Tao Artifacts
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides the definitive source of truth for the four primary artifacts used in the TAO execution pipeline: spec-bundle, job-record, results_dir layout, and best_rec. It defines the structural requirements, validation schemas, and operational rules necessary for producers and consumers to interact reliably within the TAO ecosystem. By enforcing consistent data formats and state vocabularies, it ensures that model data, job records, and output directories remain interoperable across different execution environments.
What it does
- Provides authoritative JSON schemas for validating TAO pipeline artifacts
- Defines the fixed vocabulary for job status transitions
- Enforces structural rules for spec-bundle configuration and nesting
- Specifies the storage-agnostic layout contract for job output directories
- Outlines the lifecycle and responsibilities for model-to-platform execution seams
When to use it
- When authoring or validating a spec-bundle before job submission
- When reading or writing a job-record in the .tao/jobs directory
- When resolving the location of job output artifacts
- When consuming AutoML best_rec.json files
- When debugging pipeline state transitions or artifact inconsistencies
Advantages
- Ensures structural consistency across heterogeneous execution platforms
- Provides a single source of truth for pipeline data contracts
- Simplifies integration between model producers and platform consumers
- Standardizes job state reporting for reliable monitoring and polling
Usage examples
- “Validate the spec bundle”
- The agent uses the provided JSON schema to verify that the spec-bundle configuration adheres to the required nested structure and mode discrimination rules.
- “What is the status vocabulary?”
- The agent returns the fixed set of job states: PENDING, RUNNING, COMPLETE, ERROR, CANCELED, and UNKNOWN.
- “Show me the results_dir layout”
- The agent explains the storage-agnostic layout where artifacts are organized by job_id and output_key under the defined results root.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-artifacts/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-artifacts.zip -d .agents/skills
Documentation by NVIDIA Corporation
tao-artifacts
Four typed artifacts flow through every TAO job. Their schemas live here and
nowhere else — producers (model/data skills) and consumers (platform skills)
both validate against this skill's references/.
| Artifact | Schema | Produced by → consumed by |
|---|---|---|
| spec-bundle | references/spec_bundle.schema.json | model/data skill → platform skill (at the submit seam) |
| job-record | references/job_record.schema.json | scripts/tao_job_record.py (the ONLY writer) → any re-attaching agent/poller |
| results_dir layout | references/results_dir.contract.md | platform skill at submit → whoever collects outputs |
| best_rec | references/best_rec.schema.json | tao-run-automl adapter → DEFT warm-start |
Quick Start — validate an artifact
python - <<'PY'
import json, yaml, jsonschema, pathlib
ref = pathlib.Path("${TAO_SKILL_BANK_PATH:?}/skills/core/tao-artifacts/references")
schema = json.loads((ref / "spec_bundle.schema.json").read_text())
bundle = yaml.safe_load(open("/path/to/bundle.yaml")) # or a dict built in-context
jsonschema.validate(bundle, schema) # raises on violation
print("bundle OK")
PYValidate the bundle before the verify-before-launch gate; validate a job-record only when debugging (the writer script already enforces the schema).
The two rules the schemas enforce structurally
Nested, not dotted. A
specis a nested dict mirroring the container's config shape —{"train": {"num_epochs": 12}}. Any key containing.at any depth is rejected ({"train.num_epochs": 12}is the #1 authoring mistake). Note the distinction:declared_inputs[].spec_keyandgpu_spec_keyare dotted/indexed pointers into the spec (dataset.train_data_sources[0].image_dir) — dots are correct there.Mode discrimination.
mode: configrequiresspec+config_formatand acommandcontaining{config_path}, and forbidsargs.mode: argsrequiresargsand forbidsspec. There is no other mode.
Optional action lifecycle
Use execution when an action needs more than its primary command. This is the
shared model-to-platform seam; do not add a model-specific Docker, Kubernetes,
or SLURM renderer merely to carry runtime environment, attestations,
post-processing, or helper dependencies.
The producing model/data skill owns
environment, orderedpre_commands, orderedpost_commands, distributed-launch intent, and completion evidence.The platform owns container mounts, scheduler/container syntax, task/rank binding, timeouts, log paths, and preservation of the real child exit code.
environmentis non-secret. Credential values continue to use the selected platform's secret/sidecar contract and never enter a spec-bundle.Commands, environment values, and string values in
specmay use{config_path},{job_id}, and{results_dir}. The platform binds them only after the job record has been opened; the job record'sresults_diris authoritative over any pre-review display path. Persist hashes of both the producer bundle and the bound runtime config.supporting_filesnames checked-in orchestration helpers relative to the producing skill root. The platform stages the closed set, verifies every declared SHA256, and rejects traversal, undeclared siblings, or overwrite of a different bundle. Supporting files orchestrate an action; they must never shadow or patch code inside the selected image.A
torchrundeclaration expresses process topology, not SLURM/Kubernetes syntax. Each platform maps it to its native distributed launcher.
Fixed status vocabulary
Every job state anywhere in the pipeline is exactly one of:
PENDING · RUNNING · COMPLETE · ERROR · CANCELED · UNKNOWN
Platform-native sub-states (ImagePullBackOff, PENDING-because-resources,
Insufficient-GPU, slurm COMPLETING…) are never new states — they ride in
the transition's message field. Terminal = COMPLETE | ERROR | CANCELED.
This is what lets the in-turn poll loop and the detached poller share one code
path across docker/slurm/kubernetes/brev.
Ordering invariants (enforced at the seam, stated here)
The verify-before-launch gate runs on the spec-bundle, before any job id exists.
tao_job_record.py openwritesPENDING+ the resolvedresults_dirfirst and returns the id — the only handle a launch can use. A submit that skipped the gate has no id, so it cannot launch.transitionsis append-only;.tao/lives outside every synced results tree.
Files
- ATTRIBUTION.mdAdded by AutoGineer909 B
- BENCHMARK.md6.9 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.3 KiB
- config/skillspector-baseline.yaml834 B
- evals/evals.json903 B
- references/best_rec.schema.json2.3 KiB
- references/job_record.schema.json3.9 KiB
- references/results_dir.contract.md1.6 KiB
- references/spec_bundle.schema.json8.0 KiB
- references/tests/test_artifact_schemas.pyScript12.9 KiB
- skill-card.md4.3 KiB
- skill.oms.sig5.9 KiB
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Requirements and compatibility
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, Needs Python
The author says: Python 3.10+ with jsonschema for validation. No nvidia-tao-sdk.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
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
- Created by
- NVIDIA Corporation
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/tao-artifacts · 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 Corporation or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-artifacts.zip