical optimization skill documentation, benchmark report, metadata, testing status, and Sigstore attestation. It also adds spacing before the main ChangescuOpt skill refresh
Estimated code review effort: 2 (Simple) | ~10 minutes Possibly related PRs
Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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ramakrishnap-nv
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improvement
CI Test Summary⏭️ All 5 test job(s) skipped. |
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/nvskills-ci |
Signed-off-by: nvskills-svc-account <svc-nvskills-signing@nvidia.com>
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@skills/cuopt-numerical-optimization-api/BENCHMARK.md`:
- Around line 35-46: Correct the uplift values in both
skills/cuopt-numerical-optimization-api/BENCHMARK.md lines 35-46 and
skills/cuopt-numerical-optimization-api/skill-card.md lines 70-77 so they match
the displayed rounded scores, including Claude Code Overall as +38 and Codex
Effectiveness as +13. If the values intentionally use unrounded scores, document
the rounding rule in both tables.
🪄 Autofix
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
⚙️ Run configuration
Configuration used: Path: .coderabbit.yaml
Review profile: CHILL
Plan: Enterprise
Run ID: df814b0d-865a-47f4-90da-3e4300174444
📒 Files selected for processing (3)
skills/cuopt-numerical-optimization-api/BENCHMARK.mdskills/cuopt-numerical-optimization-api/skill-card.mdskills/cuopt-numerical-optimization-api/skill.oms.sig
| | 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) | | ||
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| - `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. | ||
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| ## Test Tasks | ||
| Example: `47% → 92% (+45 points)` means the skill-assisted run scored 92%, 45 percentage points above its 47% no-skill baseline. |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -eu
for file in skills/cuopt-numerical-optimization-api/BENCHMARK.md \
skills/cuopt-numerical-optimization-api/skill-card.md; do
echo "===== $file ====="
sed -n '25,55p' "$file" 2>/dev/null || true
sed -n '60,90p' "$file" 2>/dev/null || true
done
echo "===== related metric definitions and source values ====="
rg -n -i -C 3 'baseline|uplift|overall|effectiveness|raw score|rounding|percentage points|benchmark' \
skills/cuopt-numerical-optimization-apiRepository: NVIDIA/cuopt
Length of output: 1947
🌐 Web query:
site:github.com/NVIDIA/cuopt "Claude Code (Baseline → Skill Uplift)" "Efficiency"
💡 Result:
Claude Code is a tool-use capable AI coding agent utilized in software development environments to automate tasks such as code generation, refactoring, issue resolution, and documentation [1][2][3][4]. In the context of "baseline to skill uplift" efficiency, this refers to the process of enhancing an AI agent's performance by moving from a general-purpose baseline capability to using specialized, curated "skills" [5][4]. NVIDIA's cuOpt project provides a practical example of this approach. Developers use Claude Code to perform tasks, and performance is "uplifted" by equipping the agent with specific domain-knowledge packages called "skills" [5][4]. Key aspects of this efficiency model include: 1. Baseline Capability: The AI agent begins with general coding knowledge, which is sufficient for standard tasks but may struggle with highly specialized or complex, multi-objective problem formulations [5]. 2. Skill Uplift: Developers introduce "skills"—which may include domain-specific APIs, structured documentation, and guided workflows—that the agent can reference to perform tasks more accurately and efficiently [5][4]. For example, a "multi-objective exploration" skill allows an agent to move beyond single-objective optimization to exploring Pareto frontiers, providing better guidance on trade-offs rather than just returning a single answer [5]. 3. Performance Metrics: Efficiency gains are often measured through A/B testing on synthetic instances or standard benchmarks [5]. This involves comparing the agent's output "with vs. without" the specific skill to ensure the agent demonstrates better adherence to requirements, improved logical reasoning, or faster task completion [5]. 4. Agentic Evolution: The framework often includes a "skill-evolution" workflow where the skills themselves are maintained, evaluated, and updated over time, allowing the agent to continuously improve its performance on domain-specific tasks without needing a fundamental retraining of the underlying model [4]. This structure allows technical teams to standardize agent behavior, reduce hallucinations or errors in specialized domains, and improve the reliability of AI-generated code in production-critical environments [5][4].
Citations:
- 1: VRP: transparent remote execution via CUOPT_REMOTE_HOST/PORT (solve parity with LP/MIP) #1633
- 2: VRP gRPC: server-side validation of routing problem inputs #1631
- 3: VRP gRPC: log streaming and incumbent parity with LP/MILP #1630
- 4: https://github.com/NVIDIA/cuopt/blob/main/RELEASE-NOTES.md
- 5: Proposal: a cuopt-multi-objective-exploration concept skill #1351
🌐 Web query:
site:github.com/NVIDIA/cuopt "How to read this table" "skill score - baseline score"
💡 Result:
In technical contexts, particularly within performance evaluation, model assessment, or skill-based frameworks (such as the NVIDIA cuOpt Agent Skills system), comparing a skill score to a baseline score is a method for measuring the value-add of a specific model, agent, or intervention over a reference point [1][2][3]. How to interpret this comparison: 1. Skill Score: This represents the performance metric achieved by your target system (e.g., an AI agent, a specific configuration, or an optimized model) under test [1][3]. It reflects the actual capability or outcome of the model being evaluated [4][3]. 2. Baseline Score: This is the reference performance metric, representing a standard, default, or "naive" approach against which the target system is compared [2][3]. It establishes a floor for performance, showing what could be achieved without the specific skill or improvement being tested [5][3]. 3. Interpretation: - If the Skill Score > Baseline Score: The target system provides a measurable improvement, demonstrating that the skill or modification adds value [1]. - If the Skill Score = Baseline Score: The system is performing at the same level as the standard or naive approach, suggesting the skill or modification is ineffective for that specific metric. - If the Skill Score < Baseline Score: The modification is negatively impacting performance, indicating that the target system performs worse than the baseline [1]. In systems like the cuOpt agent framework, these scores are used to validate "skill" updates—where an agent iterates on a task, calculates a score, and compares it against previous iterations (baseline) to decide whether to distill that learning into a permanent improvement [2][3]. A higher score relative to the baseline confirms that the new logic or configuration is a beneficial contribution to the agent's capabilities [1][3].
Citations:
- 1: Improve cuopt-developer skill content and sibling-skill routing #1176
- 2: https://github.com/NVIDIA/cuopt/blob/main/AGENTS.md
- 3: skill-evolution: collapse three-phase model to single workflow #1163
- 4: Add initial skill evaluation dataset for cuopt-developer #1167
- 5: Proposal: a cuopt-multi-objective-exploration concept skill #1351
🏁 Script executed:
#!/bin/bash
set -eu
base="https://raw.githubusercontent.com/NVIDIA/cuopt/main"
for path in \
skills/cuopt-numerical-optimization-api/BENCHMARK.md \
skills/cuopt-numerical-optimization-api/skill-card.md; do
echo "===== $path ====="
curl --fail --silent --show-error "$base/$path" \
| nl -ba \
| sed -n '25,90p'
doneRepository: NVIDIA/cuopt
Length of output: 1947
Correct the uplift values in both tables.
The displayed scores yield Claude Code Overall +38 (97 - 59) and Codex Effectiveness +13 (81 - 68). If unrounded scores produce different uplifts, document the rounding rule.
📍 Affects 2 files
skills/cuopt-numerical-optimization-api/BENCHMARK.md#L35-L46(this comment)skills/cuopt-numerical-optimization-api/skill-card.md#L70-L77
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@skills/cuopt-numerical-optimization-api/BENCHMARK.md` around lines 35 - 46,
Correct the uplift values in both
skills/cuopt-numerical-optimization-api/BENCHMARK.md lines 35-46 and
skills/cuopt-numerical-optimization-api/skill-card.md lines 70-77 so they match
the displayed rounded scores, including Claude Code Overall as +38 and Codex
Effectiveness as +13. If the values intentionally use unrounded scores, document
the rounding rule in both tables.
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/ok to test 25706dd |
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/ok to test 41eda3f |
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/ok to test 78e8627 |
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/merge |
3 participants
Trivial blank-line addition after SKILL.md frontmatter to trigger NVSkills CI — fetch updated skill card and benchmark for
cuopt-numerical-optimization-api.