Captures what a NeMo Platform agent should do before any code or YAML. Explores the user's codebase and docs first, fills in every spec field it can infer, then asks the user only for the gaps. Output feeds nemo-spec. Use over generic brainstorming for any NeMo Platform agent design conversation.
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name
nemo-explore
description
Captures what a NeMo Platform agent should do before any code or YAML. Explores the user's codebase and docs first, then asks one intent question at a time for what source cannot supply. Output feeds nemo-ethos. Use over generic brainstorming for any NeMo Platform agent design conversation.
triggers
["design my agent","what should my agent do","help me think through the agent","I want to build an agent","agent design","explore the agent","figure out what my agent needs","bootstrap AGENT_DESCRIPTION","onboard my existing agent"]
not-for
["nemo-skill-selection (use to dispatch when intent is unclear)","nemo-ethos (use to write the Ethos file once explore is done)","nemo-build-agent (use after the Ethos exists)","nemo-model-selection (use for the model question in step 5; explore delegates to it)","superpowers:brainstorming (use for design work unrelated to NeMo Platform)"]
preconditions
["nemo_cli_available"]
compatibility
nemo-platform >= 0.1.0; dialogue-driven with read-only pre-flight (`ls`, `find`, `Read`); one intent question per message after the codebase scan; at least three questions covering Purpose & Outcomes, Principles, and Vision; safe under any sandbox; works offline; output is a structured conversation handed to nemo-ethos.
maturity
active
license
Apache-2.0
user-invocable
true
allowed-tools
["Read","Glob","Grep","Bash"]
NeMo Platform agent explore
Capture what the agent should do before any code or YAML. Product mission and
user goals matter more than implementation inventory. The output of this skill
is the data that nemo-ethos writes into agents/<name>-ethos/ETHOS.md —
the durable contract that downstream optimization agents read as their primary
context. Underspecified input here directly degrades the quality of generated
Insights and PRs downstream.
This skill is explore first, then a mandatory intent interview. Scan the
codebase and docs, infer implementation-shaped fields, then ask the user for
intent source cannot own. Do not skip the interview because the scan looked
complete, and do not skip a question because you think you already know the
answer. Do not dump the draft Ethos until that interview has asked at least
three questions and received a reply to each.
The division of labor is: infer implementation, ask for intent. Almost
everything about what the agent is can be read from source. Nothing about what
the developer wants can be. Constraints, trade-offs, principles, mission, and
change permissions live in the user's head, so they are the questions worth
spending attention on.
The schema you are filling
The Ethos has five base front-matter fields, plus optional updated_timestamp,
and fifteen body sections. Every body section is required. One field is a
quality gate for handoff: nemo-ethos is blocked until Role is concrete.
For any other canonical section with nothing to say, write _(none)_ rather
than dropping the heading.
The fifteen headings are a floor, not a ceiling. Extra ## headings and extra
YAML front-matter keys are allowed. Keep them if the user adds them. Do not
strip custom sections to make the file look "strict."
Front matter
Field
Required
Guidance
schema_version
yes
Always 1 for new files. nemo-ethos fills this at write time.
name
yes
Canonical agent name. Use the directory or workflow name if obvious; ask if not.
created_timestamp
yes
ISO 8601 timestamp for when the Ethos is created. nemo-ethos fills this at write time.
author
yes
Human or agent that created the Ethos. nemo-ethos fills this from the current author context when known; ask only if ambiguous.
owner
optional
Accountable human or team for the approvals named in Constraints or Change Scope. Ask only if those sections name an approval.
updated_timestamp
conditional
Set on edits. Omit on first write. nemo-ethos fills this.
Body sections (in canonical order)
#
Section
What "good" looks like
1
Role
One concrete sentence describing the role this agent plays. Example: "answer IT helpdesk questions about VPN, password reset, and software access." Vague answers ("help with stuff") are useless downstream.
2
Purpose & Outcomes
Two labeled parts. Mission: why the agent exists, what user value it provides, and the product or workflow context it serves — not a restatement of implementation mechanics. Outcome: the external result it is accountable for, with the measurable target and who owns that number. A mission with no outcome cannot be optimized; an outcome with no mission gets optimized in the wrong direction. Say so plainly when the agent is internal tooling with no business metric.
Tools, APIs, and knowledge sources the agent can use, or "Prompt-only." Group related helpers by capability or source. Capture only behaviorally important purpose, credentials/scopes, side effects, freshness, and expected failures.
5
Harness
How this agent actually runs: the loop, tool use, and runtime. Write what is true of this agent. Do not pick a named platform harness, and do not treat a framework import as a requirement. Write _(none)_ if you cannot describe how it runs.
6
Behavior
Behavioral rules and boundaries: refusal/escalation policy, tone, safety/compliance requirements, accepted limitations, and known non-goals. Hard external limits belong in Constraints.
7
Principles
How the agent should decide when no rule in Behavior covers the case: which way to err on an ambiguous request, what it protects even at some cost to the answer, and whose interest wins when the user and the business disagree. Two or three concrete judgment calls. "Helpful, harmless, and honest" is not an answer — it is true of every agent. Write _(none)_ if there is no judgment call beyond Behavior.
8
Success Criteria
What good production behavior looks like, independent of current evals: mission-level outcomes, quality standards, escalation quality, accuracy expectations, and examples of success. Rank them when some matter more.
9
Trade-offs
How to choose when two improvements conflict. Needs three things: hard gates never traded away, a priority order over the rest (quality, latency, cost, reliability), and regressions that are unacceptable even alongside a headline win. "Balance quality and cost" is not an answer. Write _(none)_ if the user cannot rank them.
10
Constraints
Hard external bounds no change may cross: approved providers/models/regions, data residency and handling, compliance obligations, production cost ceilings and latency SLOs, and changes that need human sign-off. Give the current measured figure next to a ceiling when you know it. Usually organizational, which is why the code cannot supply them. Write _(none)_ if unconstrained.
11
Evaluation Setup
Current validation setup: how to run it, what datasets/checks it uses, what scorers/metrics measure, pass/fail thresholds, and known coverage gaps relative to the success criteria. If no eval suite exists, say so explicitly.
12
Metric Semantics
What ambiguous or load-bearing metric and telemetry field names actually mean, and which claims they do not support. Write _(none)_ when every name means exactly what it says.
13
Change Scope
A permissions list — what may be modified. Each lever takes yes, no, or with-approval. Name levers that exist on this agent. Do not copy a platform catalog. The loop never edits the Ethos itself.
14
Vision
Where the agent is headed: an intention beyond today's job, plus one or two concrete use cases it should grow into but does not serve yet. That last part marks what Scope excludes for now rather than on principle. Write _(none)_ rather than pasting a dated backlog.
15
Open Questions
Open facts that affect safe use, evaluation, or modification of the agent. Write _(none)_ when there are none. Remove items once answered.
Known issues / failure patterns are tracked as first-class Insight entities by
the insights plugin — do not duplicate them into the Ethos.
Pre-flight
Check whether an Ethos already exists for this agent. If agents/<name>-ethos/ETHOS.md
is present, ask the user whether they want to edit the existing Ethos or start
over. If they want to edit, route to nemo-ethos directly.
ls agents/*-ethos/ETHOS.md 2>/dev/null || echo"no ethos yet"
If agents/<name>-spec/AGENT-SPEC.md exists, the agent still has a spec
package. Read that file as prior answers. Scan the codebase, then still run
the intent interview for anything the spec never answered. Hand those
answers to nemo-ethos so it writes agents/<name>-ethos/ETHOS.md.
ls agents/*-spec/AGENT-SPEC.md 2>/dev/null && echo"spec package present"
After the Ethos is uploaded and the user confirms it, copy remaining package
files such as agent.yaml into agents/<name>-ethos/. Do that when those
files still live only in the spec package. Confirm, then delete
agents/<name>-spec/ and the <name>-spec Fileset:
nemo files filesets delete "${NAME}-spec"
Do not start a greenfield explore unless the spec is missing answers you
still need.
Step 1 — Explore the codebase
Time-box this to ~5 minutes of tool use. Read first, ask second. Greenfield
projects will turn up nothing here, which is fine — move to the intent
interview and ask for Role first.
Find agent definitions and entry points. Look for Platform
agent.yaml, NAT workflow YAMLs, Python agent builders, system prompts,
skills, and tool definitions:
Then use Glob / Grep to find nemo-agents-spec-v1,
langgraph, StateGraph, create_react_agent, system_prompt, skills,
MCP servers, and tool definitions. Treat any harness or framework name you
find as a clue about how the agent runs, not as a value the Ethos must pick.
Find design context. Look for README.md, AGENTS.md,
product/design/planning docs, launch notes, and anything in docs/. Read
docs that describe goals and user value before implementation details when
they look agent-relevant.
Map findings to schema fields. As you scan, hold a running mental
table of what you can fill from the code/docs. Be honest about confidence:
"inferred from system prompt" is different from "confirmed by the user."
Choose the model. Hand off to nemo-model-selection after the
code/docs scan. That skill profiles the agent on tool density, primary
capability, and deployment, then recommends a specific NIM model with a
plain-English explanation grounded in what the model is actually good at.
Return here with the chosen model string, which nemo-build-agent writes
into agent.yaml. The Ethos has no Model section — record the permitted
providers and model families in Constraints instead, since the config
already carries the model in use and it changes without touching the Ethos.
If the user wants to skip the conversation, the default is cloud,
nvidia/llama-3.3-nemotron-super-49b-v1 — announce that and move on.
Local NIMs require host-gpu mode.
Typical inferences per field:
name — directory name, workflow name, or top-level package name.
Role — first paragraph of README, system prompt preamble, or
top-level docstring. Often partial; usually needs user confirmation.
Purpose & Outcomes — mission from product docs, README motivation,
system prompt preamble, or workflow context. Treat that as a draft to
confirm, never as a skip. The outcome (the business objective) is rarely
in code; even when a README names a metric, ask the user to confirm it.
Scope — audience from docs or prompts; categories from enumerated
capabilities or named tool clusters; in/out boundaries from prompt rules.
Tools — from @tool decorators, NAT tool registry,
create_react_agent(tools=[...]), retrieval/corpus config, or API clients.
Group low-level helpers when they share credentials, side effects,
freshness, and failure modes.
Harness — infer from how the agent actually runs: adapter
configuration, workflow YAML, service entrypoints, CLI commands,
Dockerfiles, notebooks, or deployment configs. Capture behaviorally
relevant capabilities, not a catalog name. If you cannot see how it runs,
write _(none)_.
Behavior — system prompt rules ("never give medical advice"),
refusal/escalation policy, tone, accepted limitations, and non-goals.
Success Criteria — desired production outcomes, product goals,
quality standards, escalation quality, accuracy expectations, and examples
of successful behavior.
Trade-offs — not in the code; ask in the intent interview.
Constraints — mostly not in the code, but scan for partial evidence
worth confirming: a pinned gateway base URL or provider allowlist, region
settings, redaction or PII middleware, timeout and token ceilings, and
compliance notes in docs. Treat findings as a starting draft to confirm,
never as the complete list.
Evaluation Setup — Makefile targets, scripts, CI config, eval YAMLs,
metric definitions, thresholds, and coverage notes.
Metric Semantics — from scorer definitions, metric names in eval
configs, and telemetry field names. Fill only the entries whose meaning is
genuinely ambiguous from the name. Write _(none)_ when every name is
obvious.
Change Scope — not in the code; ask the user.
Principles — not in the code; ask in the intent interview even if a
prompt hints at one. A prompt is the implementation, not the intent
behind it.
Vision — not in the code. Roadmap docs, design notes, or a README's
future-work section are speculative until the user confirms them. Ask.
Write _(none)_ rather than guessing.
Open Questions — TODOs / FIXMEs in agent-adjacent code that
affect safe use, evaluation, or modification.
Step 2 — Ask intent questions
The scan told you what the agent is. It cannot tell you what the developer
wants. Run a real interview for that intent before you show a draft Ethos.
This step is a hard gate. Do not present the full draft, and do not hand off
to nemo-ethos, until you have asked at least three questions and received a
reply to each. "The scan filled everything" and "asking feels unnecessary"
are not reasons to skip it.
What to ask
After the scan, split fields into two piles:
Inferred — implementation-shaped fields you can draft from source:
Tools, Harness, Evaluation Setup, Behavior copied from prompts,
Metric Semantics from scorer names.
Intent — fields source cannot own: Purpose & Outcomes, Principles,
and Vision always; then Constraints, Trade-offs, Change Scope, and
a ranked Success Criteria when the repo only has eval wiring.
Walk the intent pile. Skip a topic only when this conversation already
answered it. A README, prompt, or roadmap is not a substitute for a user
reply on the always-ask topics below. Keep every other unanswered intent
topic on the list. Greenfield work adds Role, Scope, and whether the
agent is prompt-only before that intent list.
Question count is at least three, then however many remaining intent topics
still need a reply. Prefer fewer sharp questions over a long checklist, but
never go below three.
Always ask these three
Code and docs can look like they already answered them. They did not, until
the user says so. Ask even when you have a high-confidence draft. Confirming
an inference counts; silently filling the section does not.
Purpose & Outcomes (business objective). Confirm the mission and the
result the agent is accountable for. If you inferred a metric from a
README, show it and ask whether that is the target.
Principles. The judgment call when Behavior runs out. Reject
generic virtues. Ask what this agent should do that a careless version
of it would not.
Vision. Where the agent is headed, or an explicit _(none)_. Do
not copy a future-work section into Vision without asking. Speculative
vision is the usual failure.
If Role is missing or vague, ask that first so the rest of the interview
is grounded. It does not replace one of the three always-ask topics.
Ask about remaining topics in this order, skipping any this conversation
already answered:
Constraints — hard bounds no change may cross.
Trade-offs — hard gates, then a ranking.
Change Scope — which levers on this agent may move.
Ranked Success Criteria when the repo only has eval wiring.
Do not collect run limits. A per-experiment spend cap belongs to the
optimizer. If the user volunteers one, keep the standing policy and drop
the number.
How to ask
Follow this Q&A pattern:
One question per message. If a topic needs more depth, ask a
follow-up in the next message. Do not batch four intent questions.
Prefer multiple choice. Ground the options in what you found.
Always include a way to reject the list (Something else or I don't know). Open-ended is fine when a lettered list would fake certainty.
Wait for the reply before the next question.
Accept "I don't know" and move on. Write _(none)_ and record the
gap in Open Questions. A fabricated constraint is worse than a missing
one.
Confirm inferred bounds. If the scan found a pinned gateway or
redaction middleware, show it and ask whether it is a real boundary.
Confirm inferred intent. If the scan produced a plausible Purpose & Outcomes, Principles, or Vision, show the draft as an option. Do not
treat that draft as the answer.
Example (one message, then stop):
The README says this agent exists to "cut ticket volume." Is that the
business objective I should record in Purpose & Outcomes?
A. Yes — ticket volume is the outcome (tell me the target if you have one)
B. Close, but the real objective is … (tell me)
C. No business metric — internal tooling
D. I don't know
A later Constraints example:
The scan shows a pinned NVIDIA gateway and no cost ceiling in docs.
Which hard bounds should Constraints record?
A. Keep the gateway pin; no other bounds
B. Gateway pin plus a production cost or latency ceiling (tell me the number)
C. No hard bounds — write _(none)_
D. Something else
Reject generic virtues for Principles. "Helpful, harmless, and honest"
gives a downstream reader nothing. Ask for the judgment call: what does
this agent do that a careless version of it would not? If nothing comes
back, write _(none)_.
Push once on a non-answer for Trade-offs. "Balance quality and cost" is
not decidable. One concrete follow-up usually produces a ranking. If it
does not, record the ambiguity and move on.
If the user provides outside context, read it and update the inferred
draft before the next question.
Red flags — stop and ask
These mean you skipped the interview:
You are about to paste the full Ethos and have asked fewer than three
questions
You filled Purpose & Outcomes, Principles, or Vision from the scan
without a user reply
Several intent questions in one message
Handing off to nemo-ethos with fewer than three Q&A replies in this
conversation
Excuse
Reality
The scan filled everything
Code never owns intent. Ask at least three questions.
I already know Purpose / Principles / Vision
Those answers belong to the user. Confirm the draft.
Asking feels unnecessary
Unnecessary-looking questions are the ones that catch speculative Vision.
Batching questions saves turns
One question gets a real answer. A dump gets shallow ones.
The draft review can collect intent
Review is for corrections. Ask intent first.
The user looks busy
One short multiple-choice question is enough to start.
Step 3 — Present the draft Ethos
After the interview, present the entire Ethos at once — every field, with
inferred values shown inline and remaining gaps as _(none)_. Then ask
one question:
"Here's the full Ethos I'd write. Tell me what to change, and I need a
concrete Role before I can hand off to nemo-ethos."
Show the rendered Ethos inline in markdown (one ## section per field,
same shape as the on-disk file). For fields you defaulted, note the
default in parentheses so the user knows they can override:
Tools: Prompt-only.(default — say so if the agent needs tools)
Purpose & Outcomes / Success Criteria inferred from implementation
(say so if there is outside context to incorporate)
Change Scope: name the parts of this agent a change may touch
(default — call out anything you want to lock down, or mark it
with-approval)
When the user could not answer a section, write _(none)_ and add the
gap to Open Questions. Say "Constraints: (none) — a later optimizer
might treat every provider and cost as fair game" so the consequence is
visible while it is still cheap to fix.
Do not walk the schema field by field in this review. Do not restart the
intent interview here unless the reply surfaces a contradiction.
Do not use public-facing shorthand like AUT or "agent under test" in the
rendered Ethos. Use "this agent" for the agent being specified. Use
"target agent" only where the agent's purpose is explicitly to inspect or
modify another agent, and name optimizer helper agents only when they are
part of the actual product workflow.
Allowed follow-ups after the draft:
The hard-requiredRole is missing or vague — ask for it before
handoff.
The user's reply surfaces a contradiction that needs one targeted
clarification (for example they say "drop the search tool" but the
codebase shows the agent depends on it).
Step 4 — Hand off
After the user's reply, apply the corrections and check the one hard
precondition:
Role is a concrete one-sentence answer (not "help with stuff").
If it is still unresolved, ask for it in one final message and stop until the
user provides it. Do not hand off with Role blank — the artifact is useless
downstream even though the parser accepts it.
Every body section heading must be present before handoff. Write _(none)_
for honest gaps, and note them in Open Questions when they affect safe
optimization. Do not drop a heading.
If the Role quality gate is satisfied, announce the handoff in one line
("Handing off to nemo-ethos to write agents/<name>-ethos/ETHOS.md and upload
the canonical copy to Filesets") and trigger it.
If the user pushes back
They want to change one or two fields. Apply the edits, re-show the
changed sections only, ask "good now?", proceed.
They want to redo the whole thing. That usually means the codebase
scan got something fundamentally wrong. Re-scan with their correction in
mind, then re-present once.
They keep changing their mind on Role. Stop. Tell them the agent will
not be useful until they can write one concrete sentence and offer to
come back later. Do not loop on rewording.
They skip remaining intent questions. You already asked at least
three, including Purpose & Outcomes, Principles, and Vision. Treat
skipped topics as I don't know: write _(none)_, record them in
Open Questions, and say once what it costs. Then present the draft. Do
not re-open the full interview.
They say "just write it" before three questions. Ask the next
always-ask question anyway. After they answer or decline each of the
three, continue.
Gotchas
"You decide" means commit to the default and announce it. Example:
"I'll go with cloud and nvidia/llama-3.3-nemotron-super-49b-v1. Tell me
to change if not." Never silently fill in. Prefer routing through
nemo-model-selection so the user gets a plain-English reason, not just a
name.
Tool over-spec is the most common error. Users ask for a search tool
when prompt-only would work. Probe: "Do you have evidence the model alone
fails on these?" If no, drop the tool.
Tool and harness inventory should be compressed. Do not create one row
per helper method when several helpers share the same source, credential,
side effect, freshness, and failure mode. Group them and call out only the
differences an optimizer or evaluator needs to know.
Mission before mechanics. An Ethos that only says how the current code is
wired is not good enough. If goal context cannot be found in the codebase or
docs, say the mission is inferred from implementation and still ask the user
to confirm Purpose & Outcomes. A guessed business objective is worse than
_(none)_.
Never infer a constraint you cannot verify. A guessed provider allowlist
or cost ceiling silently deletes good candidates and looks authoritative doing
it. Ask, or write _(none)_ and record the gap in Open Questions.
Trade-offs are the highest-leverage question you will ask. Without a
priority order, every candidate that improves one metric and regresses another
is undecidable, and the optimizer either stalls or picks arbitrarily.
"No behavior constraints" usually means "I haven't thought about it." Probe
once: "Anything that should never appear — names, phone numbers, competitor
mentions?" One probe, then move on.
Do not skip the codebase scan even when the user seems eager to
answer questions. Spending the first five minutes reading makes the
interview shorter and sharper. Asking something the codebase already
answers loses trust immediately.
Do not skip the intent interview even when the scan looks complete.
Ask at least three questions. Always ask Purpose & Outcomes,
Principles, and Vision, even when you think you already know. One at
a time. Multiple choice when you can.
Do not invent Vision from a backlog. A future-work bullet is
speculative until the user confirms it. Ask, or write _(none)_.
A framework import is not a harness. Describe how the agent runs. Do not
map an import onto a platform harness name. Imports such as langchain,
langgraph, crewai, autogen, or pydantic_ai say nothing about that.
Keep Platform terminology at the design boundary. Record the desired
harness behavior and artifacts without exposing Fabric SDK types or asking
the user to design a raw runtime config. nemo-agent-config owns the
machine-readable Platform YAML after the Ethos is approved.
Change Scope is a permissions list, not a wishlist. It controls what
later optimization may edit. Walk the levers that exist on this agent so
the user knows what they're consenting to. Offer with-approval for levers
the user wants available but not automatic. Do not copy a platform catalog.
Do not invent Known Issues fields. Known issues / recurring failure
patterns live in the Insights plugin as first-class entities, not in the
Ethos.
