Loading...
Loading...
Found 1,289 Skills
YC-style office hours partner. Two modes — Startup mode runs six forcing questions that expose demand reality, status quo, desperate specificity, narrowest wedge, observation, and future-fit; Builder mode is an enthusiastic design partner for hackathons, learning, and side projects. Produces a design doc, never code. Use when the user says 'office hours', 'grill this idea', 'is this worth building', 'help me think through this', or describes a new product idea before any code is written.
Add a new cuTile GPU kernel operator to TileGym. Covers dispatch registration in ops.py, cuTile backend implementation, __init__.py exports, test creation, and benchmark in tests/benchmark. Use when adding, creating, or implementing a new cuTile operator/kernel in TileGym, or when asking how to register a new cuTile op.
External NeMo-RL end-to-end validation workflow for Megatron-Bridge model/provider changes, including downstream compatibility checks, external RL lifecycle behavior, Megatron policy setup, HF import/export, checkpoint/resume, non-colocated vLLM refit, delta weight transfer, optional LoRA/generation variants, and questions such as "does this model work in NeMo-RL", "run NeMo-RL e2e", or "external RL loop validation". Covers running NeMo-RL Megatron policy jobs from a Bridge checkout, choosing GRPO/SFT/checkpoint/non-colocated refit variants, setting PYTHONPATH so NeMo-RL imports the local Bridge tree, and reporting pass/fail evidence.
This skill should be used when the user asks to "make a GIF", "convert to GIF", "create a GIF from this video", "export as GIF", "turn this clip into a GIF", "make an animated GIF", or "gif this".
Redis security guidance covering authentication (requirepass and ACL users), TLS, ACL-based least-privilege access control, restricting network exposure via bind and protected-mode, firewall rules, and disabling dangerous commands. Use when deploying Redis to production, defining ACL users for an application, configuring TLS connections, locking down a Redis instance behind a firewall, or auditing a Redis deployment for security hardening.
Extract Feishu (Lark) Docs, Wiki pages, Wiki collections/hubs, spreadsheets, and Minutes (妙记) transcripts into clean high-fidelity local Markdown. The primary path is the lark-cli API — programmatic extraction with no LLM rewriting of the body — which recursively follows a collection's reference graph (mention-doc / sheet / cross-tenant links) and uses error codes to resolve permission boundaries precisely; a browser-DOM path is the fallback only when lark-cli cannot reach the content. Use this whenever the source is a Feishu/Lark URL and fidelity matters — including 导出飞书文档/合集/妙记转写, 把飞书 wiki/知识库转 markdown, scraping or archiving a Feishu collection, exporting a Feishu Minutes/妙记 transcript, or saving a Feishu page locally — even if the user only says clipping, archiving, converting, or "save this". Also covers the permission-denied path (owner-exported .docx → faithful Markdown with heading/highlight restoration).
Import transactions from CSV, OFX, or QIF bank exports and deduplicate.
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
Shared launch intake for any TAO workflow or action. Use when the user wants to run TAO AutoML, train, evaluate, infer, export, generate TensorRT engines, or launch DEFT/workflow jobs on an execution platform.
Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. Masks random patches and reconstructs them to learn visual representations; supports pretrain and finetune stages. Use when training, evaluating, exporting, or running inference for a TAO MAE backbone. Trigger phrases include "pretrain MAE", "self-supervised vision pretraining", "Masked Autoencoder", "Mask Auto-Encoder", "MAE fine-tune".
OCDNet for scene text detection. Detects arbitrary-oriented text regions in natural images using a differentiable binarization approach. Use when training, evaluating, exporting, pruning, quantizing, retraining, or running inference for a TAO OCDNet model. Trigger phrases include "train OCDNet", "scene text detection", "arbitrary-oriented text boxes", "differentiable binarization detector".
Mask Grounding DINO for grounded instance segmentation. Extends Grounding DINO with a mask-prediction head for open-set segmentation guided by text prompts. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Mask-Grounding-DINO model. Trigger phrases include "train Mask Grounding DINO", "open-vocabulary segmentation", "text-prompted instance segmentation", "grounded mask DETR".