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Found 1,833 Skills
Dense vector embeddings, semantic search, RAG pipelines, and reranking via Together AI. Generate embeddings with open-source models and rerank results behind dedicated endpoints. Reach for it whenever the user needs vector representations or retrieval quality improvements rather than direct text generation.
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
Use when building Apache Spark applications, distributed data processing pipelines, or optimizing big data workloads. Invoke for DataFrame API, Spark SQL, RDD operations, performance tuning, streaming analytics.
Video production workflow orchestrator for Veo 3. Guides users through creating professional video prompts via two paths - direct text-to-video OR image-to-video pipeline (Imagen 3/4 → Veo 3). Validates prompt completeness, checks conflicts, ensures all mandatory components present. Integrates camera-movements, great-prompt-anatomy, short-prompt-guide, long-prompt-guide, and imagine skills.
Enterprise skill for iOS production error observability and logging (iOS 15+, Swift 5.5+). Use this skill when writing or reviewing error handling code, adding logging to iOS apps, replacing print() with os.Logger, configuring crash reporting SDKs (Sentry, Crashlytics, PostHog), fixing silent error patterns (try?, Task {} swallowing errors, Combine pipelines dying), adding privacy annotations to logs, integrating MetricKit, implementing retry logic with observability, handling errors in SwiftUI .task {} modifiers, or auditing catch blocks for proper error reporting. Use this skill any time someone writes a catch block, uses try?, creates a Task {}, sets up error handling, or mentions logging, crash reporting, or error tracking in an iOS context — even if they just say 'add error handling' or 'why is this failing silently.'
Create business process and integration diagrams using PlantUML syntax with BPMN, EIP, and Lean Mapping stencil icons. Best for workflow automation, approval processes, message-based integration patterns, ETL pipelines, and value stream mapping. NOT for simple flowcharts (use mermaid) or UML activity diagrams (use uml skill).
Four-step image referring-expression pipeline: turns images plus KITTI bounding-box labels into region descriptions, scene captions, grounded referring expressions, and (optionally) verified expressions via VLM distillation. Use when the user wants to generate referring-expression annotations from images with KITTI labels, build region descriptions, produce grouped grounding phrases tied to bboxes, run a double-check verification pass on grounding expressions, auto-label traffic / scene images for referring datasets, or run the image_referring_expression pipeline. Triggers include 'referring expression', 'region description', 'KITTI labels', 'spatial relationship annotation', 'auto-label image referring expression', 'image_referring_expression'.
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
This skill should be used when users need to scrape websites, extract structured data, handle JavaScript-heavy pages, crawl multiple URLs, or build automated web data pipelines. Includes optimized extraction patterns with schema generation for efficient, LLM-free extraction.
Run cold and warm outreach campaigns to find and engage potential customers or partners. Use when building a prospecting pipeline, writing cold emails or LinkedIn messages, identifying and qualifying leads, planning an outreach strategy, or scaling lead generation as a solopreneur. Covers lead identification, qualification frameworks, cold email writing, LinkedIn outreach, multi-touch sequences, and tracking. Trigger on "cold outreach", "prospecting", "find customers", "cold email", "LinkedIn outreach", "lead generation", "outreach strategy", "build a pipeline", "find clients".
Auto-generate features with encodings, scaling, polynomial features, and interaction terms for ML pipelines.