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Ethos Orchestrate

NVIDIAOfficialPublished by NVIDIA

  • Skill
  • Meta (authoring, orchestration, planning, memory)
  • Agent tooling
  • Apache-2.0
  • Medium risk

From the author

Entry point for an AI agent's ETHOS.md: a durable snapshot of what the agent is for, what it must not do, and what success looks like. Routes a request to explore and capture intent (ethos-explore), or write or edit the file (ethos-write). Owns the standard every ethos sub-flow follows. Use when the user asks "help me write an ETHOS.md", "does my agent have an ETHOS.md?", "update my agent's ETHOS.md", or when you need to pick between the ethos sub-flows.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

ethos-orchestrate serves as the primary entry point for managing an agent's ETHOS.md file, which acts as a durable record of the agent's purpose, principles, and constraints. It routes user requests to specialized sub-flows for exploring intent or writing and editing the file, ensuring a consistent standard for documentation. By maintaining context across interactions, it prevents redundant questioning and guides the user through a structured process of discovery, drafting, and validation.

What it does

  • Routes requests to appropriate sub-flows for exploring or writing ETHOS.md files.
  • Maintains conversation context to avoid repeating questions or steps.
  • Provides a standardized checklist for creating new documentation.
  • Locates existing ETHOS.md files within a repository.
  • Ensures that intent is confirmed by the user rather than inferred by the agent.
  • Reports on validation status and identifies unresolved questions.

When to use it

  • When you need to create an ETHOS.md file for an agent from scratch.
  • When you want to update or modify specific sections of an existing ETHOS.md.
  • When you need to verify if an agent has an ETHOS.md file and check its validity.
  • When you are unsure which specific ethos-related sub-flow to invoke.

Advantages

  • Ensures documentation accurately reflects user-confirmed intent.
  • Reduces user effort by carrying forward previously established information.
  • Provides a clear, step-by-step path for documentation tasks.
  • Maintains consistency in how agent intent is captured and recorded.

Usage examples

“Help me write an ETHOS.md for my agent.”
The skill initiates the exploration process, presenting the path-ahead checklist and beginning to scan the agent's code to draft intent.
“Update my agent's ETHOS.md to include a new constraint.”
The skill routes the request to the editing sub-flow to modify the specific section in place, followed by validation and review.
“Does my agent have an ETHOS.md?”
The skill searches the repository for an existing file, reports its location, and confirms whether it passes validation.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/ethos-orchestrate/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip ethos-orchestrate.zip -d .agents/skills

Documentation by NVIDIA

<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->

<!-- SPDX-License-Identifier: Apache-2.0 -->

Ethos orchestrate

Purpose

Help a developer create and maintain their agent's ETHOS.md. Route each request to the narrow sub-flow that owns it, and carry what is already known into that sub-flow so the user is never asked twice.

Continue from prior context

When a user or another workflow supplies an earlier Ethos conversation, reuse the selected repository, agent, file location, and requested operation. Skip the repeated opening and completed questions. Check that prior findings and answers still apply to this agent; distinguish source evidence from explicit user replies, retaining where each answer came from. Without usable prior context, use the standalone entry below.

Resume the earliest unfinished work. Missing or conflicting intent goes to ethos-explore; completed intent goes to draft review or ethos-write as needed. Before writing, recover the exact accepted draft and the user's confirmation. If either is unavailable or the draft changed, review the draft again while retaining completed answers. Review a suitable existing file without rewriting it. Reuse an earlier saved-content review only when the contents are unchanged, the confirmation applies to this agent, and current findings reveal no conflict.

On a pause or completion, report the file location, actual validation outcome, unresolved questions, and what the user confirmed or still needs to review. Carry relevant answers and draft context back to the invoking conversation or workflow, which owns any persistence. These skills write only ETHOS.md; they require no external state format. Missing validation or a required sub-flow must be reported as unfinished work, not completion.

Choose the entry route

For a continuation, use the context above. The following routing and opening apply to standalone requests.

Locate an existing file first: the user's path, otherwise ETHOS.md at the repository root or at the root of the selected agent's package.

find . -maxdepth 4 -iname ETHOS.md -not -path '*/.git/*' -not -path '*/node_modules/*' 2>/dev/null | grep . || echo "no ETHOS.md yet"
Starting situation and requestRouteFirst action and deliverable
No ETHOS.md and the user wants oneethos-exploreShow the path ahead below, then scan and interview. Explore hands off to ethos-write.
An ETHOS.md exists and the user names specific changesethos-writeEdit the named sections in place, validate, and review. No new interview.
An ETHOS.md exists and intent may have drifted, or the user wants it revisitedethos-exploreRead the file as prior answers; interview only for what changed or is missing.
The user wants to know what ETHOS.md is, or whether one existsThis skillExplain it, or report what was found and whether it validates. Offer the next route; do not start writing.

When several apply, take the earliest row that matches. A request phrased as "write the ETHOS.md" with no prior interview in this conversation still goes through ethos-explore: writing needs intent the code cannot supply.

Show the path ahead

For a fresh request to create an ETHOS.md, open with this checklist using literal - [ ] checkboxes, marking the first step We're here:

  • [ ] Explore the agent — read its code and docs and draft what they establish.

  • [ ] Confirm intent — answer a few questions the code cannot: purpose, principles, and vision.

  • [ ] Review the draft — see the whole ETHOS.md and correct it.

  • [ ] Save and validate — write the file, check it, and confirm the final contents.

End with "Ready to start?" and wait. On return visits, resume the earliest unfinished step instead of repeating the opening.

The standard

ETHOS.md records intent the user confirmed, not intent you inferred.

  • Code, prompts, configs, and docs establish what the agent does today. They draft Tools, Harness, Evaluation Setup, and similar sections.

  • Only the user establishes purpose, principles, vision, trade-offs, and constraints. An inferred answer is a draft to confirm, never a filled section.

  • An honest _(none)_, recorded in Open Questions, beats a plausible guess.

  • The bundled validator establishes structure: front matter, schema version, and the fourteen required sections. A file that validates can still be wrong; the user's review establishes that it is right.

Vocabulary

TermMeaning
ETHOS.mdThe agent's intent snapshot: front matter plus fourteen required ## sections, schema version 1
SectionOne required ## heading from the template. Extra headings are allowed and preserved
InferredDrafted from source; shown to the user as an option, never final
ConfirmedAnswered or accepted by the user in this conversation, or carried with explicit user-reply provenance in matching prior context

Sub-flows

Load the selected sub-flow skill when its step begins and follow it. This file carries the standard and the routing; the sub-flow carries the steps.

Sub-flowUse it to
ethos-exploreScan the agent's code and docs, then ask one intent question at a time; hand the agreed answers to ethos-write
ethos-writeRender the template, save ETHOS.md, run the validator, show a gut-check, and get the user's confirmation; edit an existing file

Boundaries

  • Write only the ETHOS.md. Never edit the agent's code, prompts, configs, or evals. The file goes at the user's path or the repository root, and nothing is committed automatically.

  • Preserve what exists. Keep custom sections, extra front-matter keys, author, and created_timestamp on edits.

  • Capture intent without authorizing application changes. Do not ask optimization or change-permission questions in the interview, draft review, or completion checkpoint. Creating an ETHOS.md implies no follow-up to optimize or change the agent. If the user later requests such work, its workflow owns the permission decisions.

  • Keep runtime safety requirements. Record behavioral constraints and approval requirements for actions the agent takes during operation, such as issuing refunds. These describe intended behavior, not permission to edit the application. Also preserve already-confirmed organizational safety and review policies and their known owners without reopening permission questions.

  • No run settings. Spend caps, experiment counts, and per-run limits belong to the tool that runs them, not to ETHOS.md.

Communicating with the user

Do not assume the user knows the term. Before the first question, explain in a sentence or two that ETHOS.md records what the agent should do, what it should avoid, and what a good result looks like, and that people and agents working on the agent read it first. Make the explanation concrete for this agent. Reuse that explanation when it has already been given.

Ask one question per message, prefer multiple choice grounded in what the scan found, and always allow "I don't know". When reporting an existing file, lead with whether it validates and what it says the agent is for, then the gaps.

Prerequisites

A local checkout of the agent, or a description of a planned one. Validation needs Python 3.10+.

Limitations

ETHOS.md is a snapshot of intent; it does not prove the agent behaves that way. Measuring that belongs to evaluation tools.

Troubleshooting

  • Several candidate files or agents: ask which agent the ETHOS.md describes.

  • Validator unavailable: check the structure by reading the file against the template, and say the check was not run.

  • User cannot state a concrete Role: stop, explain that the file is not useful without one, and offer to resume later.

Examples

Request: "Help me write an ETHOS.md for this support bot."
Route: ethos-explore, after the path-ahead checklist.
Deliverable: a reviewed, validated ETHOS.md at the repository root.

Request: "Add a no-PII rule to Constraints in our ETHOS.md."
Route: ethos-write, editing Constraints in place.
Deliverable: the updated file, validated, with the change shown for review.

Request: "We still have the ETHOS.md we wrote last month."
Route: this skill, checking the existing file.
Deliverable: the existing ETHOS.md with unchanged name and contents, validated.

Files

skill-card.md

Description: <br>

Entry point for an AI agent's ETHOS.md (a durable snapshot of what the agent is for, what it must not do, and what success looks like) that routes requests to explore and capture intent (ethos-explore) or to write or edit the file (ethos-write). <br>

This skill is for research and development only. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache 2.0 <br>

Use Case: <br>

Developers building AI agents use this skill with a coding assistant to find, explain, create or update their agent's ETHOS.md, routing each request to the ethos-explore or ethos-write sub-flow while ensuring the file records only user-confirmed intent. Interrupted conversations can resume from supported prior context without repeating completed questions or assuming unconfirmed approval. <br>

Deployment Geography for Use: <br>

Global <br>

Requirements / Dependencies: <br>

Requires API Key or External Credential: [No] <br> Credential Type(s): [None] <br>

Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

Skill Output: <br>

Output Type(s): [Analysis, Shell commands, Files] <br> Output Format: [Markdown with inline bash code blocks; routed sub-flows produce a single ETHOS.md file] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [Routing reads the local checkout only and works offline; writes are limited to the ETHOS.md file via sub-flows and nothing is committed automatically.] <br>

Evaluation Agents Used: <br>

  • Claude Code (aws/anthropic/bedrock-claude-opus-5-5) <br>

  • Codex (openai/openai/gpt-6.1-sol) <br>

Evaluation Tasks: <br>

4 evaluation tasks (3 positive, 1 negative), 1 attempt per task, each run in an isolated sandbox pod (k8s-sandbox) and compared against a no-skill baseline; evaluator version 1.5.6, evaluated 2026-10-23. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

  • Security: Is it safe to use? Scored from the security signal (100%). <br>

  • Correctness: Is the answer correct? Scored from the accuracy signal (100%). <br>

  • Discoverability: Was the right skill loaded when needed? Scored from the skill_execution signal (100%). <br>

  • Effectiveness: Did the skill help complete the task? Equal-weight mean of goal_accuracy and behavior_check. <br>

  • Efficiency: Did it avoid wasted tool calls and token usage? 50% skill_efficiency plus 50% token_efficiency. <br>

Underlying evaluation signals used in this run: <br>

  • security: Unsafe operations, secret leakage, and unauthorized access. <br>

  • skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>

  • skill_efficiency: Tool-call productivity. <br>

  • accuracy: Final-answer correctness against the reference answer. <br>

  • goal_accuracy: Whether the user's goal was achieved. <br>

  • behavior_check: Whether the expected workflow behavior was followed. <br>

  • token_efficiency: Actual uncached prompt plus completion usage. <br>

Evaluation Results: <br>

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall92.2% (baseline not comparable; uplift unavailable)93.7% (baseline not comparable; uplift unavailable)
Security100.0% → 100.0% (±0.0 points)100.0% → 100.0% (±0.0 points)
Correctness65.0% → 100.0% (+35.0 points)85.0% → 90.0% (+5.0 points)
Discoverability76.7% (baseline not comparable; uplift unavailable)93.3% (baseline not comparable; uplift unavailable)
Effectiveness56.0% → 93.1% (+37.1 points)64.8% → 89.2% (+24.4 points)
Efficiency91.0% (baseline not comparable; uplift unavailable)96.0% (baseline not comparable; uplift unavailable)

Skill Version(s): <br>

0.1.0 (source: pyproject.toml; git SHA 97cd9ee, committed 2026-10-23) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>

Requirements and compatibility

Written for: Claude Code

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: Declares allowed tools, Claude Code frontmatter

The author says: Routing reads the local checkout only. Sub-flows write one ETHOS.md; validation needs Python 3.10+ and ethos-write's standard-library script. Works offline.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
NVIDIA
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/ethos-orchestrate · 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 or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download ethos-orchestrate.zip