Build full-stack applications on top of Microsoft Fabric using Python, ODBC, XMLA, and REST APIs. Use when the request involves building applications connected to Fabric data. Delegates endpoint-specific implementation to specialized skills.
Haiku 5.5 decision sampler for the deciding-with-confidence skill. Answers one sample prompt from decide.py emit --agent decider with a probability per option, as JSON, with no tools and no prose. Dispatch several in parallel, then pool the replies with decide.py aggregate; one reply alone is not a calibrated answer.
Reviews diffs that add or change a write-time enrichment feature / retrieval channel for training-serving skew — verifies the feature is wired into the ONE canonical ingest path AND the eval seed AND the seed liveness contract, so it can't ship "merged-but-inert". Use after changes touching ingest enrichment, a new…
Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.
Use for full page or route-level WCAG accessibility audits. Triggers when the user asks to 'audit the page', 'full a11y audit', 'accessibility sign-off', or 'WCAG page audit'. For single component audits use a11y-component-audit.
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques.
Petra - The Wordsmith ✍️. Prompt engineering specialist who crafts instructions that work with LLM mechanics. Invoke when creating agent definitions, system prompts, or LLM instructions. Leverages token prediction, attention mechanisms, and pattern reinforcement for maximum effectiveness.
I am the NVIDIA Dynamo Expert Agent, specialized in all aspects of NVIDIA's high-throughput, low-latency inference framework for distributed LLM serving. I possess comprehensive knowledge of Dynamo's architecture, deployment strategies, performance optimization, and operational best practices.
Prompt engineer. Designs, evaluates, and optimizes prompts for LLMs — system prompts, few-shot templates, chain-of-thought patterns, and production prompt systems. Use when building a new agent, tuning an existing system prompt, evaluating prompt quality, or debugging inconsistent LLM output. Reports honesty ledger on…
An agent for auditing AI prompt development against company standards. It reviews prompt structure, type definitions, safety controls, versioning, tests, and requirements for different use cases.
Builds or updates Flowise AgentFlow v2 JSON with selective retrieval, structural validation, and Floweyes audit. Use for Flowise generation and repair tasks.
Use this agent when the user needs to discover external data sources — APIs, datasets, open data portals, and commercial data providers — to fulfil project requirements. This agent performs extensive web research to find real, current data sources. Examples: Context: User has a project with requirements and wants to…
Autonomous data engineering assistant powered by Data Workers — 11 specialized agents with 160+ tools for pipelines, incidents, catalog, quality, schema, governance, observability, connectors, usage intelligence, orchestration, and ML.
Use for Vercel AI SDK v5 implementation work: chat interfaces, tool calling, streaming, agentic loop control, type-safe custom messages, or migrating from v4. Covers useChat, streamText, generateText, tools, and the React/Vue/Svelte/Angular integrations.
Use this agent when you need to design, optimize, or troubleshoot data engineering pipelines, process structured or unstructured data, implement ETL/ELT workflows, or apply data engineering best practices. Examples: Context: User needs to create an efficient data pipeline for processing financial transaction data from…
Phase 1: map raw inputs to canonical contract and produce ingest evidence + mapping artifacts. Use when starting a verification run. Trigger with 'phase 1 ingest', 'map schema', or 'canonicalize inputs'.
Expert in AI/ML integration, LLM applications, RAG systems, embeddings, prompt engineering, and AI agent architecture. Use for building AI-powered features, chatbots, semantic search, content generation, and AI product development. Triggers on ai, llm, gpt, claude, openai, embeddings, rag, vector, prompt, chatbot…
PROACTIVELY scan codebases for LLM API usage patterns and classify each call by task type, model used, and optimization potential.
★not rated 9 3mo agoA32 tokens
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: