Skip to content

chore: trigger NVSkills CI for cuopt-user-rules #1679

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
ramakrishnap-nv wants to merge 4 commits into
base: main
Choose a base branch
from
Open

chore: trigger NVSkills CI for cuopt-user-rules #1679

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
24e086e
chore: trigger NVSkills CI for cuopt-user-rules
ramakrishnap-nv Aug 5, 2026
62840f0
Merge branch 'main' into chore/nvskills-ci-cuopt-user-rules
ramakrishnap-nv Aug 5, 2026
a8d1cd0
Attach NVSkills validation signatures
svc-nvskills-signing Aug 5, 2026
ca45cea
Merge branch 'main' into chore/nvskills-ci-cuopt-user-rules
ramakrishnap-nv Aug 6, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
124 changes: 72 additions & 52 deletions skills/cuopt-user-rules/BENCHMARK.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,80 +1,100 @@
# Evaluation Report
# Skill Benchmark: cuopt-user-rules

Evaluation of the `cuopt-user-rules` 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-user-rules`
- Evaluation date: 2026-06-26
- NVSkills-Eval profile: `external`
- Environment: `astra-sandbox`
- Dataset: 1 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: 1 evaluation tasks (1 positive)
- Dataset digest: `sha256:f5dc9533f039e98840d48630667c1be9ac2d1a7910eefdea89b6121e2dd52707` (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 | 66% → 91% (+25 points) | 52% → 98% (+45 points) |
| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
| Correctness | 80% → 100% (+20 points) | 20% → 100% (+80 points) |
| Discoverability | 50% → 100% (+50 points) | 44% → 94% (+50 points) |
| Effectiveness | 61% → 88% (+26 points) | 49% → 94% (+45 points) |
| Efficiency | 38% → 67% (+29 points) | 50% → 100% (+50 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 1 evaluation tasks:
## Tier Status

- Positive tasks: 1 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); 3 finding(s) |
| Tier 2 | Semantic deduplication | **NOT RUN** | No result was recorded |
| Tier 3 | Live agent evaluation | **PASS** | 2 agent(s); 1 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
<details>
<summary>Show detailed findings and successful checks</summary>

| Dimension | Num | `claude-code` | `codex` |
|---|---:|---:|---:|
| Security | 1 | 100% (+0%) | 100% (+0%) |
| Correctness | 1 | 65% (+20%) | 94% (+36%) |
| Discoverability | 1 | 25% (+0%) | 62% (+0%) |
| Effectiveness | 1 | 43% (+12%) | 94% (+65%) |
| Efficiency | 1 | 25% (+0%) | 48% (-12%) |
- **MEDIUM** SCHEMA/frontmatter_field_placement: Root field 'version' is ignored; use 'metadata.version' (`skills/cuopt-user-rules/SKILL.md`)
- **MEDIUM** SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (`skills/cuopt-user-rules/SKILL.md`)
- **LOW** SCHEMA/author_format: Author must be of the form 'Name <email@host>' (`skills/cuopt-user-rules/SKILL.md`)

Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available.
</details>

## Tier 1: Static Validation Summary
## Scoring Methodology

Tier 1 validation passed with observations. NVSkills-Eval ran 1 checks and found 2 total findings.
<details>
<summary>Show dimension definitions, source signals, and thresholds</summary>

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-user-rules/SKILL.md`)
- LOW SCHEMA/author_format: Author must be of the form 'Name <email@host>' (`skills/cuopt-user-rules/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
</details>

## 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.
1 change: 1 addition & 0 deletions skills/cuopt-user-rules/SKILL.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@ metadata:




# cuOpt User Rules

**Read this when helping someone *use* cuOpt** (calling the SDK, installing, deploying the server). For modifying cuOpt itself, switch to `cuopt-developer`.
Expand Down
52 changes: 26 additions & 26 deletions skills/cuopt-user-rules/skill-card.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -9,14 +9,14 @@ NVIDIA <br>
### License/Terms of Use: <br>
Apache 2.0 <br>
## Use Case: <br>
Developers and engineers using NVIDIA cuOpt for optimization tasks (routing, LP, MILP, QP) who need guidance on correct API usage, installation, environment setup, and interaction patterns. <br>
Developers and engineers using NVIDIA cuOpt for vehicle routing, linear programming, mixed-integer linear programming, and quadratic programming via Python, C, CLI, or REST server interfaces. <br>

### Deployment Geography for Use: <br>
Global <br>

## Requirements / Dependencies: <br>
**Requires API Key or External Credential:** [No] <br>
**Credential Type(s):** [None] <br>
**Requires API Key or External Credential:** [Not Specified] <br>
**Credential Type(s):** [None identified] <br>
Comment on lines +18 to +19

@coderabbitai coderabbitai Bot Aug 5, 2026

Copy link
Copy Markdown

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

🗄️ Data Integrity & Integration | 🟠 Major | ⚡ Quick win

Declare the NGC API key requirement.

Lines 18-19 state that no credential is identified. The cuOpt quick-start requires an NGC API key for registry authentication. Update the credential metadata so consumers do not omit a required credential.

Proposed correction 
-**Requires API Key or External Credential:** [Not Specified] <br>
-**Credential Type(s):** [None identified] <br>
+**Requires API Key or External Credential:** [Yes] <br>
+**Credential Type(s):** [NGC API key] <br>
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
**Requires API Key or External Credential:** [Not Specified] <br>
**Credential Type(s):** [None identified] <br>
**Requires API Key or External Credential:** [Yes] <br>
**Credential Type(s):** [NGC API key] <br>
🤖 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-user-rules/skill-card.md` around lines 18 - 19, Update the
credential metadata in the cuOpt skill card so it explicitly declares that an
NGC API key is required for registry authentication. Replace the unspecified
API-key requirement and “None identified” credential type with the appropriate
NGC API key information, preserving the existing metadata format.


Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>

Expand All @@ -27,52 +27,52 @@ 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 API Reference](https://docs.nvidia.com/cuopt/user-guide/latest/api.html) <br>
- [cuopt-examples repo](https://github.com/NVIDIA/cuopt-examples) <br>
- [cuopt-examples repository](https://github.com/NVIDIA/cuopt-examples) <br>
- [Google Colab notebooks](https://colab.research.google.com/github/nvidia/cuopt-examples/) <br>


## Skill Output: <br>
**Output Type(s):** [Configuration instructions, Code, Analysis] <br>
**Output Type(s):** [Analysis, Configuration instructions, Code] <br>
**Output Format:** [Markdown with inline code blocks] <br>
**Output Parameters:** [1D] <br>
**Other Properties Related to Output:** [None] <br>

## Evaluation Agents Used: <br>
- claude-code <br>
- codex <br>
- Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`) <br>
- Codex (`openai/openai/gpt-5.5`) <br>



## Evaluation Tasks: <br>
Evaluated against 1 evaluation task in the NVSkills-Eval external profile within astra-sandbox environment. <br>
1 evaluation task (1 positive) from skill-evaluator-dataset-snapshot, each attempt in an isolated sandbox pod. <br>

## Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
- Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access. <br>
- Correctness: Checks whether the agent follows the expected workflow and produces the correct final output. <br>
- Discoverability: Checks whether the agent loads the skill when relevant and avoids using it when irrelevant. <br>
- Effectiveness: Checks whether the agent performs measurably better with the skill than without it. <br>
- Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work. <br>
- Security: Whether the skill is safe to use (no unsafe operations, secret leakage, or unauthorized access). <br>
- Correctness: Whether the answer is correct against the reference answer. <br>
- Discoverability: Whether the right skill was found and executed when needed. <br>
- Effectiveness: Whether the skill helped complete the user's goal and expected workflow (goal_accuracy 50% + behavior_check 50%). <br>
- Efficiency: Whether the skill avoided wasted tool or skill usage. <br>

Underlying evaluation signals used in this run: <br>
- `security`: Checks for unsafe operations, secret leakage, and unauthorized access. <br>
- `skill_execution`: Verifies that the agent loaded the expected skill and workflow. <br>
- `skill_efficiency`: Checks routing quality, decoy avoidance, and redundant tool usage. <br>
- `accuracy`: Grades final-answer correctness against the reference answer. <br>
- `goal_accuracy`: Checks whether the overall user task completed successfully. <br>
- `behavior_check`: Verifies expected behavior steps, including safety expectations. <br>
- `token_efficiency`: Compares token usage with and without the skill. <br>
- `skill_execution`: Whether the expected skill was found and executed. <br>
- `skill_efficiency`: Routing quality, workspace-aware skill reads, and productive tool use. <br>
- `accuracy`: Final-answer correctness against the reference answer. <br>
- `goal_accuracy`: Whether the user's goal was achieved. <br>
- `behavior_check`: Whether the expected workflow behavior was followed. <br>



## Evaluation Results: <br>
| Dimension | Num | `claude-code` | `codex` |
|---|---:|---:|---:|
| Security | 1 | 100% (+0%) | 100% (+0%) |
| Correctness | 1 | 65% (+20%) | 94% (+36%) |
| Discoverability | 1 | 25% (+0%) | 62% (+0%) |
| Effectiveness | 1 | 43% (+12%) | 94% (+65%) |
| Efficiency | 1 | 25% (+0%) | 48% (-12%) |
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---:|---:|
| Overall | 66% → 91% (+25 points) | 52% → 98% (+45 points) |
| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
| Correctness | 80% → 100% (+20 points) | 20% → 100% (+80 points) |
| Discoverability | 50% → 100% (+50 points) | 44% → 94% (+50 points) |
| Effectiveness | 61% → 88% (+26 points) | 49% → 94% (+45 points) |

@coderabbitai coderabbitai Bot Aug 5, 2026

Copy link
Copy Markdown

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win

Correct the duplicated Claude Code effectiveness uplift.

Both publication artifacts report 61% → 88% (+26 points). The correct uplift is +27 percentage points.

  • skills/cuopt-user-rules/skill-card.md#L74-L74: change +26 points to +27 points.
  • skills/cuopt-user-rules/BENCHMARK.md#L41-L41: change +26 points to +27 points.
📍 Affects 2 files
  • skills/cuopt-user-rules/skill-card.md#L74-L74 (this comment)
  • skills/cuopt-user-rules/BENCHMARK.md#L41-L41
🤖 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-user-rules/skill-card.md` at line 74, Correct the Claude Code
effectiveness uplift from “+26 points” to “+27 points” in
skills/cuopt-user-rules/skill-card.md lines 74-74 and
skills/cuopt-user-rules/BENCHMARK.md lines 41-41, keeping the surrounding
benchmark values unchanged.

| Efficiency | 38% → 67% (+29 points) | 50% → 100% (+50 points) |

## Skill Version(s): <br>
26.10.00 (source: frontmatter) <br>
Expand Down
Loading
Loading