Results for “gff”

Use when printing canned sample NCBI XML, JSON, flatfile, or GFF documents for testing, parser development, or xtract query prototyping.

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

Use when you need to annotate BAM alignments with a two-character tag based on overlaps with BED, GFF, or VCF annotation files, such as labeling reads by feature class or interval source.

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

Gdb reference tool. Use when working with gdb in devtools contexts.

Convert and quantize models to GGUF format for efficient CPU/GPU inference with llama.cpp, supporting 2-8 bit quantization and Apple Silicon acceleration.

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

Imported skill gloock_ofl from anthropic

Use this skill for glTF/GLB assets, compression, optimization, transforms, texture sizing, browser delivery. Trigger when the task involves 3d web work related to Gltf Asset Pipeline, production implementation, audits, debugging, strategy, or validation.

Use when working with GenBank format files and need to create reference indexers for sequence data retrieval or processing within the Entrez Direct toolkit.

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

CLI interface for igf (Grapefruit) dynamic instrumentation server. Use to enumerate Frida devices, list apps, run hooks, query logs, access device file systems, inspect classes, dump memory, and perform mobile app security analysis.

Use when converting GenBank format (.gbf) files to FASTA format (.fsa) as part of sequence data preprocessing

Estimates the memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub, using HTTP Range requests without downloading weights.

Configures GKE storage including PVCs, PersistentVolumes, Filestore, and GCS FUSE with best practices for production workloads.

Search the Hugging Face Hub for llama.cpp-compatible GGUF models, select the right quantization, and run them locally with llama-cli or llama-server.

Prüft KG- und Personengesellschaften-Fälle in Großkanzlei Corporate/M&A-Mandaten: Frist, Form, Zuständigkeit, Rechtsweg und Sofortmaßnahmen; liefert eine Fristen- und Risikoampel mit Sofortschritten.

Quantize large language models to 4-bit with minimal accuracy loss using GPTQ, enabling deployment of 70B+ models on consumer GPUs with 4× memory reduction and 3-4× faster inference.

llama.cpp local GGUF inference + HF Hub model discovery.

Estimates GPU memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub using HTTP Range requests, without downloading weights locally.

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Discover hidden content, directories, subdomains, and API endpoints with ffuf — the fastest web fuzzer. Use when someone asks to "find hidden directories", "fuzz URLs", "discover API endpoints", "subdomain enumeration", "content discovery", "ffuf", "brute force paths", or "find hidden files on a website". Covers directory fuzzing, parameter fuzzing, subdomain discovery, virtual host enumeration, and recursive scanning.

Expert en inference llama.cpp avancé (GGUF, quantization, local models, HTTP server, hardware)

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Filters user-generated content for safety using Google Model Armor via the gws CLI.

Renews or reactivates Google Workspace Events subscriptions, either individually by name or all subscriptions expiring within a specified time window.

Generates and edits images and videos via fal.ai's queue-based API, supporting models like Flux, Gemini image, and Kling video-to-video, with automatic polling.

Greenfield development path for products, tools, and systems that do not yet exist. G Stack validates the idea before anything is planned or built. Use when starting from zero: no existing codebase, no existing schema, no existing API that this extends. AUTO-FIRE immediately when the user says any of: - "new development", "/new-development [description]" - "I have an idea for [X]", "I want to build [X] from scratch" - "new product", "new tool", "new platform", "new application" - "build [X] — it doesn't exist yet", "start a new project" Do NOT use when an existing codebase already exists for the work — use /full-development for large brownfield work. If unsure: ask "Does any part of this already exist in code?" If yes → /full-development. This is the most token-intensive command. Use it when the investment is justified by the scope of what is being built.

Create context handoff when pausing work mid-phase

GOF State Pattern

Generates unit tests for code changed in the current branch by comparing against origin/main and following existing test conventions.

GOF Singleton