Top-level skill selector for ambiguous tasks involving NeMo Platform (NVIDIA's agent platform). Picks the right downstream skill for setup, design, specification, agent configuration, build, deployment, testing, observability, status, teardown, evaluation, optimization, security, or model customization. Use when the user needs help deciding where a NeMo Platform task should start.
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name
nemo-skill-selection
description
Top-level skill selector for ambiguous tasks involving NeMo Platform (NVIDIA's agent platform). Picks the right downstream skill for setup, design, specification, agent configuration, build, deployment, testing, observability, status, teardown, evaluation, optimization, security, or model customization. Use when the user needs help deciding where a NeMo Platform task should start.
triggers
["build an agent","create an agent","deploy an agent","set up nemo","install nemo","try nemo","improve my agent","help me with nemo","nemo platform","shut down nemo","tear down nemo","what is running on nemo","help me ship an agent"]
not-for
["setup (use to verify install or to be told how to run the CLI install)","nemo-build-agent (use for the actual scaffold/deploy flow)","nemo-explore (use to reason about agent design)","superpowers:brainstorming (use for design work unrelated to NeMo Platform)","running downstream workflow or state-changing platform commands (each downstream skill owns its own commands)","loading multiple downstream skills in one turn"]
compatibility
nemo-platform >= 0.1.0; selection plus a host scan on macOS or Linux; works without an installed CLI (selector can pick setup, which then tells the user how to run the CLI install).
maturity
active
license
Apache-2.0
user-invocable
true
NeMo Platform skill selection
Decide which downstream NeMo Platform skill should run. Bash access is unrestricted at runtime and
can execute state-changing commands; the scope below is a behavioral constraint, not an enforced
allowlist. Execute only the host scan in this skill's Pre-flight section, then announce the choice
and hand off. Never run downstream workflow or state-changing platform commands from this skill.
New NeMo Platform agent builds use a Platform-managed agent.yaml with
config_format: nemo-agents-spec-v1 and a supported harness. NVIDIA NeMo Agent
Toolkit (NAT) workflow YAML remains a compatibility path. Do not describe NAT
as the only supported implementation model.
If an existing agent does not fit a supported harness contract, route based on
the user's goal: preserve an existing NAT workflow, identify a custom adapter,
or use nemo-agent-config for a best-effort migration. Do not promise that an
arbitrary Python entrypoint can be converted mechanically.
Decision table
Match the user's intent to one downstream skill. Pick exactly one.
Verify the platform is installed and running. If not, the skill tells the user how to run the CLI install (make bootstrap + nemo setup). Install itself is CLI-only.
"design an agent", "I want an agent that handles X", "what should my agent do"
nemo-explore
Capture the agent's job, audience, categories, tools, model, constraints before any code
"write the ethos", "save the design", "capture what we agreed"
nemo-ethos
Persist the explore answers as agents/<name>-ethos/ETHOS.md
"write agent.yaml", "validate agent.yaml", "choose a harness", "migrate this NAT YAML", "convert to nemo-agents-spec-v1"
nemo-agent-config
Author or migrate the Platform-managed machine-readable config without running the full build
"build the agent", "create the agent", "deploy", "scaffold from ethos"
nemo-build-agent
Build from the approved Ethos, default to Platform agent.yaml, register, deploy, evaluate, and optionally apply guardrails
"ask my agent", "try the agent", "test it", "invoke this agent.yaml"
nemo-try-agent
Invoke a named deployment or run a local agent YAML config once
"instrument my agent", "send traces", "use Intake", "agent observability", "query spans or traces"
nemo-intake
Choose an ingest path, instrument the source, ingest telemetry, and verify spans, traces, sessions, or evaluator results
"create an experiment", "publish evaluation runs", "evaluation leaderboard"
nemo-experiments-upload
Create Experiments and Evaluations, ingest their telemetry and scores, and verify leaderboard rollups
"status", "what is running", "platform health", "is the platform up", "what's deployed", "show me what's running"
Stop the cluster (keep data, delete platform data, or full cleanup)
"fine-tune", "customize the model", "train on my data", "SFT", "LoRA"
nemo-customizer
Model customization via installed customization contributor plugins (nemo-customizer-plugin). Requires plugin skills to be installed (nemo skills install / enabled-plugins).
"why does my agent keep failing", "analyze my agent's traces", "find recurring failure patterns", "generate insights for my agent"
nemo-analyst (plugin-owned, in plugins/nemo-insights)
Reads an agent's existing telemetry and files each recurring failure pattern as an Insight citing the traces that evidence it. Requires the Insights plugin; produces the Insight nemo-experimentalist acts on.
"improve the agent's own code", "fix my agent harness", "candidate code change", "optimize from an Insight", "improve on train and validation datasets"
nemo-experimentalist (plugin-owned, in plugins/nemo-experimentalist)
Source/harness optimization: generate and validate candidate code changes against Harbor-compatible evaluation data. Requires the Experimentalist plugin; use after agents analyst has created an Insight, or with explicit datasets.
"optimize my agent", "make it cheaper", "reduce latency", "smaller model", "switchyard", "routing split", "compare against a newer model"
agents-optimize (plugin-owned, in plugins/nemo-agents)
Cost / latency / quality optimization for a deployed agent. Routing splits, skill tuning, prompt tuning, new-model scans.
"secure my agent", "harden my agent", "check for PII", "leaked secrets", "guardrail coverage"
agents-secure (plugin-owned, in plugins/nemo-agents)
Safety and security audit for a deployed agent. Guardrails, PII, secrets scan.
"evaluate my agent", "run a benchmark", "eval suite"
nemo-evaluator (plugin-owned, in plugins/nemo-evaluator)
Evaluation metrics, LLM-judge, benchmark jobs against a deployed agent or model.
Optimize vs build: Do NOT route optimize asks to nemo-build-agent. Build is for creating new agents from a spec. Use agents-optimize for a deployed agent's routing, prompts, skills, cost, or latency; use nemo-experimentalist when the requested improvement changes the agent's own source or harness and is evaluated with candidate code changes. If the user says "make my agent faster" or "use a cheaper model," that is agents-optimize, not nemo-build-agent.
If a request includes both config authoring and deployment, choose
nemo-build-agent; it delegates the config portion to nemo-agent-config.
Choose nemo-agent-config when the requested output stops at a validated config
or migration. Otherwise, if two rows fit, pick the earliest one in the
lifecycle. If nothing matches, ask one disambiguating question with the
relevant rows as a numbered list.
Pre-flight
Before handing off, run a host-wide platform scan. Three signals, in order — the first one that fires wins:
# 1. Ground truth: is anything listening on the canonical port?
lsof -iTCP:8080 -sTCP:LISTEN 2>/dev/null
# 2. Functional check: does the platform readiness endpoint answer?
curl -sS --connect-timeout 2 --max-time 5 http://localhost:8080/health/ready -o /dev/null -w "%{http_code}\n" 2>/dev/null || echo"no-response"# 3. Conflict check: other platform processes / data dirs / configs on this host?
ps -eo pid=,user=,comm=,args= 2>/dev/null \
| awk '$0 ~ /[n]emo services (run|start)|[n]emo-platform run/ {print $1, $2, $3}'ls -d ~/.local/share/nemo* 2>/dev/null
ls ~/.config/nmp*/config.yaml 2>/dev/null
Interpretation:
What you observe
Hand off to
Why
(1) returns a listener AND (2) returns 200
the requested downstream skill
Platform is up and ready. Skip setup.
(1) returns a listener but (2) returns no-response or non-200
nemo-status
Something is bound to :8080 but the platform is not ready. Do not start a second platform.
(1) empty but (3) finds another nemo services process OR more than one data dir / config
stop, do not hand off yet
Another install on this host, possibly on a different port. Surface only the redacted PID, user, and executable inventory emitted above. Ask whether to tear that one down first, pick a different port + data dir, or abort. Two installs writing to the same ~/.config/nmp/config.yaml is how users end up with one Studio frontend pointing at the wrong backend.
(1), (2), and (3) all empty
setup
Clean machine, no platform installed.
Read-only callers (this skill, nemo-status, the build/try pre-flights) should not trust nemo services status or nemo services ls as an up-check. Both report stale "running" from a held instance lock after the underlying process has died. The lock reconciles automatically the next time nemo services run is invoked, but until that happens, lsof is ground truth. (Tracking a CLI-side fix for this so we can drop the workaround from skills.)
What to announce
Tell the user, in one sentence, which skill is next and what it will do. For example: "Handing off to setup to verify the platform is installed and running. If it isn't, the skill will tell you the CLI command to run; install is a 5-minute shell step that this skill cannot do reliably for you."
Then hand off. Do not run downstream workflow or state-changing platform commands from this skill.
If nothing matches
If the user's intent doesn't fit any row, do not guess. Read out the available skills and ask which one they want:
NeMo Platform skills I can route to:
setup verify install or get the CLI install command
nemo-explore design conversation: capture goal, audience, tools, constraints
nemo-ethos write the design to agents/<name>-ethos/ETHOS.md
nemo-agent-config author, validate, or migrate Platform agent.yaml
nemo-build-agent build from the Ethos, register, deploy, evaluate, and sign off
nemo-try-agent invoke a named deployment or local agent YAML config
nemo-intake instrument agents, ingest/query telemetry, attach scores
nemo-experiments-upload publish named evaluation runs to an Experiments leaderboard
nemo-status read-only platform health dashboard
nemo-teardown guided shutdown
Plugin-owned skills:
agents-optimize cost / latency / quality optimization for a deployed agent
agents-secure safety and security audit for a deployed agent
nemo-evaluator evaluation metrics, LLM-judge, benchmark jobs
nemo-customizer fine-tuning of models
nemo-analyst analyze agent telemetry and file recurring problems as Insights
nemo-experimentalist source/harness optimization from Insights or evaluation datasets
guardrails content-safety middleware via virtual models
auditor red-team vulnerability scanning (garak)
data-designer synthetic dataset generation
anonymizer PII handling for datasets
Which one fits what you're trying to do?
For things outside this catalog (for example, "show me how Switchyard routes between models"), point at the relevant repo skill (nemo-evaluator, nemo-auditor, etc.) or tell the user no skill claims that intent yet. Do not invent a path.
If the pre-flight finds no platform but the user insists they have installed one: ask them to report
the output of lsof -iTCP:8080 -sTCP:LISTEN and the redacted scan below from the shell where they ran
setup. The platform may be bound to a non-default port, or the install may be in a venv whose nemo
binary is not on PATH.
Skills route through CLI commands, not Studio. But customers ask "what's Studio?" or "do you have a web UI?" Answer honestly, do not invent capabilities, and do not steer users into the experimental flows.
What to say:
Studio is the NeMo Platform web UI. When the platform is running locally, it serves at http://localhost:8080/studio.
Documentation: docs/studio/index.md in this repo covers the stable views (Agents, Optimizations, Monitor, Workspaces, Datasets). Point users there rather than enumerating features in-conversation — the docs stay up to date, this skill won't.
Honest caveats to flag every time:
The Optimizations "Apply suggestion" flow is incomplete today. Suggestions render, but the apply action is not reliable end-to-end. Tell the user to apply optimizer suggestions via the CLI (nemo agents …) instead, using the suggestion's apply block as the source of truth — see agents-optimize.
Other views may evolve; refer to the docs for the current state rather than promising specific behavior.
For local development on Studio itself, the source lives at web/packages/studio/. The studio-dev skill (if available) covers that workflow.
Do not proactively suggest Studio as the path for anything a skill already covers (chat, deploy, status, teardown, optimization). The CLI path is what these skills verify and what we can confidently support.
Gotchas
One skill at a time. Do not load more than one downstream skill in the same turn. Each downstream skill is a full procedure with its own context budget.
Install must happen before any skill can do useful work. Build, try, and status all assume the platform is up. If the user has not run the CLI install (make bootstrap + nemo setup), the skills cannot work around that; hand them to setup for instructions.
NeMo Platform is the product name. Capital N, e, M, o, P. Not "nemo" or "Nemo." NAT on first mention is "NVIDIA NeMo Agent Toolkit (NAT)."
Model customization goes to the nemo-customizer plugin skill when nemo-customizer-plugin (and a training backend) are installed. If that skill is not available, tell the user to enable customization plugins and install skills — do not improvise training with an external library.
Execution compatibility. New Platform configs must select a supported
harness. Existing NAT workflows may remain on the NAT compatibility path.
For another framework or an arbitrary Python entrypoint, inspect whether a
supported harness owns its lifecycle; otherwise identify a custom adapter or
NAT wrapper instead of claiming direct support.
