tao-skill-bank
Review Skills in NVIDIA-TAO/tao-skill-bank, with license, maintenance context, and source paths.
- Skills
- 39
- Repository stars
- 82
- Identity status
- Source-linked
Provenance
Source and identity
- Profile type
- Repository
- Canonical name
- tao-skill-bank
- Public sources
- 1
- License context
- Apache-2.0
Source entries
Agent Skills from tao-skill-bank
Repository stars and maintenance signals provide context, but do not automatically become an individual Skill's quality score.
NVIDIA-TAO/tao-skill-bank
paidf-anomalygen
Full PAIDF AnomalyGen pipeline — fine-tune on a new anomaly dataset, generate synthetic anomaly images (SDG), evaluate quality (nn_score), and search per-sample (guidance, crop_ratio) parameters. Three modes: full (Phase 0→7: finetune then generate), finetune_only (Phase 0→1: train only), inference_only (Phase 0, 2→7: generate from an existing checkpoint). Use when the user asks to "fine-tune AnomalyGen", "generate anomaly images", "run PAIDF SDG", "evaluate SDG output quality", "run per-sample
NVIDIA-TAO/tao-skill-bank
paidf-cosmos-predict
Prepare and run PAIDF Cosmos Predict video generation for DEFT media samples.
NVIDIA-TAO/tao-skill-bank
REPLACE-WITH-PLATFORM-NAME
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).
NVIDIA-TAO/tao-skill-bank
tao-analyze-gaps-visual-changenet
Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the pinned TAO data-services container directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, or auditing PASS/NO_PASS boundary cases.
NVIDIA-TAO/tao-skill-bank
tao-convert-dataset-format
Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data. Use when the user asks to convert a DAFT dataset, change DAFT format, change a TAO dataset format, or run `tao-daft convert`.
NVIDIA-TAO/tao-skill-bank
tao-finetune-huggingface-model
Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container when no dedicated TAO model skill matches. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Suppo
NVIDIA-TAO/tao-skill-bank
tao-finetune-nv-tesseract-forecasting
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", or "fine-tune forecasting", or mentions "nv-tesseract-forecasting", "moment_head_512_6hr", or "run8_best_model_cr".
NVIDIA-TAO/tao-skill-bank
tao-generate-image-grounding
Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them to pixel-space bounding boxes via a VLM. Use when the user wants to ground captions to bboxes, generate phrase-grounded annotations, auto-label images for grounding, or run the image_grounding pipeline. Triggers include 'image grounding', 'phrase grounding', 'ground captions', 'auto-label image grounding', 'image_grounding'.
NVIDIA-TAO/tao-skill-bank
tao-generate-referring-expressions
Four-step image referring-expression pipeline: turns images plus KITTI bounding-box labels into region descriptions, scene captions, grounded referring expressions, and (optionally) verified expressions via VLM distillation. Use when the user wants to generate referring-expression annotations from images with KITTI labels, build region descriptions, produce grouped grounding phrases tied to bboxes, run a double-check verification pass on grounding expressions, auto-label traffic / scene images f
NVIDIA-TAO/tao-skill-bank
tao-generate-video-reasoning-annotations
Multi-step video annotation pipeline that turns raw videos into Chain-of-Thought training data — multi-level captions, structured descriptions, and QA pairs (MCQ, binary, open-ended) with reasoning traces, via VLM/LLM distillation. Use when the user wants to "create video training data", "generate video QA datasets", "build CoT reasoning traces from videos", "auto-label videos", or run the video_reasoning_annotation pipeline. Triggers include "video annotation", "video CoT", "video QA", "chain-o
NVIDIA-TAO/tao-skill-bank
tao-launch-workflow
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",
NVIDIA-TAO/tao-skill-bank
tao-run-automl
Run container-backed AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm selection (bayesian, hyperband, asha, bohb, llm, hybrid, autoresearch), WandB experiment tracking, job execution on any TAO SDK platform, result interpretation, and per-rec custom evaluation hooks. Use when the user mentions TAO AutoML, hyperparameter optimization, HPO, automl, automl_settings, AutoMLRunner, tao_automl, bayesian search, hyperband, ASHA, LLM-guided search,
Pinned paths
Every entry links to a specific source path and commit when available.
Repository context
Activity, license, and repository-level popularity are shown as context.
Open the source
Inspect the public repository