Pva Debug
NVIDIAOfficialCreated by Venkat BalanDistributed by NVIDIA
- Skill
- Workflow
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The pva-debug skill provides a structured, evidence-based approach to diagnosing and resolving failures in NVIDIA PVA operators. It requires a comprehensive Doctor report as input to ensure the environment, SDK version, and failure context are verified before proceeding. The skill follows a rigorous process: reproducing the failure, isolating the root cause through falsifiable hypotheses, applying a minimal code repair, and verifying the fix against the original failing case and relevant test matrices. It is designed to integrate into larger workflows like OpGen or function as a standalone diagnostic tool, ensuring that repairs are narrow, safe, and documented without modifying public APIs or performance test requirements.
What it does
- Verifies failure evidence against SDK references and lock files.
- Reproduces the exact failure signature without initial code changes.
- Systematically tests falsifiable hypotheses for compile, runtime, or numerical errors.
- Applies the smallest possible code repair to address the proven cause.
- Validates the repair by running the original failing case and affected test matrices.
- Generates a detailed debug report documenting the findings and the repair.
When to use it
- When a PVA operator fails during build, link, or runtime execution.
- When numerical discrepancies are detected between EMU and hardware results.
- When performance regressions occur in a PVA operator.
- When you need a structured, repeatable process to isolate and fix a specific operator bug.
Advantages
- Ensures repairs are evidence-based and minimal, reducing the risk of side effects.
- Maintains strict traceability by preserving original failure evidence.
- Integrates seamlessly with existing build and test workflows.
- Provides a standardized reporting format for debugging outcomes.
Usage examples
- “Debug the failure in the conv2d_pva operator reported in doctor-report.md.”
- The agent validates the report, reproduces the failure, identifies a numerical error in the DMA synchronization, applies a fix, and returns a report confirming the fix.
- “Investigate why the pva_pool_op is failing on hardware but passing in EMU.”
- The agent isolates the hardware-specific failure, tests hypotheses related to device integration, repairs the code, and verifies the fix on hardware.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/pva-debug/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip pva-debug.zip -d .agents/skills
Documentation by Venkat Balan
<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->
Debug a PVA operator
Find one demonstrated cause. Make the smallest safe repair. Do not declare the complete operator passed.
Resolve paths
SKILL_ROOT is the absolute directory containing this skill's SKILL.md,
after resolving symlinks. Bundled files are relative to that directory. Resolve
supplied relative paths against the invocation directory before changing
directories. Reuse OpGen's WORK_DIR for its reports.
For a direct call, use the supplied absolute report path, or resolve the caller's
report work directory as WORK_DIR.
Get the inputs
Require:
A Doctor report that covers the failed capability.
Absolute source and artifact paths, plus a report path or work directory.
Caller identity: OpGen or a user with the same complete failure evidence.
Failed stage, exact command, return code, full log, and source identity.
Smallest known failing input, expected result, and tolerance.
SDK version, PVA generation, compiler backend, build mode, and target identity when relevant.
EMU and hardware results that already exist.
One-based attempt number and maximum attempts for an OpGen call.
Return BLOCKED before a source change when required evidence is absent or the
attempt number is greater than the maximum.
Load the technical basis
Require Doctor's public-reference-corpus check to be PASS. Set
SDK_REFERENCES to the report's absolute sdk_reference_root. Require a readable
index.md and reference-lock.json, a lock SHA-256 matching the report's
sdk_reference_lock_sha256, and the reported SDK version matching this task.
If these fields are absent or no longer match, return BLOCKED and request a
fresh Doctor report. Do not fall back to an installed package or CWD path. Read the
pva-debug route in $SDK_REFERENCES/index.md.
Before reading Markdown, check its SHA-256 against the lock
outputs entry; return BLOCKED on a mismatch. Resolve links relative to each
generated file. Use rg
to find the narrow Markdown that matches the first failed stage.
For each product fact, record the SDK documentation root, local Markdown path, packaged source path, source SHA-256, version, and section or PDF page.
Use supplied or public implementations within the user's authorized scope and their license terms.
Preserve and reproduce the failure
Save the original source identity, uncommitted changes, command, return code, log, input, actual output, and expected output.
Reproduce the failure without a source change.
Reduce the input only when the failure signature stays the same.
Identify the first failed command, operation, or output.
Return NOT_REPRODUCED if the supplied source and command do not reproduce the
failure. Do not make a speculative repair.
Diagnose one cause
Classify the first failure as:
Compile.
Link or device integration.
cuPVA API or lifecycle.
Dataflow, DMA, memory, or synchronization.
Numerical or output correctness.
EMU-pass but hardware-fail.
Target runtime.
Performance regression.
For each hypothesis:
State one falsifiable cause.
Name the supporting evidence.
Run the smallest diagnostic that can prove or reject it.
Save the command and result.
Reject an unsupported hypothesis before you test another.
Use the independent CPU reference and first wrong element for numerical work. Use hardware evidence for a hardware-only failure. Use target compiler listings and hardware profiles for a performance regression. Do not use EMU timing as PVA performance evidence.
Repair and check
Change only the code that the proved cause requires. Do not change the public API, required result, tolerance, or performance test to hide the failure.
Record each changed file and reason. Keep the original evidence unchanged. Write diagnostics and repair evidence to new files.
Then:
Rebuild the failed target.
Run the original failing case.
Run nearby affected cases.
Run the EMU matrix when source changed and EMU support is available or required. Missing optional EMU support is not a failure. Record
SKIPPED_OPTIONALwhen optional EMU support is absent.Run the original hardware case for a hardware-only or performance failure.
Confirm that the original signature is absent.
Record commands, return codes, logs, outputs, and repaired source identity.
These checks prove only the narrow repair. Build-Test owns final verification.
Return control
Write the report with
the debug report template. For an OpGen
workflow, use $WORK_DIR/reports/debug-attempt-<number>.md. For a direct call,
use the supplied absolute report path or $WORK_DIR/reports/debug-report.md.
Return one result:
REPAIRED: The original case and affected checks pass.NOT_REPRODUCED: The supplied source and command do not reproduce the failure.BLOCKED: Required evidence, source, toolchain, target, contract, or attempt budget is unavailable.
Return the report to the caller. Direct the caller to invoke
$pva-build-test with the complete original matrix and repaired source.
For an OpGen call, resume the existing OpGen workflow: REPAIRED requires
fresh Build-Test, and other results require OpGen's final report. Do not
reinvoke OpGen or end the overall run with only the Debug response.
Check public release notes before you identify an SDK limitation. Do not claim an SDK defect without direct evidence.
Files
- ATTRIBUTION.mdAdded by AutoGineer925 B
- BENCHMARK.md9.7 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- README.md7.6 KiB
- SKILL.md5.8 KiB
- agents/openai.yaml349 B
- assets/debug-report-template.md1.2 KiB
- config/skillspector-baseline.yaml400 B
- evals/EVAL.md1.4 KiB
- evals/config.yml167 B
- evals/evals.json4.1 KiB
- evals/files/unverified/doctor-report.md278 B
- evals/files/unverified/failure.md317 B
- skill-card.md4.4 KiB
- skill.oms.sig6.3 KiB
BENCHMARK.md
Skill Benchmark: pva-debug
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
pva-debugValidation status:
passedEvaluation date: 2026-10-28
Evaluator version:
1.6.3Agents: Claude Code (
aws/anthropic/bedrock-claude-opus-5-5), Codex (openai/openai/gpt-6.1-sol)Tasks: 4 evaluation tasks (3 positive, 1 negative)
Dataset digest:
sha256:a8cf25f8aae4bbf25e5486ee05522ad68af43f97d45e798f89157fcb2847840c(skill-evaluator-dataset-snapshot/1)Attempts per task: 1
Environment:
k8s-sandboxTier 2 evidence: required for publication
Tier 3 evidence: required for publication
Each task attempt ran in its own isolated sandbox pod.
Tier 1 LLM Provenance
SkillSpector provider, model, analyzer revision, and determinism evidence retained for this scan.
| Skill | Provider | Adapter | Analyzer | Model | Revision | Determinism |
|---|---|---|---|---|---|---|
| pva-debug | nv_inference | openai | mcp_tool_poisoning | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| pva-debug | nv_inference | openai | semantic_developer_intent | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| pva-debug | nv_inference | openai | semantic_quality_policy | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| pva-debug | nv_inference | openai | semantic_security_discovery | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| pva-debug | nv_inference | openai | meta_analyzer | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
Controls for
pva-debug: temperature=not set (source=provider_default, forwarded=no); seed=not set (source=unset, forwarded=no); reasoning_effort=not set (source=provider_default, forwarded=no)
What This Report Answers
The three-tier evaluation checks whether the skill:
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.
Results at a Glance
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 97.3% — baseline ran, but no comparable score was available; uplift unavailable | 96.8% — baseline ran, but no comparable score was available; uplift unavailable |
| Security | 100.0% → 100.0% (±0.0 points) | 100.0% → 100.0% (±0.0 points) |
| Correctness | 90.0% → 100.0% (+10.0 points) | 95.0% → 100.0% (+5.0 points) |
| Discoverability | 96.7% — baseline ran, but no comparable score was available; uplift unavailable | 88.3% — baseline ran, but no comparable score was available; uplift unavailable |
| Effectiveness | 100.0% → 100.0% (±0.0 points) | 100.0% → 100.0% (±0.0 points) |
| Efficiency | 89.9% — baseline ran, but no comparable score was available; uplift unavailable | 95.7% — baseline ran, but no comparable score was available; uplift unavailable |
How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.
Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.
A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.
Comparison details
Shared-source comparisons use the same signals and weights on both conditions. They do not replace the full dimension scores above or change the publication verdict.
| Agent | Dimension | Basis | Baseline → With skill (uplift) | Explanation |
|---|---|---|---|---|
| claude-code | Discoverability | Dimension comparison | N/A | Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover. |
| claude-code | Efficiency | Token-only comparison | 97.8% → 96.5% (-1.3 points) | Only Token Efficiency is comparable; the full dimension scores use different evaluator sources. |
| codex | Discoverability | Dimension comparison | N/A | Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover. |
| codex | Efficiency | Token-only comparison | 92.5% → 91.3% (-1.2 points) | Only Token Efficiency is comparable; the full dimension scores use different evaluator sources. |
Token Usage
Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.
| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
|---|---|---|---|---|---|---|
| claude-code | All cases | 617,376 | 566,402 | +50,974 | +9.00% | skill 4/4; base 4/4 |
| claude-code | debug-exhausted-budget | 152,490 | 35,425 | +117,065 | +330.46% | skill 1/1; base 1/1 |
| claude-code | debug-missing-evidence | 155,474 | 145,286 | +10,188 | +7.01% | skill 1/1; base 1/1 |
| claude-code | debug-negative | 35,453 | 35,209 | +244 | +0.69% | skill 1/1; base 1/1 |
| claude-code | debug-unverified-corpus | 273,959 | 350,482 | -76,523 | -21.83% | skill 1/1; base 1/1 |
| codex | All cases | 310,629 | 278,896 | +31,733 | +11.38% | skill 4/4; base 4/4 |
| codex | debug-exhausted-budget | 75,701 | 13,289 | +62,412 | +469.65% | skill 1/1; base 1/1 |
| codex | debug-missing-evidence | 80,225 | 87,068 | -6,843 | -7.86% | skill 1/1; base 1/1 |
| codex | debug-negative | 13,356 | 13,310 | +46 | +0.35% | skill 1/1; base 1/1 |
| codex | debug-unverified-corpus | 141,347 | 165,229 | -23,882 | -14.45% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 928,005 | 845,298 | +82,707 | +9.78% | skill 8/8; base 8/8 |
Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. Change is 100 * (with - without) / without; negative means fewer tokens. N/A comparisons include their reason in the coverage column; coverage is never estimated.
Tier Status
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED WITH OBSERVATIONS | 11 validator(s); 10 finding(s) |
| Tier 2 | Semantic deduplication | PASSED | 2 validator(s); 0 finding(s) |
| Tier 3 | Live agent evaluation | PASS | 2 agent(s); 4 task(s) |
Findings and Observations
<details> <summary>Show detailed findings and successful checks</summary>
MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.tags' (
skills/pva-debug/SKILL.md)MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (
skills/pva-debug/SKILL.md)MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (
skills/pva-debug/SKILL.md)LOW QUALITY/quality_correctness: No examples provided (
skills/pva-debug/SKILL.md)LOW QUALITY/quality_discoverability: Description very long (316 chars, recommend 50-150) (
skills/pva-debug/SKILL.md)5 additional finding(s) are available in the full evaluation artifacts.
</details>
Scoring Methodology
<details> <summary>Show dimension definitions, source signals, and thresholds</summary>
| 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 calls and token usage? | skill_efficiency (50%) + token_efficiency (50%) |
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).Efficiency is 50% tool-call productivity (the backward-compatible
skill_efficiencywire id) and 50%token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.
Signals present in this run:
security(Security): unsafe operations, secret leakage, and unauthorized access.skill_execution(Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.skill_efficiency(Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).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.token_efficiency(Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).
</details>
Freshness
Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Venkat Balan
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/pva-debug · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Venkat Balan or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download pva-debug.zip