Skill UI

AFL++ is an advanced fork of AFL that runs multi-core campaigns, supports diverse mutations, and targets large C/C++ codebases with stable tooling via Docker or host builds.

Use Declarative Automation Bundles (DABs) to manage and deploy Databricks assets, including jobs, DLT pipelines, and ML models, using an Infrastructure-as-Code approach. This skill allows defining resources and deployment targets (dev, staging, prod) in a single YAML file, enabling robust CI/CD practices and controlled promotion of changes across environments.

Guides engineers through initializing MCUs, crafting RTOS-aware drivers, and tuning power/timing for STM32, ESP32, and bare-metal targets; covers constraint analysis, interrupt safety, resource tracking, and validation with static analysis and real-time debugging.

Knowledge Distillation is a crucial technique used to compress large, high-performing Language Models (Teacher) into smaller, more efficient Student models. This allows developers to deploy powerful AI capabilities (like those of GPT-4) on resource-constrained devices while significantly reducing inference costs. The method uses advanced techniques such as soft targets, temperature scaling, and Reverse KL Divergence (MiniLLM) to transfer knowledge effectively.

Runs build, test, lint, serve, and other tasks in Nx workspaces via run, run-many, and affected flows, honoring the detected package manager and target metadata while exposing filters, parallelism, and caching controls for CI or local workflows.

This skill specializes in implementing Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to enforce reliability standards. It guides users in designing comprehensive SLO frameworks, calculating error budgets, and building robust monitoring systems. Use this when you need to establish data-driven reliability targets, balance feature velocity with technical health, and align service performance directly with core business objectives.

Comprehensive skill set covering the full lifecycle of advanced visual AI systems. Expertise includes object detection (YOLO, Faster R-CNN), instance and semantic segmentation (Mask R-CNN, SAM), model training using PyTorch, and production deployment optimization using ONNX and TensorRT for edge or cloud targets.

A specialized tool for comprehensive content optimization and SEO strategy. It analyzes existing content to calculate keyword density, identify primary and secondary targets, and generate relevant LSI (Latent Semantic Indexing) keywords and semantic variations. Use this skill proactively to improve topical authority and ensure natural, high-ranking content structure.

This framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.

Defines and monitors SLAs, SLIs, and SLOs so teams can set performance targets, track metrics like error budgets and availability, and react before issues impact customers.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.

Programmatically query ChEMBL bioactive molecules, targets, and approved drugs via the Python client; filter by structure, properties, activities, and indications to support inhibitor hunting or SAR studies for medicinal chemistry.