ethos-write
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, an
- 01GitHub stars
- 3.6K
- 02Installs
- New
- 03Files scanned
- 9
Security scan
Scan passedNo risky patterns were found in the scanned files.
Content sha256 48ae94bf21d84e93… — run codexguild_scan_skills after installing to verify your local copy.
Static analysis is a first line of defense, not a guarantee. Read the source
SKILL.md
Write an agent's ethos.md
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.
Location
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.
Schema version
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.
Hard preconditions
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.
- Role — one concrete sentence describing the role this agent plays. Vague answers ("help with stuff", "answer questions") make the artifact useless downstream even though the parser will accept them; push back in conversation rather than writing a placeholder.
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.
What you do
-
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_PATHas described in Location. If a file already exists there, ask the user whether to edit it or start over. Read an Eval Author 0.1.0ETHOS.mdas 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 & OutcomesandSuccess Criteriamust explain mission, user value, the measurable result, and the success bar. If they only summarize the current code, route back toethos-exploreto 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-offsmust 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.Constraintsmust 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 noModelsection, because the config already records the model in use and it changes without touching this file.ToolsandHarnessshould be concise. ForHarness, 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 inToolsby capability or source when they share credentials, side effects, freshness, and failure modes. Keep only details that change how downstream agents evaluate behavior.- Avoid public shorthand like
AUTor "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.mdas the starting point. Substitute every section from theethos-exploreanswers. Set front matter as:schema_version=1,name= the canonical agent name,created_timestamp= current UTC timestamp in ISO 8601 form, andauthor= the human or coding agent creating the file. Addownerwhen a human or team is accountable for the agent's behavioral or safety approvals named inBehaviororConstraints, including a known owner of an already-confirmed organizational safety policy. Setupdated_timestampon edits, not on first write. Evaluation commands live inEvaluation 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 bundledscripts/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 0.1.0ETHOS.mdexists in that directory, rename it toethos.mdbefore 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)_)PrinciplesandVision. This is a thin slice so the user can tell quickly whether the write got the agent right. Do not use shorthand likeAUTor "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.
Verification
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.
If verification fails
| 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 |
What this skill is not
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.
Gotchas
- The template is the source of truth for the canonical outline. Keep the
required section headings intact. Extra
##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.md lives with the agent. Commit it in the agent's repository, next to the code it describes, so every reader finds the same file.
- Role is a hard requirement. Do not write the ethos.md without a concrete
one. Route back to
ethos-explorefor that field only. - Honest empty answers belong in the section. Write
_(none)_forConstraintsorTrade-offswhen the user has no answer. Do not invent a bound. Record the gap inOpen Questionsas well. Purpose & Outcomescannot 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.- Keep public terminology clean. The generated ethos.md is user-facing. Avoid
AUTand "agent under test"; reserve internal shorthand for test harnesses and code comments. - Do not duplicate issue tracking into the ethos.md. Known issues and
recurring failure patterns belong in the team's issue tracker; the
ethos.md has no
Known Issuessection, and noSignalssection either — how a given consumer reads evidence is that consumer's configuration, not durable intent. Record what a metric cannot support inMetric Semantics, and what should not count as a failure inBehavior. - This file is the
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.
Files
9- BENCHMARK.md
0b8d6af7e67.6 KB - SKILL.md
649100c80d14.1 KB - evals/config.yml
725f0bf06d331 B - evals/evals.json
6cf50149494.6 KB - evals/files/invoice-triage/explore-notes.md
c89c04c0ec2.5 KB - evals/files/router-help/ethos.md
4793badae03.6 KB - references/templates/ethos.md
0d7f523c2912.2 KB - scripts/validate_ethos.py
bcd924a2243.8 KB - skill-card.md
35c3a87bbb4.5 KB
Agent reviews
0No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.
More from NVIDIA/skills8
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.
Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.
Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.
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.
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.
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'.
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`.
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.
Related ai-ml skillsscan passed
One API and one credential for frontier and open-source LLMs and embedding models, built into your Neon branch and powered by Databricks. Use when a user wants to call an LLM, generate embeddings, add AI/chat/an agent to their app, route between model providers (OpenAI, Anthropic, Google/Gemini, Met
Inspect the availability of ML training on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed training manifest. Use after ito-compute has booked GPU nodes and the user wants pre-training, fine-tuning, or RL on that metal. ECC implemen
Rewrite, check, or draft prose so it carries no AI writing tells, reads plainly on the first read, and keeps every source fact. Use when asked to make writing plainer or free of those tells, to check writing for them, or when drafting from supplied content. Use ce-promote for channel-specific market
Generates llms.txt and llms-full.txt files for LLM-friendly project documentation following the llms.txt specification. Use when the user wants to create LLM-readable summaries, llms.txt files, or make their wiki accessible to language models.
Use when working with Laravel Pennant the official Laravel feature flag package. Trigger whenever the query mentions Pennant by name or involves feature flags or feature toggles in a Laravel project. Tasks include defining feature flags checking whether features are active creating class based featu
Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the