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Found 334 Skills
Weekly analytics report combining GA4, Google Search Console, and Microsoft Clarity into a premium HTML email. Use for automated Sunday cron jobs that generate weekly product analytics reports. Tracks users, sessions, pageviews, SEO performance (queries, impressions, CTR, position), UX friction (dead clicks, rage clicks, quickbacks), and week-over-week trends. Includes accountability tracking (did we do what we said?), prioritized recommendations with impact/effort ratings, and actionable checklists. Persona: $15K/month SEO consultant. Outputs: HTML email + chat summary.
Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
Generates a comprehensive client health overview across all accounts. Reads CRM data, support tickets, usage metrics, billing, and engagement logs. Calculates health scores, trend direction, and RAG status per client. Outputs a sorted risk report with recommended actions.
Complete guide for OpenAI's traditional/stateless APIs: Chat Completions (GPT-5, GPT-4o), Embeddings, Images (DALL-E 3), Audio (Whisper + TTS), and Moderation. Includes both Node.js SDK and fetch-based approaches for maximum compatibility. Use when: integrating OpenAI APIs, implementing chat completions with GPT-5/GPT-4o, generating text with streaming, using function calling/tools, creating structured outputs with JSON schemas, implementing embeddings for RAG, generating images with DALL-E 3, transcribing audio with Whisper, synthesizing speech with TTS, moderating content, deploying to Cloudflare Workers, or encountering errors like rate limits (429), invalid API keys (401), function calling failures, streaming parse errors, embeddings dimension mismatches, or token limit exceeded. Keywords: openai api, chat completions, gpt-5, gpt-5-mini, gpt-5-nano, gpt-4o, gpt-4-turbo, openai sdk, openai streaming, function calling, structured output, json schema, openai embeddings, text-embedding-3, dall-e-3, image generation, whisper api, openai tts, text-to-speech, moderation api, openai fetch, cloudflare workers openai, openai rate limit, openai 429, reasoning_effort, verbosity
Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations.
Comprehensive prompt and context engineering for any AI system. Four modes: (1) Craft new prompts from scratch, (2) Analyze existing prompts with diagnostic scoring and optional improvement, (3) Convert prompts between model families (Claude/GPT/Gemini/Llama), (4) Evaluate prompts with test suites and rubrics. Adapts all recommendations to model class (instruction-following vs reasoning). Validates findings against current documentation. Use for system prompts, agent prompts, RAG pipelines, tool definitions, or any LLM context design. NOT for running prompts, generating content, or building agents.
PostgreSQL-based semantic and hybrid search with pgvector and ParadeDB. Use when implementing vector search, semantic search, hybrid search, or full-text search in PostgreSQL. Covers pgvector setup, indexing (HNSW, IVFFlat), hybrid search (FTS + BM25 + RRF), ParadeDB as Elasticsearch alternative, and re-ranking with Cohere/cross-encoders. Supports vector(1536) and halfvec(3072) types for OpenAI embeddings. Triggers: pgvector, vector search, semantic search, hybrid search, embedding search, PostgreSQL RAG, BM25, RRF, HNSW index, similarity search, ParadeDB, pg_search, reranking, Cohere rerank, pg_trgm, trigram, fuzzy search, LIKE, ILIKE, autocomplete, typo tolerance, fuzzystrmatch
Amazon OpenSearch Service and Serverless across five capabilities — migration (Solr/ES/self-managed OpenSearch into AOS/AOSS, schema/query translation, sizing, cutover); provisioning (domain + AOSS lifecycle, upgrades, storage tiers, FGAC, monitoring); search (vector / semantic / hybrid / RAG with Bedrock connectors); log-analytics (PPL, OSI ingestion, anomaly detection, OpenSearch Dashboards, Splunk/Datadog alternatives); trace-analytics (OTel spans, service maps, Data Prepper). Triggers on OpenSearch, AOS, AOSS, Elasticsearch, ELK, Solr, Lucene, vector / k-NN / semantic / hybrid / neural search, RAG, ELSER, log analytics, observability, Kibana, OSI, OCU, PPL, trace analytics, BM25, eDisMax, schema.xml, ILM, ISM, FAISS, HNSW, Migration Assistant for Amazon OpenSearch Service, Historical Data Migration, Live Traffic Migration, UltraWarm, OR1, Splunk/Datadog alternative, moving off Solr. Picks ONE capability per ask, names instance class + count + shard math, ships query DSL examples.
Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series
Use when connecting to a self-hosted memory backend, searching, storing, or managing memories, importing connection tokens, or troubleshooting retrieval issues. Use this skill whenever the user mentions memory search, RAG retrieval, embedding, memory storage, multimodal document upload, knowledge queries, or wants to connect to a memory service, even if they do not explicitly say "transcendence-memory".
PDF data extraction tool. Use it when users mention "PDF extraction", "PDF to Markdown", "PDF parsing", "extract PDF content", "PDF to JSON", "RAG PDF". OpenDataLoader PDF is currently the top-ranked PDF parser in benchmark tests, supporting local mode (fast, deterministic) and hybrid AI mode (for complex tables, scanned documents, formulas), with output formats including Markdown, JSON (with bounding boxes), and HTML. It is suitable for scenarios where structured data needs to be extracted from PDFs for RAG/LLM pipelines, or where batch processing of PDF documents is required.
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).