Results for “product”

MLflow, model versioning, experiment tracking, model registry, and production ML systems

Extract Amazon product reviews by ASIN using the BrowserAct API, returning structured data including ratings, text, reviewer info, and verified purchase status.

Generates 3D models from text or images via fal.ai, useful for game assets, AR previews, product mockups, and concept sculpting.

Use when implementing mcp functionality with production-grade patterns and safeguards.

Shopify Admin & Storefront GraphQL APIs via curl. Products, orders, customers, inventory, metafields.

Extracts Amazon product details, buy box owner, other sellers' prices, and seller ratings for a given ASIN using the BrowserAct API.

Design habit-forming product loops using the Hook Model (Trigger, Action, Variable Reward, Investment) to analyze and improve user engagement, retention, and re-engagement strategies.

Generates, edits, and transforms images via the Bria API, including text-to-image, background removal, product photography, and batch processing for e-commerce catalogs.

Generate visual storyboards with AI image generation, covering shot types, camera angles, movement, continuity rules, and panel layout for video planning and pre-production.

Runs an autonomous multi-agent software development pipeline that takes a PRD through to production with zero human intervention, using model-tiered agents, memory, and verification cycles.

Expert en data mesh (domain ownership, data products, self-serve, gouvernance fédérée)

Plans, builds, and deploys a complete Ethereum dApp using a three-phase build system with local fork, testnet, and production stages, plus a secret safety protocol and ERC-8004/x402 commerce flow.

Compact the current conversation into a handoff document for another agent to pick up.

Expert strategy advisor for products built on LLMs or agents — not general product strategy (see `product-strategy` for that). Use this — proactively and without waiting to be asked — whenever choosing where to apply AI in a product, deciding between RAG and fine-tuning, designing how much autonomy an AI feature should have, evaluating whether an AI feature is actually defensible, or deciding whether to add AI to a feature at all. Also triggers for: "should this be an agent or a simple LLM call", "how much autonomy should this feature have", "RAG vs fine-tuning", "is this AI feature defensible", "our AI feature keeps hallucinating and users don't trust it", "should we build this with AI or just ship it deterministic", "AI product wedge", "what happens to this feature when the models get better", "human-in-the-loop design for AI features". Produces a decision-focused brief: the wedge, the architecture choice, the autonomy level, and the defensibility bet — each with an explicit trade-off.

Composite skill — full debugging workflow from "this is broken" to root cause and fix. Chains systematic-debugging (root-cause hypotheses) → tracer agent (evidence walk) → sentry (production correlation if applicable) → ci-watch (regression check) → incident-response (if production-impacting). Use when a bug needs deep investigation, not just a quick fix.

Build MCP tools that are sticky for vibe coders and powerful for developersUse when "Designing new MCP tools, Improving tool UX or DX, Writing error messages, Planning tool naming, Discussing user onboarding, Making tools "sticky", Vibe coder experience, mcp, product, ux, dx, vibe-coding, onboarding, developer-experience, tool-design" mentioned.

Derive a portable product-idea kickoff prompt from the invoking repository for use with $idea-scope-brief in a different repository. Use when a repo suggests an adjacent product, SaaS, devtool, library, workflow, or experiment worth briefing elsewhere, but the user wants an idea prompt rather than code extraction or repo creation.

Packet-first BMAD/BMM front door for idea notes, product briefs, PRDs, architecture drafts, review feedback, existing repo state, and milestone pressure. Use when the user wants to know what BMAD phase or artifact comes next, or needs a portable BMAD entrypoint before routing review, execution slicing, runtime setup, or game-production work outward.

Generates and edits images using GPT Image 2 across three modes: direct generation via OpenAI-compatible API, prompt engineering for host-native image tools, or pure prompt advisory. Includes 80+ structured templates for posters, UI mockups, product visuals, maps, slides, and more.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

Agent profile for art direction in 3D web design, product configurators, hero scenes, AR previews, and immersive visual interfaces.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

Expert prompt optimization system for the prompts INSIDE an AI product you are building — system prompts, LLM feature prompts, chatbot/agent instructions. Use when the user wants to write or improve a system prompt for an AI feature they're shipping, review/critique an LLM prompt, apply prompt-engineering techniques (chain-of-thought, few-shot, structured output, hard constraints) to a product prompt, or optimize cost/latency of a production prompt. Do NOT use this to clarify or structure the user's own vague request to Claude Code — that is `prompt-clarifier`'s job, not this skill's.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

Automate Flexisign design and production tasks through Composio's Flexisign toolkit via Rube MCP.

Build and deploy machine learning models with TensorFlow, covering Keras, data pipelines, and production serving.

For analysis/task/review agents using a Professional Skill on models, RAG, agents, evaluation, or safety; not for work without AI decision impact.

Instrument LLM applications with OpenInference tracing for Phoenix AI observability, covering setup, custom spans, and production deployment.

MLOps Engineer IA — Expert en déploiement de modèles ML (A/B testing, feature stores, MLflow, Kubeflow). Industrialise le ML du notebook à la production.

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations from Arize AI.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

Generate a complete, production-ready MCP server project in TypeScript with tools, resources, and proper configuration.

Design the structure, hierarchy, and navigation model for a product's content and features.

Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI.