The mandatory pre-launch gate and four-verb execution contract for every TAO workflow or action. Invoke BEFORE launching anything side-effecting — AutoML, train, evaluate, inference, export, TensorRT engine generation, or DEFT/application workflows — on any execution platform. Covers platform selection, credentials, image confirmation, dataset intake, preflight, the launch review, job records, monitoring, and failure/retry classification. Trigger phrases include "train this model", "run AutoML", "launch on SLURM/docker/k8s/brev/virtualenv", "evaluate my checkpoint", "start a TAO job".
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
The mandatory pre-launch gate and four-verb execution contract for every TAO workflow or action. Invoke BEFORE launching anything side-effecting — AutoML, train, evaluate, inference, export, TensorRT engine generation, or DEFT/application workflows — on any execution platform. Covers platform selection, credentials, image confirmation, dataset intake, preflight, the launch review, job records, monitoring, and failure/retry classification. Trigger phrases include "train this model", "run AutoML", "launch on SLURM/docker/k8s/brev/virtualenv", "evaluate my checkpoint", "start a TAO job".
license
Apache-2.0
compatibility
Requires the packaged TAO skill bank helper scripts.
metadata
{"author":"NVIDIA Corporation","version":"0.1.1"}
allowed-tools
Read Bash
tags
["tao","workflow","launch"]
TAO Workflow Launch Intake
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).
Use this skill before launching any TAO workflow or model action.
Quick Start
Run the platform helper, ask for platform and monitoring preferences, then run
the selected platform detail helper before asking for credentials.
Non-Negotiable Launch Gate
This gate is model-agnostic. Apply it to every TAO model, data action, and
application workflow before launching side-effecting work.
Do not create runner scripts, launch scripts, compatibility shims,
workspace folders, state files, logs, or dependency-install side effects until
the launch preflight passes.
Preflight passes only after all of these are true:
The execution platform is selected from the packaged platform helper.
Platform credentials and required credential groups are satisfied.
Model-specific credentials are satisfied.
The default container image is resolved from packaged model/action metadata,
shown to the user, and either confirmed or replaced by an explicit
image=<override>.
The platform access check succeeds from the launch host.
Dataset inputs are mapped to concrete spec keys and verified from the
selected platform's point of view.
Required compute shape fields from the model/workflow skill are known.
Required local tools for the selected data/platform path are present, or the
user approved installing the smallest missing dependency and preflight was
rerun.
A launch review with image, platform, datasets, compute shape, expected
runtime, and any generated/default configuration changes has been shown and
confirmed by the user. For AutoML, the launch review must explicitly state
recommendation count/budget, max concurrency, algorithm, metric, direction,
and searched parameters/ranges even when defaults are used.
If any item is missing, ask for the missing input and stop before generating
artifacts. This applies to AutoML, normal train/eval/infer/export/TRT, and
DEFT/application workflows.
When preflight work clears a blocker, keep track of the original user request.
After the fix, rerun the relevant preflight and continue toward that request;
do not stop at "blocker fixed" unless the user explicitly asked only for the
repair.
The Four-Verb Execution Contract
Once the launch gate passes and the producing model/data skill has authored the
spec-bundle (schema: tao-artifacts), execution is exactly four verbs. Every
platform skill implements them over its native CLI — the bank ships five
(, , , , ), and any
externally installed platform skill joins the same contract (§ External
platform skills); nothing else is platform-specific.
= .
tao-run-on-docker
-slurm
-kubernetes
-brev
-virtualenv
$BANK
${TAO_SKILL_BANK_PATH}
submit(spec-bundle) — resolve the data question first: if the inputs are
already readable from the compute frame (a local path, an existing mount —
tier A in place, the common local and the only air-gapped case), there is
nothing to stage — record tier A and move on. Invoke tao-data-io only on a
frame mismatch (remote URIs, cross-host paths, PTM fetches, tier-C result
uploads). Then lint the assembled command with redact_secrets.py lint and
open the record and launch, in that order:
JOB_ID=$("$BANK/scripts/tao_job_record.py" open --platform <p> --image <img> \
--network-arch <arch> --action <action> --storage-tier <A|B|C> --results-root <root>)
# <native launch, naming the backend object after $JOB_ID>"$BANK/scripts/tao_job_record.py" mark "$JOB_ID" --state RUNNING --backend-ref <ref>
status(id) — poll the native backend, map to the fixed vocabulary
PENDING RUNNING COMPLETE ERROR CANCELED UNKNOWN; the native sub-state
(ImagePullBackOff, PENDING-resources, slurm COMPLETING) rides in the
transition message. Never read "what's running" from records — poll the backend.
logs(id, tail) — native log fetch.
cancel(id) — native cancel + orphan teardown, then mark <id> --state CANCELED.
Record-then-launch is the ordering invariant.open mints the id and binds
results_dirbefore any launch, and the id it returns is the only handle the
launch can use — a submit that skipped the gate or the open has no id, so it
cannot launch. This is what keeps a run recoverable across a context break:
results_dir is recorded before the backend object (which K8s TTL or docker
--rm may later delete) ever exists.
External platform skills
No registry, no interface file: a platform skill declares the contract by
documenting the four verbs, and you verify by reading before first use. A
skill with only native primitives may be used by inferring the mapping
(bank invariants still bind; the mapping goes in the launch review; persist
what worked). Rules and the no-equivalent hard floor:
references/external-platforms.md.
Failure analysis & retry
When status reaches ERROR, read the log tail and classify before any
retry — infrastructure faults are retriable (new record, --retry-of, up
to 10), program faults never are. Full criteria, the two judgment calls
(device-side asserts, downstream tracebacks), and the post-turn poller
rules: references/failure-analysis-retry.md.
Initial Questions
After the user confirms what they want to do, ask which execution platform
should run it. Discover the choices from the platform skills installed in this
session — you already see them by name and description (tao-run-on-docker,
-slurm, -kubernetes, -brev, -virtualenv, plus any externally installed one such as the
official brev-cli skill). There is no central platform registry to read. If
your runtime surfaces only the core router skills (e.g. Codex), list the bank's
platform skills by reading skills/platform/tao-run-on-*/SKILL.md frontmatter
(name + one-line description) under ${TAO_SKILL_BANK_PATH}.
Then ask:
Which supported platform should run this workflow?
Should I monitor the run in this chat? Monitoring means I keep polling the
backend/job logs after launch and report progress until the job finishes,
fails, or you ask me to stop, even if the job stays queued for hours or days.
If disabled, I launch the job, give you the job id/log path, and stop
polling. Default: monitor in chat.
How often should I post status? Default: every 5 minutes. Use 1-2 minutes for
smoke tests, 5 minutes for normal training, or 10-15 minutes for long runs.
Use long_running_enabled=true and status_interval_minutes=5 when the user
accepts the defaults.
When monitoring is enabled, do not send a final summary just because several
polls have elapsed or the job is still PENDING. Keep the turn attached and
emit status every status_interval_minutes until a terminal state or explicit
user stop/detach request. If the runtime environment cannot keep the chat turn
open, say that clearly and leave a durable watcher/log path; do not imply that
chat updates will continue after the turn ends.
Final-answer rule: a final response ends chat-side monitoring. While
long_running_enabled=true and any launched job is non-terminal, status
messages must be sent as in-progress updates and the agent must continue
polling. Only send a final response when the workflow reaches terminal state,
the user explicitly asks to detach/stop monitoring, or the runtime genuinely
cannot keep the turn open; in that last case, say it is a runtime limitation
and provide the exact durable status command/log path.
Missing-Input Prompt Shape
When intake inputs are missing, ask with the exact prompt shape in
references/intake-prompts.md (one consolidated ask, concrete examples,
no invented defaults).
Implementation Backend Resolution
After model ownership resolution, inspect the selected model's
references/skill_info.yaml. If it declares backend_contracts, resolve the
implementation before selecting an image or authoring a spec. An explicit
backend wins when it supports the model/action; otherwise apply the packaged
backend_selection policy and show its rationale. The selected backend
contract—not the legacy top-level fallback—owns the image, entrypoint,
configuration schema, data mappings, topology, checkpoint format, output
layout, and status behavior. Never treat one backend as a version of another.
Pass action, backend, and workload hints to the model resolver. When metadata
declares a backend planner, use it. The shared Cosmos frontend, for example,
uses scripts/cosmos_workflow.py plan to generate backend-native TOML and a
launch sequence.
Container Image Confirmation
Before creating specs, runner scripts, workspaces, logs, state files, or
submitting a job, resolve the image for the selected model/action:
If the helper is unavailable, read skills/models/<network>/config.json
directly. Resolve image fields in this order:
actions.<action>.container_image
actions.<action>.image
top-level container_image
top-level image
Show the exact image and ask:
Container image for <network>/<action>:
default=<resolved image>
Use this image, or provide image=<override>?
If the user accepts, pass the resolved image as the job image. If the user
overrides, require a non-empty image reference and pass that value instead.
Do not silently launch on the default image. This confirmation applies to
training, AutoML recommendations, evaluation, inference, export, TensorRT
engine generation, and application workflows that submit TAO containers.
Credential Filtering
After the user chooses a platform, get the credential list for only that
platform from the chosen skill itself — its ## Credentials section and, if
present, references/skill_info.yaml (required_credentials, credential_groups,
optional_credentials). The launch preflight (check_tao_launch_preflight.py)
reads that same per-skill skill_info.yaml to enforce the credential gate; a
credential-free platform (e.g. Docker) may ship only prose, in which case rely on
its Preflight section.
Ask only for credentials that platform actually needs, plus model-specific
credentials from the selected model skill. Do not ask for Brev credentials on
SLURM, Kubernetes, or Docker. Do not ask for SLURM credentials on Brev,
Kubernetes, or Docker. Ask S3 credentials only when the selected
platform and the dataset/result URIs require s3:// access.
Credentials may already be present in the process environment or in a
user-approved secret env file such as ~/.tao/secrets.env or
~/.config/tao/.env; source such files only when needed and never print,
grep, cat, paste, or log their contents. Verify only variable presence.
For initial launch intake, ask for required credentials and required credential
groups only. Treat the helper's optional credentials/settings section as
reference material; do not request those values unless their only_when
condition applies, the selected workflow cannot proceed without them, or the
user asks to customize that setting.
When the helper output includes a "Required credential groups" section, satisfy
one credential from each group before proceeding. Explain each requested value
using the helper's description and "How to get it" text.
For SLURM, user-facing prompts should ask for SSH_KEY_PATH first. Mention
SSH_AUTH_SOCK only if the user says they already use an SSH agent.
Dependency Remediation
If a required CLI/library is missing, say exactly what is missing and why it is
needed, then ask before installing. Examples:
S3 dataset or results path -> require an S3-capable client such as aws.
Local Docker path -> require the Docker CLI and the configured Docker
network.
After user approval and installation, rerun the same preflight. Do not create
runner files or launch jobs between the failed check and the rerun.
Dataset Intake
Accept dataset inputs in either mode:
Dataset root mode: the user gives train/eval/calibration roots, and the
model skill maps required files by convention. Example for Cosmos-RL train:
custom.train_dataset.annotation_path=<root>/annotations.json and
custom.train_dataset.media_path=<root>.
Direct spec mode: the user gives exact spec-key paths when annotations,
media archives, videos, or image folders live in different places. Preserve
those keys directly, for example
custom.train_dataset.annotation_path=<TRAIN_ANNOTATION_PATH>
and custom.train_dataset.media_path=<TRAIN_MEDIA_PATH>.
Ask for dataset examples that match the selected platform:
SLURM: explicit shared cluster paths supplied by the user and verified from
the allocated compute node; the skill has no site-specific storage default.
Brev, Kubernetes: usually s3://bucket/path/train and
s3://bucket/path/eval unless the platform profile mounts shared storage.
Local Docker: local paths visible to the Docker host, such as
/data/tao/<model>/train, or direct spec paths visible inside the planned
container mount.
Remote Docker: absolute paths visible on the remote Docker host named by
DOCKER_HOST, not paths on the local agent machine.
Do not assume "dataset root" is the only acceptable input. When direct spec
paths are supplied, validate the exact spec paths rather than appending default
filenames.
Platform Preflight
Run the selected platform's preflight checks before any launch artifact is
created — prefer the packaged helper scripts/check_tao_launch_preflight.py
(--platform <p> --container-image <img> --path <label>=<path> ...). It verifies
credentials, client tools, platform/cluster/object-store access, dataset paths
from the compute frame, GPU/runtime health, and image-architecture fit; treat any
failure as blocking. Never use --skip-platform-access for a real launch.
See references/platform-preflight.md for the full per-platform detail (SLURM
SSH/key setup + resource defaults, docker/remote-docker GPU + bind-mount checks,
Brev/Kubernetes API + object-store checks, annotation content-field checks, and
data staging).
Runtime And Configuration Review
Before any side-effecting launch, show a concise review:
selected platform and exact container image
GPU ids/count and nodes, including any GPUs avoided because they are already
occupied
dataset roots or direct spec paths, with sample counts when available
important model/workflow overrides that differ from template defaults
estimated runtime and the assumptions behind it
monitoring interval and whether chat-side monitoring will stay attached
implementation backend and selection rationale when the model exposes more
than one backend
For AutoML, also show the algorithm, metric/direction, recommendation budget,
search parameters, ranges, and generated/default recommendation details as
described in skills/applications/tao-run-automl/SKILL.md. Ask for confirmation after
this review. If the user supplied a time limit, flag any plan that exceeds it
and offer concrete reductions before launch.
Structured Training Metrics
When the model contract declares a structured status path or metric extractor,
poll it alongside the native backend. Scheduler/container completion is not a
successful training result by itself: require the model's terminal structured
success record, collect concrete checkpoint events, and return final train
loss plus every epoch validation-complete loss. Do not promote validation
heartbeat/batch metrics or a train-loss line to epoch validation loss. If the
process fails before its native logger exists, invoke the packaged status
finalizer or report the real process exit failure; use raw log parsing only as
a fallback.
