Pva Spec
NVIDIAOfficialCreated by Venkat BalanDistributed by NVIDIA
- Skill
- Workflow
- Software development
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The pva-spec skill provides a structured workflow for defining the behavior contract of NVIDIA PVA operators. It guides users through collecting requirements, designing a comprehensive test matrix, and documenting evidence to ensure a deterministic handoff to the OpGen tool. The process requires a valid Doctor report and strictly adheres to predefined JSON templates for specifications, test matrices, and evidence, ensuring all requirements are linked to tests and all conflicts are resolved before finalization.
What it does
- Generates operator specifications, test matrices, and evidence manifests based on requirements.
- Validates inputs against a Doctor report to ensure reference consistency.
- Enforces a strict precedence for resolving conflicting requirements.
- Automates the finalization of OpGen inputs through a validation script.
- Produces a configuration report summarizing the operator status and blocking issues.
When to use it
- After running the Doctor skill for new PVA operators, changes, or repairs.
- When you need to define a formal behavior contract before implementation.
- When preparing operator specifications and test matrices for OpGen.
- When you need to reconcile conflicting requirements or reference implementations.
Advantages
- Ensures deterministic and consistent handoffs to OpGen.
- Provides a clear, auditable trail of requirements and evidence.
- Prevents implementation of ambiguous or conflicting operator behavior.
- Standardizes the specification and testing process across different operators.
Usage examples
- “Create a new PVA operator specification for a custom convolution operator using the provided requirements file.”
- The agent initializes the workspace, validates the Doctor report, and generates the operator-spec.json, test-matrix.json, and evidence.json templates for the user to populate.
- “Finalize the OpGen inputs for the updated image-resize operator.”
- The agent runs the finalizer script to validate the JSON files against the Doctor report and project root, returning a status of CONFIGURED if all checks pass.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/pva-spec/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip pva-spec.zip -d .agents/skills
Documentation by Venkat Balan
<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->
Specify a PVA operator
Define behavior before implementation. Spec owns the behavior contract and test matrix. It does not implement the operator or invoke OpGen.
Inputs
Accept one or more of these sources:
A plain-language description or local requirements file.
A public requirements URL.
A partial or complete specification.
A local or public reference implementation.
An existing operator name or path.
Resolve:
Task mode:
new,change, orrepair.PVA generation:
GEN2,GEN3, or both.PVA SDK version and PVA Solutions version, when used.
Host API: C, C++, or both.
EMU policy:
optionalby default orrequiredby user request.Work directory (
WORK_DIR) and project root (PROJECT_ROOT).A Doctor report that is ready for
spec, or accepted limitations.
Default WORK_DIR to ./pva-operator/<operator-name>/. Resolve it and
user-supplied relative paths against the invocation directory before changing
directories. PROJECT_ROOT is the absolute supplied project directory. Work
directly in that project. Do not overwrite an existing configuration.
SKILL_ROOT is the absolute directory containing this skill's SKILL.md,
after resolving symlinks. Bundled files are relative to that directory. All
command paths below use these absolute roots.
Verify references
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-spec 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 select only relevant API, requirement, architecture, and
data-movement Markdown. Do not fetch the same NVIDIA product documentation.
For each product fact, record the SDK documentation root, local Markdown path,
packaged source path, source SHA-256, version, and section or PDF page.
Do not inspect the VPU ISA instruction-by-instruction reference during Spec (the Programmer's Guide chapter titled "Instruction Set Reference"). The architecture overview remains available for defining platform constraints.
Collect requirements
Inspect supplied implementations within the user's authorized scope and their license terms.
Use this precedence:
Explicit user requirements.
An approved supplied specification.
The public API and independent CPU reference.
Correctness tests, workloads, fixtures, and expected results.
Public operator API documentation.
Public algorithm and format documentation.
Public SDK constraints.
An assumption that the user must approve.
For a URL, record its title, URL, access date, license when available, and SHA-256 for downloaded source. Follow only links that resolve the contract.
For a change or repair, reconcile the existing API, CPU reference, tests, workloads, KPI cases, benchmark cases, and build wiring. Record each conflict. Do not silently select one conflicting source.
Do not copy source into the output. Do not use an example as proof for an unspecified case.
Write the three model outputs
Copy these templates into $WORK_DIR/configure/ and fill them according to
the handoff requirements:
operator-spec-template.json as
operator-spec.json.test-matrix-template.json as
test-matrix.json.evidence-template.json as
evidence.json.
The templates own the field names. Write strict JSON. Do not write comments,
duplicate keys, trailing commas, NaN, or infinity tokens. Use null for an
unknown optional value.
Before finalizing, set the spec's status to CONFIGURED only when no blocking
requirement remains. Set the matrix's operator to the spec's operator.name.
Both coverage.uncovered_requirement_ids and coverage.unresolved_cases must
be empty; resolve outstanding cases before creating the handoff.
For each requirement:
Assign a stable ID.
State the source and authority:
public_nvidia,user_input,skill_workflow, orassumption.Define the observable API, resource, parameter, arithmetic, border, aliasing, execution, error, safety, and interoperability behavior that applies.
Add an unresolved item when a missing value can change behavior.
Mark the item as blocking only when implementation cannot proceed safely.
Record design constraints, not implementation choices. Mark a possible
dataflow, tile, VMEM layout, vector-tail method, address generator, lookup
method, or L2 use as candidate unless the contract requires it.
Define the test matrix
Require at least one correctness case with an independent expected result. Each requirement must have a test. Link every test to one or more requirement IDs. Include the cases that apply:
Each supported type, format, plane, and parameter family.
Minimum, maximum, odd, partial-tile, and vector-tail dimensions.
Tight and padded pitch.
Each border mode and corner.
Rounding, saturation, overflow, NaN, and infinity rules.
Structured and seeded-random input.
Valid and invalid aliasing.
Repeated submission and asynchronous execution.
Invalid resources, formats, dimensions, pitches, and parameters.
Required hardware correctness.
Set execution_policy.hardware to required. Set
execution_policy.emulator to the selected EMU policy. Use the same approved
correctness cases, inputs, references, and tolerances on EMU and hardware when
EMU runs. EMU execution is an early aid. It is not hardware verification.
Add performance cases only when the user or source contract requires them. For each performance case, define the workload, metric, warm-up count, at least 1000 hardware samples, target conditions, and acceptance rule when one exists. Do not turn a public benchmark into a universal threshold. Do not derive a Speed-of-Light target from the implementation under test.
A missing performance target does not block correctness configuration. Use
not_set for performance acceptance.
Record evidence
evidence.json records where requirements came from and which files support
them. See the field purposes in
the handoff requirements.
In evidence.json, record each used source once. For a project file, record
only its category, path relative to PROJECT_ROOT, requirement IDs, and use.
Do not calculate a hash, size, or absolute path. The finalizer calculates those
values.
Record assumptions and conflicts in their named lists.
Create and check the handoff
| Script | Command | Inputs | Outputs | Exit codes |
|---|---|---|---|---|
| finalize_opgen_inputs.py | python3 "$SKILL_ROOT/scripts/finalize_opgen_inputs.py" --operator-spec "$WORK_DIR/configure/operator-spec.json" --test-matrix "$WORK_DIR/configure/test-matrix.json" --evidence "$WORK_DIR/configure/evidence.json" --doctor-report "$DOCTOR_REPORT" --workspace "$PROJECT_ROOT" --output "$WORK_DIR/configure/opgen-inputs.json" | Spec, test matrix, evidence, Doctor report, and project root. | opgen-inputs.json and a JSON validation summary. | 0: valid; 1: failed; 2: invalid command arguments. |
| finalize_opgen_inputs.py | python3 "$SKILL_ROOT/scripts/finalize_opgen_inputs.py" --check "$WORK_DIR/configure/opgen-inputs.json" | Finalized handoff and its recorded inputs. | JSON validation summary; read-only check. | 0: valid; 1: failed; 2: invalid command arguments. |
After the three JSON files are final, resolve DOCTOR_REPORT to the absolute
path of the supplied Doctor report and run:
python3 "$SKILL_ROOT/scripts/finalize_opgen_inputs.py" \
--operator-spec "$WORK_DIR/configure/operator-spec.json" \
--test-matrix "$WORK_DIR/configure/test-matrix.json" \
--evidence "$WORK_DIR/configure/evidence.json" \
--doctor-report "$DOCTOR_REPORT" \
--workspace "$PROJECT_ROOT" \
--output "$WORK_DIR/configure/opgen-inputs.json"Do not create opgen-inputs.json with the model.
If this command reports a missing test for a requirement, add the test or link an existing test that checks the requirement. Run the command again.
Then run:
python3 "$SKILL_ROOT/scripts/finalize_opgen_inputs.py" \
--check "$WORK_DIR/configure/opgen-inputs.json"Do not modify a recorded input after finalization. Return BLOCKED if either
command still fails. Give the files to OpGen only after both commands pass.
Output Format
Write $WORK_DIR/reports/configure-report.md with
the report template.
Return one result:
CONFIGURED: No blocking question remains, and both finalizer commands pass.NEEDS_INPUT: Missing behavior can change the API or required result.INVALID_INPUT: Sources conflict, and no precedence rule resolves them.BLOCKED: A required path, tool, or permission is unavailable.
Return:
Result: CONFIGURED | NEEDS_INPUT | INVALID_INPUT | BLOCKED
Operator: <name>
Task mode: <new | change | repair>
EMU policy: <optional | required>
Requirements: <count>
Correctness cases: <count>
Performance cases: <count>
Blocking questions: <count>
Specification: <absolute path>
Test matrix: <absolute path>
Evidence: <absolute path>
OpGen inputs: <absolute path>
Active workspace: <absolute path>Stop after these outputs. Do not invoke OpGen.
Files
- ATTRIBUTION.mdAdded by AutoGineer922 B
- BENCHMARK.md9.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- README.md7.6 KiB
- SKILL.md10.2 KiB
- agents/openai.yaml425 B
- assets/configure-report-template.md738 B
- assets/evidence-template.json186 B
- assets/operator-spec-template.json1.5 KiB
- assets/test-matrix-template.json377 B
- config/skillspector-baseline.yaml400 B
- evals/EVAL.md1.5 KiB
- evals/config.yml167 B
- evals/evals.json5.0 KiB
- evals/files/approved/doctor-report.md250 B
- evals/files/approved/evidence.json316 B
- evals/files/approved/operator-spec.json2.0 KiB
- evals/files/approved/test-matrix.json940 B
- evals/files/unresolved/doctor-report.md250 B
- evals/files/unresolved/evidence.json316 B
- evals/files/unresolved/operator-spec.json2.0 KiB
- evals/files/unresolved/test-matrix.json1023 B
- references/handoff-requirements.md4.5 KiB
- scripts/finalize_opgen_inputs.pyScript19.5 KiB
- skill-card.md4.8 KiB
- skill.oms.sig9.0 KiB
BENCHMARK.md
Skill Benchmark: pva-spec
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
pva-specValidation 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:
sha256:8afc16927ab74b52ce8abc4e89d82baf444d5004181f7268b76f184587efd3b6(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-spec | nv_inference | openai | mcp_tool_poisoning | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| pva-spec | nv_inference | openai | semantic_developer_intent | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| pva-spec | nv_inference | openai | semantic_quality_policy | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| pva-spec | nv_inference | openai | semantic_security_discovery | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| pva-spec | nv_inference | openai | meta_analyzer | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
Controls for
pva-spec: 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 | 97.0% — baseline ran, but no comparable score was available; uplift unavailable | 93.1% — 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 | 80.0% → 100.0% (+20.0 points) | 75.0% → 95.0% (+20.0 points) |
| Discoverability | 95.0% — baseline ran, but no comparable score was available; uplift unavailable | 90.0% — baseline ran, but no comparable score was available; uplift unavailable |
| Effectiveness | 82.5% → 100.0% (+17.5 points) | 67.5% → 85.0% (+17.5 points) |
| Efficiency | 90.0% — 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 | 97.2% → 95.8% (-1.4 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 | 92.3% → 91.4% (-0.9 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 | 727,189 | 718,308 | +8,881 | +1.24% | skill 4/4; base 4/4 |
| claude-code | spec-finalize-approved-contract | 197,890 | 351,729 | -153,839 | -43.74% | skill 1/1; base 1/1 |
| claude-code | spec-missing-doctor | 159,293 | 144,924 | +14,369 | +9.91% | skill 1/1; base 1/1 |
| claude-code | spec-negative | 35,089 | 34,911 | +178 | +0.51% | skill 1/1; base 1/1 |
| claude-code | spec-unresolved-contract | 334,917 | 186,744 | +148,173 | +79.35% | skill 1/1; base 1/1 |
| codex | All cases | 276,306 | 270,660 | +5,646 | +2.09% | skill 4/4; base 4/4 |
| codex | spec-finalize-approved-contract | 83,828 | 113,072 | -29,244 | -25.86% | skill 1/1; base 1/1 |
| codex | spec-missing-doctor | 64,225 | 55,451 | +8,774 | +15.82% | skill 1/1; base 1/1 |
| codex | spec-negative | 13,255 | 13,183 | +72 | +0.55% | skill 1/1; base 1/1 |
| codex | spec-unresolved-contract | 114,998 | 88,954 | +26,044 | +29.28% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 1,003,495 | 988,968 | +14,527 | +1.47% | 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); 14 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: Instructions don't mention 'run_script' (
skills/pva-spec/SKILL.md)MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (
skills/pva-spec/SKILL.md)MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (
skills/pva-spec/SKILL.md)MEDIUM SECURITY/MCP Least Privilege (LP3): MCP Least Privilege: Without declared permissions the skill's intent is opaque and cannot be validated. (
SKILL.md:1)LOW QUALITY/quality_discoverability: Description very long (352 chars, recommend 50-150) (
skills/pva-spec/SKILL.md)9 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.
Needs: Needs Python
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
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-spec · 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-spec.zip