Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
日本語の概要は準備中です。原文の説明を表示しています。
Writes an AI agent's ethos.md — a durable snapshot of its purpose, intended behavior, boundaries, and success criteria — from the answers gathered by ethos-explore. Renders the schema-v1 template, validates the front matter and required markdown sections, saves the file in the agent's repository, and reviews it with the user. Also edits an existing ethos.md. Use over generic planning skills for an agent's ethos.md.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Turn the answers from ethos-explore into a durable artifact. The ethos.md is
the contract that people and agents read before they build, evaluate, review,
or change this agent. Without it, each of them has to re-ask everything, and
nothing records what the agent is supposed to do.
Creating or reviewing an ethos.md does not authorize application changes or imply
that changes will follow. Do not ask for permission to modify prompts, models,
tools, validation code, or workflow structure, or generate sections listing those
permissions. Any workflow that later changes the application
collects the permissions it needs when that work is undertaken. Keep behavioral
constraints and safety boundaries, including approvals the agent needs for its
own actions, in Behavior and Constraints. Also retain already-confirmed
organizational safety and review policies and their known owners; recording
such a restriction does not grant permission to make a change.
The ethos.md records the intended state of the agent, which is not the same as the implemented state. The codebase already shows what the agent does. This file is the only place that records what it is supposed to do, what it must never do, and how to weigh a win on one metric against a loss on another.
The ethos.md is a local file in the agent's repository, committed alongside
the code it describes. Use the path the user names. Otherwise reuse an
existing ethos.md, including an ETHOS.md written by Eval Author 0.1.0. Otherwise write
ethos.md at the repository root; in a repository with several agents, put it
at the root of the agent's own package and confirm that path with the user.
The file is the only copy. There is no remote canonical copy to sync.
Write schema_version: 1. Every canonical body section is required. The
validator rejects a file missing any of those headings. When you have nothing
to say, write _(none)_ rather than dropping the section.
Version 1 is the only schema version. When a new version ships, its migration
steps belong in this skill.
The schema is a floor, not a ceiling. Extra ## headings and extra YAML
front-matter keys are allowed. The parser keeps unknown body sections and
does not fail on unknown front-matter keys. If the user already added custom
sections, preserve them on rewrite. Do not strip custom content to make the
file look strict. This includes a legacy Change Scope section: preserve it on
edits unless the user asks otherwise, but do not treat it as authorization for
application changes in this workflow.
Do not invent content to look complete: _(none)_ in Constraints is honest,
while a fabricated bound actively misleads anyone changing the agent. If the user has no
answer yet, write _(none)_, record the gap in Open Questions, and move on.
ethos.md holds durable intent, so keep run-scoped configuration out of it. A spend
ceiling, an experiment count, or a wall-clock limit for a single evaluation
run belongs to the tool that runs it. If a user offers one, record
the standing policy it implies — a production cost ceiling in Constraints, or
who approves an overrun — and leave the run limit itself to that tool's config.
Before writing anything, the answers carried over from ethos-explore must
satisfy one non-negotiable. If it is missing or ambiguous, stop and route back
to ethos-explore for that field only — do not invent a default.
The parser cannot catch a vague Role, which is why this skill enforces it
upstream: the user sees a clear gap-question instead of a file that validates
and then helps nobody.
Confirm the agent name. Short and recognizable: it-helpdesk,
support-triage, code-reviewer. If the user has not named it, propose
two options based on the role.
Pre-flight: locate the file. Resolve ETHOS_PATH as described in
Location. If a file already exists there, ask the user whether
to edit it or start over. Read an Eval Author 0.1.0 ETHOS.md as the existing ethos.md.
find . -maxdepth 4 -iname ethos.md -not -path '*/.git/*' -not -path '*/node_modules/*' 2>/dev/null | grep . || echo "ethos_new"
Run a focus check before rendering. The carried-over answers should be mission-led and reviewable, not a raw inventory of implementation details:
Purpose & Outcomes and Success Criteria must explain mission, user
value, the measurable result, and the success bar. If they only summarize the
current code, route back to ethos-explore to ask whether the user has
outside context that is not in the codebase. If no such context exists,
say the section is inferred from implementation.Trade-offs must be decidable. "Balance quality and cost" is not usable;
a priority order with named hard gates is. If the user has not ranked
anything, ask for the ranking rather than writing a platitude.Constraints must be checkable. Prefer "models must come from the
internal gateway" over "use approved models." This is also where the
permitted model and provider set lives; there is no Model section,
because the config already records the model in use and it changes without
touching this file.Tools and Harness should be concise. For Harness, describe how this
agent actually runs. Do not pick a named harness from a catalog, and do not
treat a framework import as a requirement. Group related helpers in
Tools by capability or source when they share credentials, side
effects, freshness, and failure modes. Keep only details that change how
downstream agents evaluate behavior.AUT or "agent under test." Use "this agent"
for the agent being specified. Use "target agent" only when this agent's
job is explicitly to inspect or modify another agent.Render the ethos.md. Use the template at
references/templates/ethos.md as the starting point. Substitute
every section from the ethos-explore answers. Set front matter as:
schema_version = 1, name = the canonical agent name,
created_timestamp = current UTC timestamp in ISO 8601 form, and author =
the human or coding agent creating the file. Add owner when a human or
team is accountable for the agent's behavioral or safety approvals named in
Behavior or Constraints, including a known owner of an already-confirmed
organizational safety policy.
Set updated_timestamp on edits, not on first write. Evaluation commands
live in Evaluation Setup, not in front matter. Keep the required section
headers exactly so the file stays parseable. Extra ## headings after
(or among) the canonical fourteen are allowed — keep them. The file is
lightly validated by the bundled scripts/validate_ethos.py, which
checks front matter, schema version, required sections, and duplicate
sections. It does not reject unknown headings. Section bodies stay markdown
for agents and humans to read directly.
Write the file. Write to ETHOS_PATH, creating its directory if needed.
If an Eval Author 0.1.0 ETHOS.md exists in that directory, rename it to ethos.md
before writing, through a temporary name so the rename also works on
case-insensitive filesystems:
DIR=$(dirname "$ETHOS_PATH")
if ls "$DIR" | grep -qx ETHOS.md; then
mv "$DIR/ETHOS.md" "$DIR/ethos.md.tmp"
mv "$DIR/ethos.md.tmp" "$DIR/ethos.md"
fi
Validate. Run the bundled validator and surface any warnings to the user. A failure means the file is malformed; fix the named problem before going on. Warnings are not failures — report them so the user can decide whether to fill the gap now.
python3 "<this skill's directory>/scripts/validate_ethos.py" "$ETHOS_PATH" \
|| echo "ethos_parse_invalid"
Show a gut-check, then the file. Before asking the user to read fourteen
sections, state your impression of this agent in a short paragraph that
combines Role, Purpose & Outcomes, Scope, and (when they are not
_(none)_) Principles and Vision. This is a thin slice so the user can
tell quickly whether the write got the agent right. Do not use shorthand
like AUT or "agent under test."
Shape:
Gut check. This is a [role] that exists to [mission / outcome]. It serves [audience] on [in-scope work] and stays out of [out of scope]. When the rules run out, it [principle or none]. It is heading toward [vision or none].
If that is the wrong agent, say so. Then we can edit before treating this file as signed off.
Then print the full file contents and ask: "Does this match what we agreed? Edit anything you want to change." If the user edits, repeat steps 5–7, including a fresh gut-check.
Hand off. Once confirmed, tell the user where the file is and that it is
ready to commit. It is now the reference for evaluating, reviewing, or
changing this agent. When intent changes, edit it in place and set
updated_timestamp.
After writing, both must hold:
# Local file present and non-empty.
test -s "$ETHOS_PATH" && echo "local_ok" || echo "local_missing"
# Passes the bundled validator.
python3 "<this skill's directory>/scripts/validate_ethos.py" "$ETHOS_PATH" \
&& echo "ethos_parse_ok" || echo "ethos_parse_invalid"
Do not announce success until local_ok and ethos_parse_ok both print, the
gut-check has been shown, and the user has confirmed the contents.
| Symptom | Cause | Recovery |
|---|---|---|
local_missing after write | Wrong working directory or permission denied | Run pwd; check the user is in the cloned repo |
ethos_parse_invalid | ethos.md malformed — missing front matter, missing required section, duplicate section, or bad schema version | Read the parser error; fix the named section in place; do not silently work around |
| User says "this is wrong" | ethos.md captured the wrong answers | Edit the relevant section in place; re-validate |
ethos-explore was skipped | User invoked ethos-write cold | Route back to ethos-explore and return here when the conversation is done |
This skill does not write agent configs, runtime YAML, or code, and does not register or deploy the agent. The ethos.md is the human-readable design; machine-readable config stays with the agent's implementation.
It also does not encode a scoring function. Trade-offs records the
developer's intent — hard gates, priority order, unacceptable regressions — in
prose. Turning that into thresholds and weights belongs to whatever evaluates
or changes the agent, not to this file.
## headings are allowed and must
be preserved. The bundled validator
rejects missing or duplicate required sections, but it does not reject
custom headings. Section bodies remain markdown for humans and agents to
read directly.ethos-explore for that field only._(none)_ for
Constraints or Trade-offs when the user has no answer. Do not invent a
bound. Record the gap in Open Questions as well.Purpose & Outcomes cannot be implementation-only by accident. If goal
context was not found in the codebase and the user did not provide outside
context, make that provenance clear instead of letting implementation details
masquerade as mission. A mission with no stated outcome cannot be measured.AUT and "agent under test"; reserve internal shorthand for test harnesses
and code comments.Known Issues section, and no Signals section either — how a
given consumer reads evidence is that consumer's configuration, not durable
intent. Record what a metric cannot support in Metric Semantics, and what
should not count as a failure in Behavior.ethos.md. Agents working on this agent read it but
should not edit it; only the developer and the developer's coding agent do. Treat it
as a long-lived contract, not a scratch pad.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
日本語の概要は準備中です。原文の説明を表示しています。
Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.
日本語の概要は準備中です。原文の説明を表示しています。
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
日本語の概要は準備中です。原文の説明を表示しています。
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
日本語の概要は準備中です。原文の説明を表示しています。
Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.
日本語の概要は準備中です。原文の説明を表示しています。
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
日本語の概要は準備中です。原文の説明を表示しています。