Nemo Relay Plugin Adaptive Tuning
NVIDIAOfficialCreated by NVIDIA Corporation and Affiliates
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured approach to tuning adaptive plugin behavior within an existing NeMo Relay instrumentation. It guides users through a measured rollout process, starting from baseline capture and telemetry-only observation, moving toward active features like tool parallelism, Adaptive Cache Governor (ACG) integration, and adaptive hint consumption. The process emphasizes safety, requiring validation of tool idempotency and request stability before enabling automated scheduling or caching optimizations.
What it does
- Configures adaptive plugin components including state backends, telemetry, and rollout policies.
- Provides a step-by-step rollout sequence to ensure performance improvements are measured against a baseline.
- Supports integration of adaptive hints, tool parallelism, and Adaptive Cache Governor (ACG) diagnostics.
- Offers guidance on selecting appropriate state backends like in-memory or Redis.
- Defines failure modes to prevent regressions in application correctness or stability.
- Facilitates the consumption of adaptive outputs within application logic via standardized hint semantics.
When to use it
- When you have baseline NeMo Relay instrumentation and need to improve latency or request behavior.
- When you want to implement adaptive features like tool parallelism or prompt-cache planning.
- When you need to transition from observation-only telemetry to active adaptive tuning.
- When you need to configure adaptive hints or ACG diagnostics for your application.
Advantages
- Enables data-driven performance tuning based on runtime signals.
- Promotes a safe, incremental rollout strategy to minimize production risk.
- Standardizes configuration across different language bindings (Python, Rust, Node.js).
- Provides clear guardrails for sensitive operations like tool scheduling and cache planning.
Usage examples
- “How do I start tuning my NeMo Relay application's adaptive behavior?”
- The agent will guide you to capture a baseline, enable telemetry with in-memory state, and validate your configuration before enabling active features.
- “I want to enable tool parallelism in my NeMo Relay plugin. What should I check first?”
- The agent will instruct you to verify tool idempotency and race behavior, and recommend starting in observe_only mode before enabling scheduling.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/nemo-relay-plugin-adaptive-tuning/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nemo-relay-plugin-adaptive-tuning.zip -d .agents/skills
Documentation by NVIDIA Corporation and Affiliates
Tune Adaptive Plugin Behavior
Use This When
Use this skill when a user has baseline NeMo Relay instrumentation and wants to improve latency, parallelism, prompt-cache behavior, or model-request behavior from runtime signals. Keep adaptive behavior measured against a known baseline.
Do Not Use This When
Do not use this skill when the application is not instrumented yet. Start with
nemo-relay-instrument-calls or nemo-relay-get-started first.
Default Guidance
Observe first, compare against a baseline, then enable one behavior change at a time.
Use the adaptive plugin component rather than inventing separate tuning logic or hand-registering adaptive behavior at every call site.
Start with in-memory state and telemetry-only behavior for local development.
Move to persistent state only when learned signals must survive restarts or be shared across workers.
Add active behavior only after representative runtime events show what should change.
Embedded Adaptive Model
Adaptive behavior is configured through the first-party plugin component with kind
adaptive.Adaptive requires existing NeMo Relay scopes and at least one relevant managed tool or LLM lifecycle event stream because it learns from runtime signals.
Main configuration areas are state, telemetry, adaptive hints, tool parallelism, Adaptive Cache Governor (ACG), and rollout policy.
State backends are
in_memoryandredis.Tool-parallelism modes are
observe_only,inject_hints, andschedule.Adaptive Cache Governor providers are
passthrough,anthropic, andopenai; omit ACG until prompt-cache planning is needed.Helper APIs exist in Rust
nemo_relay_adaptive, Pythonnemo_relay.adaptive, and Node.jsnemo-relay-node/adaptive. Go and raw FFI are source-first or advanced surfaces.
Default Path
Use this rollout sequence:
Confirm the app emits scope events and the managed tool or LLM events needed for the behavior being evaluated. Do not require both call types when the workflow uses only one.
Capture a baseline for the workflow you want to improve.
Enable adaptive telemetry with in-memory state.
Read
references/config.mdwhen exact plugin configuration fields are needed.Run representative traffic and inspect reports or runtime events.
If configuration validation fails or expected events are absent, return the diagnostics and stop. Keep the last known working configuration active.
Before enabling scheduling, verify tool idempotency and race behavior. Before enabling ACG, verify that provider request payloads are stable.
Enable the smallest behavior change in config.
Read
references/hints.mdwhen application logic consumes adaptive hints, tool-parallelism guidance, or ACG diagnostics.Compare results against the baseline. If latency, correctness, or failure rate regresses, restore the last known working configuration and retain the sanitized diagnostics for review.
Failure Modes To Avoid
Do not enable scheduling before tool idempotency and race behavior are known.
Do not enable prompt-cache planning before provider payloads are stable.
Do not treat adaptive hints as mandatory instructions unless the consuming path explicitly defines that contract.
Do not use environment variables as the primary adaptive configuration model.
Do not tune from a single run or unrepresentative traffic.
Do not suppress or replace original tool and model errors.
Do not add retries until the call owner defines their safety.
Revert adaptive behavior when it increases the failure rate.
Load A Reference When
You need the exact adaptive config shape ->
references/config.mdYou need to consume adaptive hints or scheduling guidance in app logic ->
references/hints.md
Use Another Skill When
You need to build reusable plugin behavior instead of configuring the built-in adaptive component ->
nemo-relay-plugin-build
Related Skills
nemo-relay-get-startednemo-relay-instrument-callsnemo-relay-plugin-observabilitynemo-relay-plugin-build
Files
- ATTRIBUTION.mdAdded by AutoGineer984 B
- BENCHMARK.md3.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md4.4 KiB
- evals/evals.json16.5 KiB
- references/config.md3.3 KiB
- references/hints.md2.7 KiB
- skill-card.md4.0 KiB
- skill.oms.sig5.0 KiB
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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 Node.js
Security
What our scanner noticed· Medium risk
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Attribution and licence
- Created by
- NVIDIA Corporation and Affiliates
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/nemo-relay-plugin-adaptive-tuning · 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 Corporation and Affiliates or NVIDIA.
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