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Found 563 Skills
Guidance for setting up HuggingFace model inference services with Flask APIs. This skill applies when downloading HuggingFace models, creating inference endpoints, or building ML model serving APIs. Use for tasks involving transformers library, model caching, and REST API creation for ML models.
Guidance for designing fusion protein gBlock sequences from multiple protein sources. This skill applies when tasks involve combining proteins from PDB databases, plasmid files, and fluorescent protein databases into a single optimized DNA sequence with specific linkers and codon optimization requirements.
This skill provides guidance for merging data from multiple heterogeneous sources (JSON, CSV, Parquet, XML, etc.) into a unified dataset. Use this skill when tasks involve combining records from different file formats, applying field mappings, resolving conflicts based on priority rules, or generating merged outputs with conflict reports. Applicable to ETL pipelines, data consolidation, and record deduplication scenarios.
Send and read iMessages/SMS from macOS. Use for texting contacts, scheduling services, or automating message-based workflows. Triggers on queries about texting, messaging, SMS, iMessage, or contacting someone via text.
Configure LLM models and providers for Letta agents and servers. Use when setting model handles, adjusting temperature/tokens, configuring provider-specific settings, setting up BYOK providers, or configuring self-hosted deployments with environment variables.
Guidance for extracting weight matrices from black-box ReLU neural networks using only input-output queries. This skill applies when tasked with recovering internal parameters (weights, biases) of a neural network that can only be queried for outputs, particularly two-layer ReLU networks. Use this skill for model extraction, model stealing, or neural network reverse engineering tasks.
Guidance for implementing PyTorch pipeline parallelism for distributed model training. This skill should be used when tasks involve implementing pipeline parallelism, distributed training with model partitioning across GPUs/ranks, AFAB (All-Forward-All-Backward) scheduling, or inter-rank tensor communication using torch.distributed.
Guidance for counting tokens in datasets, particularly from HuggingFace or similar sources. This skill should be used when tasks involve counting tokens in datasets, understanding dataset schemas, filtering by categories/domains, or working with tokenizers. It helps avoid common pitfalls like incomplete field identification and ambiguous terminology interpretation.
Generate, edit, and compose images using Gemini Nano Banana models via portable Python scripts. Handles authentication via API Key or Vertex AI environment variables. Available parameters: prompt, model, aspect-ratio, safety-filter-level. Always confirm parameters with the user or explicitly state defaults before running.
Guidance for filtering JavaScript and XSS attack vectors from HTML while preserving original formatting. This skill should be used when tasks involve removing script content, sanitizing HTML, filtering XSS payloads, or creating security filters that must preserve the original document structure unchanged.
Guidance for implementing path tracers and ray tracers to reconstruct or generate images. This skill applies when tasks involve writing C/C++ ray tracing code, reconstructing images from reference images, or building rendering systems with spheres, shadows, and procedural textures. Use for image reconstruction tasks requiring similarity matching.
Guidance for setting up QEMU virtual machines with Alpine Linux and SSH access. This skill should be used when tasks involve starting QEMU with Alpine Linux ISO, configuring port forwarding for SSH, setting up OpenSSH server in Alpine, or troubleshooting QEMU networking issues.