ai-llm-patterns
Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
163 skills found
Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.
Automated CI/CD incident response, failure analysis, and remediation for GitHub Actions pipelines. Resolves build and test failures with safety guardrails.
Conduct systematic literature reviews across PubMed, arXiv, and Semantic Scholar with AI-driven synthesis, verified citations, and mandatory schematic visualization.
Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.
Read and control I2C and SPI peripherals on Sipeed boards like LicheeRV Nano, MaixCAM, and NanoKVM.
Generate professional Excalidraw diagrams, flowcharts, and technical architecture visualizations directly via Python.
Three.js material library: PBR, basic, phong, shader materials, and properties. Essential for styling meshes, texture mapping, custom GLSL shaders, and optimizing 3D material performance.
Draft competitive research proposals for NSF, NIH, DOE, DARPA, and NSTC. Master agency-specific criteria, budget preparation, visual schematics, and submission compliance.
Verify code style and formatting using Prettier and Stylelint without applying changes. Ensures consistent codebases by identifying issues in JS/TS/CSS/SCSS files.
Generate bespoke pixel art SVG illustrations for your documentation, READMEs, and presentations. Perfect for character sprites, chat dialogue visualizations, and custom branding.
Log ideas, notes, and learning progress chronologically to project archives using a CLI helper tool for systematic knowledge retention.
A reinforcement learning-inspired tracker for YouTube performance, using systematic logging to optimize thumbnails, titles, and hooks.