claude-cookbooks
Claude AI cookbooks - code examples, tutorials, and best practices for using Claude API. Use when learning Claude API integration, building Claude-powered applications, or exploring Claude capabilities.
Claude AI cookbooks - code examples, tutorials, and best practices for using Claude API. Use when learning Claude API integration, building Claude-powered applications, or exploring Claude capabilities.
Generates onboarding code snippets for Phoenix tracing integrations and wires them into the project onboarding UI. Produces install dependencies and implementation sections for SDKs like OpenAI, LangChain, Vercel AI SDK, and others. Supports Python and TypeScript. Use when asked to create onboarding code, tracing setup snippets, quickstart examples, or getting-started code for a framework integration.
Create a new built-in classification evaluator for Phoenix evals. Use this skill whenever the user asks to create a new eval, build a new metric, add a new builtin evaluator, create an LLM-as-a-judge metric, or add a new classification evaluator to Phoenix.
Complete Hindsight documentation for AI agents. Use this to learn about Hindsight architecture, APIs, configuration, and best practices.
Extracts specific local activity recommendations and sentiment from travel vlogs into a shareable HTML BD report.
Transforms raw metrics and analysis into visual charts and published, shareable HTML reports using Google Cloud Storage.
This skill should be used when users need to create or debug Bloblang transformation scripts. Trigger when users ask about transforming data, mapping fields, parsing JSON/CSV/XML, converting timestamps, filtering arrays, or mention "bloblang", "blobl", "mapping processor", or describe any data transformation need like "convert this to that" or "transform my JSON".
This skill should be used when users need to create or fix Redpanda Connect pipeline configurations. Trigger when users mention "config", "pipeline", "YAML", "create a config", "fix my config", "validate my pipeline", or describe a streaming pipeline need like "read from Kafka and write to S3".
Add a new AI provider integration to the Sentry JavaScript SDK. Use when contributing a new AI instrumentation (OpenAI, Anthropic, Vercel AI, LangChain, etc.) or modifying an existing one.
Generate per-asset visual specifications and AI generation prompts from GDDs, level docs, or character profiles. Produces structured spec files and updates the master asset manifest. Run after art bible and GDD/level design are approved, before production begins.
Configure the project's game engine and version. Pins the engine in CLAUDE.md, detects knowledge gaps, and populates engine reference docs via WebSearch when the version is beyond the LLM's training data.
Explains core Apache Beam programming model concepts including PCollections, PTransforms, Pipelines, and Runners. Use when learning Beam fundamentals or explaining pipeline concepts.
Guides development and usage of I/O connectors in Apache Beam. Use when working with I/O connectors, creating new connectors, or debugging data source/sink issues.
Guides Python SDK development in Apache Beam, including environment setup, testing, building, and running pipelines. Use when working with Python code in sdks/python/.
Use the MemOS Local memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query and call memory_search. Available tools: memory_search, memory_get, memory_write_public, memory_share, memory_unshare, task_summary, skill_get, skill_search, skill_install, skill_publish, skill_unpublish, network_memory_detail, network_skill_pull, network_team_info, memory_timeline, memory_viewer.
Construct optimized search URLs for major platforms and navigate to results with the browser. Replaces the built-in web_search tool for targeted, platform-specific searches.
Deploy and configure Higress AI Gateway for OpenClaw integration. Use when: (1) User wants to deploy Higress AI Gateway, (2) User wants to configure OpenClaw to use more model providers, (3) User mentions 'higress', 'ai gateway', 'model gateway', 'AI网关', (4) User wants to set up model routing or auto-routing, (5) User needs to manage LLM provider API keys.
Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.
Performs pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets.
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.