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Nvshmem Tune Performance

NVIDIAOfficialCreated by NVIDIA NVSHMEM Team

  • Skill
  • Meta (authoring, orchestration, planning, memory)
  • Software development
  • Apache-2.0
  • Medium risk

From the author

Route NVSHMEM tuning to data collection, remote transport, NIC-to-PE mapping, or TMA. Do not use for unrelated CUDA, NCCL, or application tuning.
— the description by NVIDIA NVSHMEM Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as a central router for NVSHMEM performance optimization. It does not perform tuning itself but instead analyzes user requests to determine the appropriate specialist skill for data collection, remote transport selection, NIC-to-PE mapping, or TMA enablement. It ensures that performance tuning follows a structured workflow, requiring explicit user selection when objectives are underspecified and maintaining context across multi-stage optimization processes.

What it does

  • Routes performance tuning requests to specialized NVSHMEM sub-skills.
  • Manages workflows for baseline data collection and targeted optimization.
  • Requires explicit user selection for underspecified performance improvement requests.
  • Sequences multi-stage workflows based on user intent and dependencies.
  • Preserves configuration constraints and context during handoffs to specialists.
  • Handles scenarios where required specialist skills are unavailable.

When to use it

  • When you need to optimize NVSHMEM performance but are unsure which specific domain to address.
  • When you have a clear objective such as baseline collection, transport selection, or NIC mapping.
  • When you need to coordinate a multi-stage performance tuning process.
  • When you want to ensure performance tuning follows established NVSHMEM best practices.

Advantages

  • Prevents misrouting of performance tuning tasks to unrelated domains.
  • Ensures that technical work is performed by the correct specialist.
  • Maintains consistency by preserving user constraints and context.
  • Reduces ambiguity by requiring explicit choices for general tuning requests.

Usage examples

“Collect a baseline for our two-node NVSHMEM all-to-all before tuning it.”
The skill invokes the performance data collection specialist while preserving the two-node and all-to-all constraints.
“Which NVSHMEM remote transport should we use with this InfiniBand cluster?”
The skill identifies the request as an inter-node data path concern and invokes the remote transport selection specialist.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/nvshmem-tune-performance/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip nvshmem-tune-performance.zip -d .agents/skills

Documentation by NVIDIA NVSHMEM Team

Tune NVSHMEM Performance

Purpose

Guide the user to the smallest applicable NVSHMEM performance workflow. Offer optional baseline collection and three focused optimization services while preserving each specialist's inputs, execution boundary, and safety rules.

Do not reproduce the specialists' technical guidance in this router. Invoke:

User goalSpecialist
Collect a baseline, run perftests, or package system and benchmark evidence$nvshmem-collect-performance-data
Select an inter-node remote transport$nvshmem-select-remote-transport
Select HCAs or optimize NIC-to-PE mapping$nvshmem-configure-nic-pe-mapping
Assess, prepare, review, or debug NVSHMEM TMA use$nvshmem-enable-tma

Requirements

  • Make the four specialist skills listed above available; this router delegates all technical work to them.

  • Require no API keys, cluster access, NVSHMEM installation, or benchmark tools to select a route. The selected specialist establishes any environment-specific prerequisites before acting.

Inputs

Required
  • Obtain the user's NVSHMEM performance objective or selected service. When the request is underspecified, require an explicit selection among the workflow alternatives in Section 2 before invoking a specialist.

Optional
  • Reuse any supplied performance report, raw logs, exact NVSHMEM version and installation prefix, source or kernel path, fabric and provider, job scale, PE-to-GPU binding, GPU/NIC topology, selected transport, tuning constraints, and optimization goal.

  • Reuse relevant results and unresolved conditions from an earlier specialist in the same workflow.

  • Ask only for information needed to resolve the routing target. Let the selected specialist inspect the environment or request its own required inputs.

Prefer the current explicit user request, then explicit invocation arguments, then non-conflicting context and prior specialist output. Report conflicting values instead of choosing silently.

Instructions

1. Resolve the Routing Target

Honor an explicitly named specialist or workflow. Otherwise classify the concrete objective:

  • Route requests for performance baselines, benchmark curves, latency or bandwidth perftests, performance sanity checks, or packaged system evidence to performance data collection.

  • Route requests about NVSHMEM_REMOTE_TRANSPORT, IBRC, IBDevX, IBGDA, GPUNetIO, UCX, libfabric, remote-network compatibility, or choosing an inter-node data path to remote transport selection.

  • Route requests about HCA selection, NIC ports, GPU-to-NIC affinity, multi-NIC use, NVSHMEM_HCA_*, or PE-to-NIC assignment to NIC-to-PE mapping.

  • Route requests about TMA, NVSHMEM_TMA_POLICY, CTA shared-memory registration, direct shared-memory transfers, or preparing a CUDA kernel for TMA to TMA enablement.

Invoke one specialist for a single concrete request. State which specialist is being invoked and why.

2. Resolve an Underspecified Performance Request

When the request states only a general intent to improve or tune NVSHMEM performance, do not select a specialist or run anything. Present these workflow alternatives and require an explicit selection:

  1. Baseline characterization workflow: collect initial performance and system evidence before selecting an optimization domain.

  2. Targeted optimization workflow: proceed without preliminary collection and select one of:

    • inter-node remote-transport selection;

    • topology-aware NIC-to-PE mapping;

    • TMA suitability assessment or enablement for eligible peer-GPU/NVLink kernel paths.

Permit performance-data collection as the terminal scope. Briefly distinguish the three optimization domains without recommending one in the absence of supporting evidence.

3. Sequence Explicit Multi-Stage Workflows
  1. Invoke performance data collection first only when the user requests it, selects the baseline characterization workflow, or explicitly specifies a collection-first sequence. Let that skill obtain its required benchmark selection before any run.

  2. Invoke remote transport selection before NIC-to-PE mapping when both are requested. Pass the selected transport, version, provider, topology evidence, and unresolved conditions into the mapping workflow.

  3. Invoke TMA separately. Do not treat TMA as a remote-transport optimization or infer that inter-node benchmark evidence establishes TMA eligibility.

  4. Invoke no unrequested specialist. Let the active specialist resolve its own prerequisites instead of filling them in.

After data collection completes, invoke an optimization specialist already selected by the user. If no optimization domain was selected, present the three targeted optimization alternatives again and require an explicit selection.

4. Preserve the Handoff
  • Pass the original request, explicit constraints, exact paths, commands, logs, configuration values, source scope, raw artifacts, resolved facts, and relevant prior specialist output.

  • Preserve the user's transport settings and tuning constraints during collection and later comparison.

  • Return the specialist's result without replacing its confidence, unknowns, validation status, or requested follow-up.

  • Keep benchmark execution, system inspection, source edits, and configuration changes inside the selected specialist's authorization and guardrails.

5. Handle an Unavailable Specialist

If a required skill is unavailable, name it, explain which requested service cannot proceed, and ask the user to make it available or choose another service. Do not imitate or reconstruct the missing skill.

Limitations

  • Do not run benchmarks, collectors, launchers, or target applications directly from this router.

  • Do not edit CUDA source, generate transport exports, or synthesize NIC mappings directly from this router.

  • Do not make data collection an implicit prerequisite merely because an optimization might benefit from later measurement.

  • Do not promise a performance improvement or declare a regression without the evidence required by the selected specialist.

  • Do not treat TMA as useful for remote-network traffic; route it only as a separate peer GPU/NVLink optimization workflow.

  • Do not route unrelated NCCL, CUDA-only, operating-system, or general application-performance questions to these NVSHMEM skills.

Examples

Route a request to collect evidence before choosing an optimization:

User: Collect a baseline for our two-node NVSHMEM all-to-all before tuning it.
Action: Invoke $nvshmem-collect-performance-data and preserve the two-node and all-to-all constraints.

Route an explicit network-configuration request directly:

User: Which NVSHMEM remote transport should we use with this InfiniBand cluster?
Action: Invoke $nvshmem-select-remote-transport because the request concerns an inter-node data path.

Troubleshooting

Error or conditionCauseSolution
Routing target is underspecifiedThe request states only a general intent to improve performance.Present the baseline-characterization and targeted-optimization workflows; require an explicit selection.
A requested specialist is unavailableIts skill is not installed or enabled.Name the unavailable specialist and ask the user to enable it or choose another route.
Supplied settings conflictThe request and earlier context specify different topology, transport, or constraints.Report the conflict and ask which value to preserve before handing off.

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Files

BENCHMARK.md

Skill Benchmark: nvshmem-tune-performance

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

  • Skill: nvshmem-tune-performance

  • Evaluation date: 2026-09-17

  • Evaluator version: 1.5.6

  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)

  • Tasks: 8 evaluation tasks (7 positive, 1 negative)

  • Dataset digest: sha256:126513e60f7ca3d43627ba9aba60b1359057942392c2730e0118164134b1d05d (skill-evaluator-dataset-snapshot/1)

  • Attempts per task: 3

  • Environment: k8s-sandbox

  • Tier 2 evidence: required for publication

  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

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

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall81.6% — baseline ran, but no comparable score was available; uplift unavailable84.6% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)100.0% → 100.0% (±0.0 points)
Correctness29.4% → 77.5% (+48.1 points)47.7% → 82.5% (+34.8 points)
Discoverability71.4% — baseline ran, but no comparable score was available; uplift unavailable74.3% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness33.6% → 68.3% (+34.7 points)27.1% → 68.1% (+41.0 points)
Efficiency90.5% — baseline ran, but no comparable score was available; uplift unavailable98.3% — 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.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

AgentDataset caseWith skillWithout skillDeltaChangeCoverage
claude-codeAll cases1,101,7623,049,342N/AN/Askill 8/8; base 17/17
claude-codenvshmem-tune-performance-00197,029280,089-183,060-65.36%skill 1/1; base 1/1
claude-codenvshmem-tune-performance-002161,561930,512N/AN/Askill 1/1; base 3/3
claude-codenvshmem-tune-performance-003192,973579,122N/AN/Askill 1/1; base 3/3
claude-codenvshmem-tune-performance-004197,938343,738N/AN/Askill 1/1; base 3/3
claude-codenvshmem-tune-performance-005196,700534,604N/AN/Askill 1/1; base 3/3
claude-codenvshmem-tune-performance-006162,679319,211N/AN/Askill 1/1; base 2/2
claude-codenvshmem-tune-performance-00762,18530,045+32,140+106.97%skill 1/1; base 1/1
claude-codenvshmem-tune-performance-00830,69732,021-1,324-4.13%skill 1/1; base 1/1
codexAll cases280,9061,480,686N/AN/Askill 8/8; base 13/13
codexnvshmem-tune-performance-00128,76718,370+10,397+56.60%skill 1/1; base 1/1
codexnvshmem-tune-performance-00243,936211,984-168,048-79.27%skill 1/1; base 1/1
codexnvshmem-tune-performance-00328,626247,663N/AN/Askill 1/1; base 2/2
codexnvshmem-tune-performance-00428,687197,900N/AN/Askill 1/1; base 3/3
codexnvshmem-tune-performance-00545,143392,058N/AN/Askill 1/1; base 3/3
codexnvshmem-tune-performance-00644,05389,216-45,163-50.62%skill 1/1; base 1/1
codexnvshmem-tune-performance-00728,606288,392-259,786-90.08%skill 1/1; base 1/1
codexnvshmem-tune-performance-00833,08835,103-2,015-5.74%skill 1/1; base 1/1
ALL AGENTSDataset aggregate1,382,6684,530,028N/AN/Askill 16/16; base 30/30

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.

Tier Status

TierPurposeStatusEvidence
Tier 1Static validationPASSED11 validator(s); 0 finding(s)
Tier 2Semantic deduplicationPASSED2 validator(s); 0 finding(s)
Tier 3Live agent evaluationPASS2 agent(s); 8 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

  • Schema & Repository Governance: Found skill manifest: SKILL.md

  • Semantic Version Validation: Valid semantic version: 1.0.0

  • Security Scan: No security vulnerabilities detected (secrets, API keys, credentials)

  • PII Scan: Scanning 1 files for PII

  • Code Integrity & Hygiene: Checking 1 markdown files for dead links

  • Unicode Smuggling Detection: No invisible Unicode characters detected in 1 file(s)

  • QUALITY: Score: 100.0/100 (Grade: A)

  • SCRIPT_LINT: No scripts/ directory found

  • Context Deduplication: Collected 1 file(s)

  • Inter-Skill Deduplication: Parsed skill 'nvshmem-tune-performance': 145 char description

  • AGENT_EVAL: Tier 3 evaluation complete: verdict PASS; best agent codex

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

DimensionQuestionScored signals
SecurityIs it safe to use?security (100%)
CorrectnessIs the answer correct?accuracy (100%)
DiscoverabilityWas the right skill loaded when needed?skill_execution (100%)
EffectivenessDid the skill help complete the task?goal_accuracy (50%) + behavior_check (50%)
EfficiencyDid 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_efficiency wire 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 an API key, Needs Node.js

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
NVIDIA NVSHMEM Team
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/nvshmem-tune-performance · 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 NVIDIA NVSHMEM Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nvshmem-tune-performance.zip