LLM Torch Profiler Analysis
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
Build, migrate, review, and maintain third-party NVIDIA NeMo Fabric adapters against the public adapter contract.
$ npx skills add NVIDIA/skills --skill nemo-fabric-build-adapter -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills nemo-fabric-build-adapter --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nemo-fabric-build-adapter .claude/skills/nemo-fabric-build-adapter && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "nemo-fabric-build-adapter" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-fabric-build-adapter into .claude/skills/nemo-fabric-build-adapter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-fabric-build-adapter", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/nemo-fabric-build-adapterType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill nemo-fabric-build-adapter -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills nemo-fabric-build-adapter --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/nemo-fabric-build-adapter .agents/skills/nemo-fabric-build-adapter && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "nemo-fabric-build-adapter" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-fabric-build-adapter into .agents/skills/nemo-fabric-build-adapter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-fabric-build-adapter", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill nemo-fabric-build-adapter -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills nemo-fabric-build-adapter --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/nemo-fabric-build-adapter .cursor/skills/nemo-fabric-build-adapter && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "nemo-fabric-build-adapter" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-fabric-build-adapter into .cursor/skills/nemo-fabric-build-adapter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-fabric-build-adapter", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/nemo-fabric-build-adapter--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill nemo-fabric-build-adapter -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills nemo-fabric-build-adapter --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/nemo-fabric-build-adapter .gemini/skills/nemo-fabric-build-adapter && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "nemo-fabric-build-adapter" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-fabric-build-adapter into .gemini/skills/nemo-fabric-build-adapter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-fabric-build-adapter", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills nemo-fabric-build-adapterInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill nemo-fabric-build-adapter -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/nemo-fabric-build-adapter .github/skills/nemo-fabric-build-adapter && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "nemo-fabric-build-adapter" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-fabric-build-adapter into .github/skills/nemo-fabric-build-adapter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-fabric-build-adapter", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill nemo-fabric-build-adapter -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills nemo-fabric-build-adapter --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/nemo-fabric-build-adapter .opencode/skills/nemo-fabric-build-adapter && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "nemo-fabric-build-adapter" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-fabric-build-adapter into .opencode/skills/nemo-fabric-build-adapter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-fabric-build-adapter", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
nemo-fabric-build-adapterBuild, migrate, review, and maintain third-party NVIDIA NeMo Fabric adapters against the public adapter contract.
Nemo Fabric Build Adapter is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Build, migrate, review, and maintain third-party NVIDIA NeMo Fabric adapters against the public adapter contract. Use when creating adapter or target descriptors, mapping AgentConfig into an agent harness or custom-agent runtime, implementing start/invoke/stop, declaring schemas and capabilities, packaging discovery metadata, or assessing adapter conformance. Do not use for consumer applications that only call the NVIDIA NeMo Fabric SDK.
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files (for example `BENCHMARK.md`, `agents/openai.yaml` and `evals/evals.json`).
It works with NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit dfdd080. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are toml and python).
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
github.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Nemo Fabric Build Adapter loads about 3.1k tokens when it runs. Until then it costs about 117 tokens; SKILL.md has 1,335 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from NVIDIA/skills at commit dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 1,335 words, ~3,147 tokens.
.claude/skills/nemo-fabric-build-adapter/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Build against the published southbound contract. Keep the adapter thin: let NeMo Fabric own planning and consumer-facing behavior, and let the adapter own only target translation and lifecycle state.
Read the current adapter contract before changing code. Start with the overview, choose an integration shape, then follow the numbered descriptor, configuration, execution, results, registration, and verification stages. Read the custom-agent page when the target loads application-defined agents or workflows. Read the optional native OpenAI streaming page only when the adapter claims that capability.
Use the committed adapter-contract JSON Schemas or the schemas installed with the matching NeMo Fabric release for exact wire shapes. Do not reconstruct a schema from examples or copy field lists into adapter code.
Establish the adapter boundary before defining its descriptor:
adapter_id.harness.settings, per-target workflow.settings,
and typed extensions.If the requested behavior cannot be expressed by the current contract, surface the gap. Do not silently consume an unsupported northbound field or hide it in an unrelated extension.
Create one self-contained *.fabric-adapter.json before implementing target
translation:
contract_version, a globally stable adapter_id,
adapter_kind, and runner binding.config.accepts fields the implementation enforces.instructions.system, declare the exact supported
config.system_instruction_modes. New descriptors must not rely on the
legacy omitted-value behavior, which means replace only.mcp.auth.oauth2 or mcp.auth.service_account only when the adapter
implements the corresponding MCP authentication mode.settings_schema, model_schema, tool_definition_schema,
and extension_schemas where applicable. Use
model_schema only for static model/provider compatibility and model settings;
keep credential validity and provider availability in startup validation.capabilities.streaming only
when the adapter implements native OpenAI Chat Completions streaming through
invoke_openai_stream. Relay-backed ATOF streaming is independent and does
not require this capability.If the adapter loads registered targets, list their types in target_types.
Create one *.fabric-target.json per target. The target record owns its
adapter_id, type-specific entry point, and workflow settings schema. It uses
the same contract_version as the Adapter Descriptor.
Validate descriptor schemas without importing adapter code. Keep all schema references local to the descriptor document; do not rely on HTTP or file references.
Install the descriptor in the standard shared-data location. For setuptools:
[tool.setuptools.data-files]
"share/nemo-fabric/adapters/acme" = ["acme.fabric-adapter.json"]
"share/nemo-fabric/targets/acme" = ["email.fabric-target.json"]Depend on nemo-fabric-adapter-contract for typed standard-library dataclasses.
Install its optional pydantic extra only for Pydantic interoperability. Add
nemo-fabric-adapters-common only if the adapter chooses its lifecycle or
Relay helpers. A bare adapter package should not depend on the NeMo Fabric
runtime.
For a TypeScript adapter, depend on
nemo-fabric-adapter-contract. Import descriptor, configuration,
runtime-context, request, and result types from the package root, matching the
Python package's single model namespace. TypeScript types do not validate data
received from a process or network boundary; validate untrusted values against
the JSON Schemas included with the package.
Accept a validated AgentConfig and translate each declared field once at the
adapter boundary:
instructions.system.mode at the adapter startup boundary as well as during
planning; replace discards the harness default, while append preserves it
and adds the configured content after it.start in
the task environment.RuntimeContext,
not from workflow settings.Reject unsupported values with stable, safe error codes. Do not log complete configs, environment values, headers, credentials, or arbitrary user input.
Use typed extension models and publish their schemas at the exact descriptor
extension point. Never treat extensions as an unchecked dictionary escape
hatch.
Implement exactly one start, zero or more ordered invoke operations, and
one stop for each NeMo Fabric runtime.
start.AgentRunRequest and RuntimeContext, then return one
AgentRunResult from invoke.stop safe after partial startup and failed invocation.capabilities.streaming, implement
async invoke_openai_stream(request, context, emit). Execute the target exactly once,
await emit(chunk) only for the openai.chat_completions.chunk/v1 profile,
and return one AgentRunResult. Each chunk requires
non-empty id and model, a nonnegative integer created, the exact
chat.completion.chunk discriminator, and structurally valid choices. An
invocation that emits no chunks is valid.Runtime.invoke_stream(); execute ordinary invoke and use the provided
telemetry context.For native OpenAI streaming, the SDK owns the authenticated loopback HTTP
transport with chunked NDJSON framing. The common host validates the transport,
removes its credentials from the adapter payload, and supplies the emit
callback. Do not persist or log stream credentials, write chunks to stdout, add
SSE framing, or forward other target-native event profiles.
For a Python adapter that opts into the common host:
from nemo_fabric_adapter_contract.models import AgentConfig
from nemo_fabric_adapter_contract.models import AgentRunRequest
from nemo_fabric_adapter_contract.models import AgentRunResult
from nemo_fabric_adapter_contract.models import AgentRunStatus
from nemo_fabric_adapter_contract.models import RuntimeContext
from nemo_fabric_adapters.common import lifecycle
class TargetRuntime:
async def start(self, payload):
config: AgentConfig = payload["config"]
...
async def invoke(
self,
request: AgentRunRequest,
context: RuntimeContext,
) -> AgentRunResult:
native = await self.target.run(request.input)
return AgentRunResult(
status=AgentRunStatus.SUCCEEDED,
output={"response": native.text},
)
async def invoke_openai_stream(self, request, context, emit):
async for chunk in self.target.stream(request.input):
await emit(chunk)
return AgentRunResult(
status=AgentRunStatus.SUCCEEDED,
output={"response": self.target.final_text},
)
async def stop(self):
...
def main() -> None:
lifecycle.serve(TargetRuntime, config_loader=AgentConfig.from_mapping)The common host decodes the internal lifecycle envelope before calling the
adapter and encodes its terminal result afterward. Adapter code does not parse
the transport envelope or infer failure from fields inside output.
Return AgentRunStatus.FAILED with an AgentRunError when the target completes
with a failed outcome. Raise an exception when the adapter cannot produce a
normalized terminal result.
For in-process Relay SDK telemetry where the adapter owns the invocation-level
Agent scope, wrap that scope with
nemo_fabric_adapters.common.utils.relay_request_context(context.request_id).
The helper uses a UUID request ID as Relay's propagated root and always returns
nemo_fabric_request_id metadata, including for non-UUID request IDs. Do not
apply this pattern to an external Relay gateway or an upstream integration that
creates an isolated scope context unless its boundary accepts a per-turn
propagation context.
For a shared framework adapter, select the registered target with
FabricConfig.workflow.target_id. Use the selected Adapter Target Descriptor's
spec.entrypoint.kind for adapter-scoped resolution semantics and ref for
the factory identity. Validate workflow.settings against that target's
spec.settings_schema.
factory intent to the corresponding target-native factory.
The current NeMo Agent Toolkit reference maps fabric.agent.react to its
ReAct workflow factory.FabricConfig
or AgentConfig.Compare the NeMo Agent Toolkit shared adapter with the dedicated LangGraph example before choosing the custom-agent boundary.
Complete these checks before handing off an adapter:
share/nemo-fabric and inspect the resolved adapter
and target descriptors in Fabric().plan(...).doctor(...) with both missing and satisfied requirements.Do not claim automated NeMo Fabric conformance until the published conformance suite exists and the exact adapter release passes it.
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 5 other files in skills/nemo-fabric-build-adapter of NVIDIA/skills.
Open the folder on GitHubat commit dfdd080
Nemo Fabric Build Adapter next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Nemo Fabric Build Adapter this skillNVIDIA/skills | 3.5k | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| LLM Torch Profiler Analysissgl-project/sglang | 37k | 2 repos | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Skill InspectorNVIDIA/SkillSpector | 20k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Megatron-LM Container and Dependency SetupNVIDIA/Megatron-LM | 18k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Embeddings via 9Routerdecolua/9router | 30k | — | ~604 | Automated safety check: Pass | MIT | |
| Megatron-LM Base Image BumpNVIDIA/Megatron-LM | 18k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 |
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
NVIDIA/SkillSpector
Decides whether an agent skill is safe to install by combining a SkillSpector static scan with the agent's own source review, ending in APPROVE, CAUTION or REJECT.
NVIDIA/Megatron-LM
Walks an agent through working inside the Megatron-LM CI container and changing dependencies with uv, so lock files resolve the same locally and in CI.
decolua/9router
Generates vector embeddings through the 9Router /v1/embeddings endpoint, using models from providers such as OpenAI, Gemini, Mistral and Voyage for RAG and semantic search.
NVIDIA/Megatron-LM
Moves Megatron-LM CI to a newer NVIDIA PyTorch base image, updating both the GitHub and GitLab pins together and handling the CI follow-up.
NVIDIA/NemoClaw
Remove bracketed NemoClaw tags from GitHub issue and PR titles.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Works with
Build, migrate, review, and maintain third-party NVIDIA NeMo Fabric adapters against the public adapter contract. Nemo Fabric Build Adapter is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Build, migrate, review, and maintain third-party NVIDIA NeMo Fabric adapters against the public adapter contract.
Nemo Fabric Build Adapter fits situations like: creating adapter; target descriptors; mapping AgentConfig into an agent harness; custom-agent runtime.
Run `npx skills add NVIDIA/skills --skill nemo-fabric-build-adapter -a claude-code`. Or copy the skill folder (skills/nemo-fabric-build-adapter in NVIDIA/skills) into .claude/skills/nemo-fabric-build-adapter in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill nemo-fabric-build-adapter -a codex`. Or copy the skill folder (skills/nemo-fabric-build-adapter in NVIDIA/skills) into .agents/skills/nemo-fabric-build-adapter in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill nemo-fabric-build-adapter -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nemo-fabric-build-adapter, .gemini/skills/nemo-fabric-build-adapter, .github/skills/nemo-fabric-build-adapter and .opencode/skills/nemo-fabric-build-adapter in your project.
SKILL.md names no scripts, command-line tools or credentials: Nemo Fabric Build Adapter is instructions for the agent only. Our summary lists: Python 3.
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Nemo Fabric Build Adapter is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.1k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Nemo Fabric Build Adapter: LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Skill Inspector (NVIDIA/SkillSpector, 20k stars), Megatron-LM Container and Dependency Setup (NVIDIA/Megatron-LM, 18k stars) and Embeddings via 9Router (decolua/9router, 30k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,546 GitHub stars. The repository holds 386 skills in this directory. The repository was last updated on October 9, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.