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49 changes: 36 additions & 13 deletions skills/nemo-retriever/BENCHMARK.md
Original file line number Diff line number Diff line change
Expand Up @@ -9,12 +9,16 @@ This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the s
- Skill: `nemo-retriever`
- Evaluation date: 2026-05-29
- NVSkills-Eval profile: `external`
- Overall verdict: FAIL
- Tier 3 live agent evaluation: not available in this report
- Environment: `local`
- Dataset: 4 evaluation tasks
- Attempts per task: 2
- Pass threshold: 50%
- Overall verdict: PASS

## Agents Used

- Tier 3 agent details were not available in this report.
- `claude-code`
- `codex`

## Metrics Used

Expand All @@ -28,19 +32,39 @@ Reported benchmark dimensions:

Underlying evaluation signals used in this run:

- No Tier 3 evaluation signal details were available in this report.
- `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.

## Test Tasks

Tier 3 evaluation task details were not available in this report.
The benchmark dataset contained 4 evaluation tasks:

- Positive tasks: 3 tasks where the skill was expected to activate.
- Negative tasks: 1 tasks where no skill was expected.
- Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred.

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.

## Results

Tier 3 dimension rollup was not available in this report.
| Dimension | Num | `claude-code` | `codex` |
|---|---:|---:|---:|
| Security | 8 | 100% (+14%) | 88% (+0%) |
| Correctness | 8 | 77% (+4%) | 69% (-0%) |
| Discoverability | 8 | 95% (-0%) | 68% (+5%) |
| Effectiveness | 8 | 45% (-3%) | 47% (-2%) |
| Efficiency | 8 | 85% (+1%) | 62% (+0%) |

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

Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 20 total findings.
Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 19 total findings.

Top findings:

Expand All @@ -52,14 +76,13 @@ Top findings:

## Tier 2: Deduplication Summary

Tier 2 validation reported findings. NVSkills-Eval ran 2 checks and found 1 total findings.
Tier 2 validation passed. NVSkills-Eval ran 2 checks and found 0 total findings.

Top findings:
Notable observations:

- HIGH DUPLICATE/duplicate: Duplicate content found across references/cli/query.md and references/pitfalls.md:
"## Common failure modes" in references/cli/query.md (lines 78-90)
vs "## Failure modes (expected, not errors)" in references/pitfalls.md (lines 18-26) (`references/cli/query.md:78`)
- Context Deduplication: Collected 9 file(s)
- Inter-Skill Deduplication: Parsed skill 'nemo-retriever': 432 char description

## Publication Recommendation

The skill should be reviewed before NVSkills-Eval publication. Skill owners should address the findings above and rerun NVSkills-Eval to refresh this benchmark.
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.
109 changes: 55 additions & 54 deletions skills/nemo-retriever/evals/evals.json
Original file line number Diff line number Diff line change
@@ -1,55 +1,56 @@
[
{
"id": "nemo-retriever-001",
"question": "Can you use the nemo-retriever tool to search my PDF folder for the term \"machine learning\" and give me the relevant passages?",
"expected_skill": "nemo-retriever",
"expected_script": "None",
"ground_truth": "The agent built a LanceDB index of the PDFs, performed a vector search for \"machine learning\", and returned the matching document excerpts in ./output.json.",
"expected_behavior": [
"The agent read the nemo-retriever SKILL.md before executing commands",
"The agent executed a Bash command to ingest the PDFs with `retriever ingest`",
"The agent executed a Bash command to query the index with `retriever query` for \"machine learning\"",
"The agent wrote ./output.json containing the query results",
"The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace"
]
},
{
"id": "nemo-retriever-002",
"question": "I have a folder of research papers in PDF. How can I find which ones discuss reinforcement learning?",
"expected_skill": "nemo-retriever",
"expected_script": "None",
"ground_truth": "The agent queried the existing PDF index for reinforcement learning topics and supplied the relevant passages in ./output.json.",
"expected_behavior": [
"The agent read the nemo-retriever SKILL.md before executing commands",
"The agent executed a Bash command to query the index with `retriever query` for reinforcement learning",
"The agent wrote ./output.json with the relevant excerpts",
"The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace"
]
},
{
"id": "nemo-retriever-003",
"question": "Our legal team needs to quickly locate clauses about data privacy in the contract PDFs we stored. Can you set up a search over those documents?",
"expected_skill": "nemo-retriever",
"expected_script": "None",
"ground_truth": "The agent created an index of the contract PDFs, searched for data‑privacy clauses, and returned the matching sections in ./output.json.",
"expected_behavior": [
"The agent read the nemo-retriever SKILL.md before executing commands",
"The agent executed a Bash command to ingest the contract PDFs with `retriever ingest`",
"The agent executed a Bash command to query the index with `retriever query` for data privacy clauses",
"The agent wrote ./output.json containing the found clauses",
"The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace"
]
},
{
"id": "nemo-retriever-004",
"question": "What's the best way to bake a chocolate cake?",
"expected_skill": null,
"expected_script": "None",
"ground_truth": "The agent answered the cooking question directly without invoking the nemo-retriever tool.",
"expected_behavior": [
"The agent responded with a textual answer without invoking any Bash commands",
"The agent did not read the nemo-retriever SKILL.md or use the retriever CLI",
"The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace"
]
}
]
{
"id": "nemo-retriever-001",
"question": "Use the nemo-retriever skill to find every mention of \"climate change\" in the PDF reports inside my folder \"research_reports\".",
"expected_skill": "nemo-retriever",
"expected_script": "None",
"ground_truth": "The agent indexed the folder and returned all passages containing \"climate change\" from the PDFs, each with the file name and page number as citations.",
"expected_behavior": [
"The agent read the nemo-retriever SKILL.md before executing commands",
"The agent executed a `retriever ingest` command to index the \"research_reports\" folder",
"The agent executed a `retriever query` command with the search term \"climate change\"",
"The agent returned the matching excerpts with file and page citations",
"The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace"
]
},
{
"id": "nemo-retriever-002",
"question": "Can you search through all the documents I uploaded and give me a summary of the sections that discuss risk management?",
"expected_skill": "nemo-retriever",
"expected_script": "None",
"ground_truth": "The agent searched across the uploaded PDFs, DOCX, and text files, produced a concise summary of each risk‑management section, and included citations to the source documents.",
"expected_behavior": [
"The agent read the nemo-retriever SKILL.md before executing commands",
"The agent executed a `retriever ingest` command to index the uploaded document collection",
"The agent executed a `retriever query` command targeting \"risk management\"",
"The agent returned a summarized answer with citations to each source",
"The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace"
]
},
{
"id": "nemo-retriever-003",
"question": "Our legal team needs to extract every clause about data privacy from the collection of contracts we have (PDFs, Word docs, and scanned images). Please provide the clauses with citations.",
"expected_skill": "nemo-retriever",
"expected_script": "None",
"ground_truth": "The agent indexed the mixed‑format contracts folder and extracted every verbatim data‑privacy clause, listing each clause together with the document name and page/slide number where it appears.",
"expected_behavior": [
"The agent read the nemo-retriever SKILL.md before executing commands",
"The agent executed a `retriever ingest` command to index PDFs, DOCX, and image files in the contracts folder",
Comment on lines +22 to +38
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P2 Summarization claim inconsistent with retriever query output contract

The ground_truth for eval 002 says the agent "produced a concise summary of each risk‑management section," and the final expected_behavior step says "returned a summarized answer with citations." However, retriever query emits a raw JSON array of vector-search hits (one object per chunk with text, source, page_number, etc.) — it does not produce a prose summary. An automated evaluator grading behavior against this step would need to distinguish between "agent returned raw hits" and "agent synthesized a summary," but the step as written conflates the two. If the summarization is expected to come from the LLM interpreting the hits, that should be a separate explicit step or the ground_truth should reflect raw excerpts, as evals 001 and 003 do.

Prompt To Fix With AI
This is a comment left during a code review.
Path: skills/nemo-retriever/evals/evals.json
Line: 22-38

Comment:
**Summarization claim inconsistent with `retriever query` output contract**

The `ground_truth` for eval 002 says the agent "produced a concise summary of each risk‑management section," and the final `expected_behavior` step says "returned a summarized answer with citations." However, `retriever query` emits a raw JSON array of vector-search hits (one object per chunk with `text`, `source`, `page_number`, etc.) — it does not produce a prose summary. An automated evaluator grading behavior against this step would need to distinguish between "agent returned raw hits" and "agent synthesized a summary," but the step as written conflates the two. If the summarization is expected to come from the LLM interpreting the hits, that should be a separate explicit step or the `ground_truth` should reflect raw excerpts, as evals 001 and 003 do.

How can I resolve this? If you propose a fix, please make it concise.

"The agent executed a `retriever query` command to locate clauses containing \"data privacy\"",
"The agent returned each clause verbatim with document and location citations",
"The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace"
]
},
{
"id": "nemo-retriever-004",
"question": "How do I bake a chocolate cake from scratch?",
"expected_skill": null,
"expected_script": "None",
"ground_truth": "The agent provided a step‑by‑step chocolate cake recipe without using the nemo-retriever skill or any tool calls.",
"expected_behavior": [
"The agent responded with a chocolate cake recipe without invoking any tools",
"The agent did not execute any Bash commands or read the nemo-retriever SKILL.md",
"The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace"
]
}
]
Comment on lines 1 to +56
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P1 Output file verification removed from all retrieval evals

The previous versions of evals 001, 002, and 003 all included an explicit expected_behavior step asserting the agent wrote ./output.json. That step has been dropped from every retrieval eval in this update. If any part of the eval harness still checks for an output file on disk to verify actual retrieved content, all three evals will now silently pass even when the agent produces no usable result — the behavioral contract that retrieval happened and was persisted is no longer encoded.

Prompt To Fix With AI
This is a comment left during a code review.
Path: skills/nemo-retriever/evals/evals.json
Line: 1-56

Comment:
**Output file verification removed from all retrieval evals**

The previous versions of evals 001, 002, and 003 all included an explicit `expected_behavior` step asserting the agent wrote `./output.json`. That step has been dropped from every retrieval eval in this update. If any part of the eval harness still checks for an output file on disk to verify actual retrieved content, all three evals will now silently pass even when the agent produces no usable result — the behavioral contract that retrieval happened and was persisted is no longer encoded.

How can I resolve this? If you propose a fix, please make it concise.

40 changes: 32 additions & 8 deletions skills/nemo-retriever/skill-card.md
Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
## Description: <br>
Use when the user wants to search, index, or answer questions over a folder of PDFs (or other documents)including building a RAG / search index over PDFs, looking up information across many PDFs, or running the `retriever` CLI (ingest, query, pipeline, recall, eval, etc.). <br>
Use when the user wants to search, query, extract, transcribe, describe, quote, filter, or aggregate across documents — PDFs, scanned forms / images (.jpg .png .tiff), Office (.docx .pptx), text (.html .txt), audio (.mp3 .wav .m4a), or video (.mp4 .mov). <br>

This skill is ready for commercial/non-commercial use. <br>

Expand All @@ -9,7 +9,7 @@ NVIDIA <br>
### License/Terms of Use: <br>
Apache 2.0 <br>
## Use Case: <br>
Developers and engineers who need to search, index, or answer questions across PDF and document collections using RAG and vector search via the retriever CLI. <br>
Developers and engineers who need to search, query, extract, or aggregate information across multimodal document collections including PDFs, images, Office files, audio, and video for retrieval-augmented generation workflows. <br>

### Deployment Geography for Use: <br>
Global <br>
Expand All @@ -19,23 +19,29 @@ Risk: Review before execution as proposals could introduce incorrect or misleadi
Mitigation: Review and scan skill before deployment. <br>

## Reference(s): <br>
- [NeMo Retriever Library Documentation](https://docs.nvidia.com/nemo/retriever/latest/extraction/overview/) <br>
- [Install Guide](references/install.md) <br>
- [Setup Guide](references/setup.md) <br>
- [Query Workflow](references/query.md) <br>
- [Pitfalls and Recovery](references/pitfalls.md) <br>
- [Query Guide](references/query.md) <br>
- [Troubleshooting](references/troubleshooting.md) <br>
- [CLI: ingest](references/cli/ingest.md) <br>
- [CLI: query](references/cli/query.md) <br>
- [NeMo Retriever Library Documentation](https://docs.nvidia.com/nemo/retriever/latest/extraction/overview/) <br>


## Skill Output: <br>
**Output Type(s):** [Shell commands, JSON] <br>
**Output Format:** [JSON] <br>
**Output Format:** [Markdown with inline bash code blocks and JSON query results] <br>
**Output Parameters:** [1D] <br>
**Other Properties Related to Output:** [None] <br>

## Evaluation Agents Used: <br>
- Claude Code (`claude-code`) <br>
- Codex (`codex`) <br>



## Evaluation Tasks: <br>
NVSkills-Eval 3-Tier evaluation (external profile); Tier 1 static validation (9 checks, 20 findings), Tier 2 deduplication (2 checks, 1 finding). Tier 3 live agent evaluation not available in this report. <br>
Evaluated against 4 evaluation tasks (3 positive skill-activation, 1 negative), 2 attempts per task, 50% pass threshold. Overall verdict: PASS. <br>

## Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
Expand All @@ -45,10 +51,28 @@ Reported benchmark dimensions: <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>

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>



## Evaluation Results: <br>
| Dimension | Num | `claude-code` | `codex` |
|---|---:|---:|---:|
| Security | 8 | 100% (+14%) | 88% (+0%) |
| Correctness | 8 | 77% (+4%) | 69% (-0%) |
| Discoverability | 8 | 95% (-0%) | 68% (+5%) |
| Effectiveness | 8 | 45% (-3%) | 47% (-2%) |
| Efficiency | 8 | 85% (+1%) | 62% (+0%) |

## Skill Version(s): <br>
3fa00d94 (source: git SHA, committed 2026-05-28) <br>
b331d0f7 (source: git SHA, committed 2026-05-29) <br>

## Ethical Considerations: <br>
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>
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