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Where and how GPU jobs run on this platform. One-to-three-sentence summary. Use when the user asks to "deploy on REPLACE-PLATFORM", "run on REPLACE-PLATFORM", or mentions the platform's distinctive concepts (e.g., resource shape, instance, node group).
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/NVIDIA-TAO/tao-skill-bank --skill replace-with-platform-name命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Run the full DEFT smart-data-augmentation loop for NVIDIA TAO Grounding DINO object detection: zero-shot baseline inference, KPI analysis, per-class gap analysis, SigLIP embedding of weak images, unique-neighbor mining against a source pool, ODVG dataset staging, and retraining — repeated for a fixed number of iterations. Also prepares the source pool the loop mines from, as a separate run: Co-DETR pseudo-labeling, folding to the target classes, KITTI→COCO→ODVG conversion, and embedding. Use for prompts like "run the DEFT OD loop", "run smart data augmentation for grounding dino", "mine and retrain my detection model", "improve OD mAP with gap analysis and mining", "prep the source pool", or "pseudo-label my unlabeled images for mining"; do not use for standalone TAO training, one-off inference, or gap analysis alone.
NV-Tesseract AD Diffusion — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune NV-Tesseract", "run AD diffusion inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune ad-diffusion", "use perform_anomaly_analysis_with_diffusion", "automl ad-diffusion", "hyperparameter search ad-diffusion", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "nv-tesseract-ad-diffusion", "ad_diffusion", or "TSDiffuser_Generic".
NV-Tesseract Forecasting — transformer-based multivariate time series forecasting with DARR (context-enhanced kNN retrieval), interpretability, and fine-tuning. Use when the user asks to "forecast with NV-Tesseract", "run forecasting inference", "use perform_forecasting", "DARR mode", "context-enhanced forecasting", "lag horizon attribution", "interpretability", "fine-tune forecasting", "fine-tune forecasting with automl", "hyper-parameter optimization with forecasting", or or mentions "nv-tesseract-forecasting", "moment_head_512_6hr", or "run8_best_model_cr".
基于 SOC 职业分类
正在显示 SKILL.md
| name | REPLACE-WITH-PLATFORM-NAME |
| description | Where and how GPU jobs run on this platform. One-to-three-sentence summary. Use when the user asks to "deploy on REPLACE-PLATFORM", "run on REPLACE-PLATFORM", or mentions the platform's distinctive concepts (e.g., resource shape, instance, node group). |
| license | Apache-2.0 |
| compatibility | REPLACE — examples — `Requires the brev CLI and an active brev login.` or `Requires kubectl configured against a GPU cluster and <PLATFORM>_API_TOKEN.` |
| metadata | {"author":"REPLACE-WITH-AUTHOR-NAME","version":"0.1.0"} |
| allowed-tools | Read Bash |
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first (host preflight, credentials, cross-skill discovery).
Short summary of the platform's execution model. Instance-based or job-based? API-first or docker-first? Single-node or multi-node? Managed or self-hosted?
For generic docker conventions, defer to tao-skill-bank:tao-run-on-docker. This skill covers the platform-specific layering on top.
| Dependency | Purpose | Install |
|---|---|---|
<cli-name> | Submit jobs / manage resources | <install-command> |
| Method | When to use |
|---|---|
| API token (recommended) | Scripted / automated workflows |
| Browser login | Interactive development |
Env vars:
| Env var | Required | Purpose |
|---|---|---|
<PLATFORM>_API_TOKEN | Yes (or manual login) | API auth |
# 1. CLI installed
which <cli-name> || echo "MISSING: install from <url>"
# 2. Logged in
<cli-name> ls >/dev/null 2>&1 || echo "NOT LOGGED IN"
# 3. Platform-specific checks
<platform-cli> exec <target> -- docker run --gpus all --rm \
-e <env-vars> \
-v <host-path>:<container-path> \
nvcr.io/... \
<command>
A platform skill is a consumer: it runs a model/data skill's spec-bundle by
implementing submit/status/logs/cancel over the native CLI, mutating only
the job-record. No SDK. See tao-skill-bank:tao-launch-workflow for the shared
contract and tao-skill-bank:tao-run-on-docker for a worked example.
tao-data-io, lint the command with
redact_secrets.py, then tao_job_record.py open (mints the id + binds
results_dir before launch), launch naming the backend object after the id,
and mark ... --state RUNNING.PENDING RUNNING COMPLETE ERROR CANCELED UNKNOWN.mark ... --state CANCELED.| Symptom | Cause | Fix |
|---|---|---|
<error> | ... | ... |