Results for “model-schema”

`task-agent`: use when physical relational schema or database-enforced integrity changes; skip conceptual-model, repository-only, or unchanged relational-storage work.

`analysis-agent`/`task-agent`: use when commit timing, payloads, schema, ordering, idempotency, or retry changes; skip when no domain-event decision is required.

Records Clean Room initialization preferences, separated artifact locations, model policy, schema profile, and clean-safe rule defaults before a clean-room run starts or resumes.

Guarantee valid JSON, XML, or code structure during text generation using Pydantic models for type-safe outputs, supporting local models (Transformers, vLLM, llama.cpp) and maximizing inference speed with structured generation.

Audit, plan, implement, or review Model Context Protocol version and SDK migrations. Use for MCP 2026-07-28, stateless Streamable HTTP, server/discover, removal of initialize or Mcp-Session-Id, MCP Tasks extension changes, full JSON Schema 2020-12 tool schemas, OAuth issuer hardening, deprecated roots/sampling/logging, or cross-version client/server compatibility.

Generates structured VGL JSON for Bria FIBO models, giving deterministic control over objects, lighting, camera, composition, and style instead of natural language prompts.

Interact with MCP servers from the command line to discover, inspect, and call tools for external systems like GitHub, filesystems, databases, and APIs.

Optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources, distinct from traditional SEO.

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.

Generate complete Power Platform custom connectors with Model Context Protocol integration for Copilot Studio, including schema generation, validation, troubleshooting, and certification preparation.

Define every visual attribute as structured VGL JSON for deterministic, reproducible image generation with Bria FIBO models, covering objects, lighting, camera settings, composition, and style.

Search, explore, and run fal.ai generative AI models for image, video, audio, and 3D generation, including schema lookup, job submission, status polling, result retrieval, and file uploads.

Recommend model families and validation strategy based on data, constraints, and objective. Use when: (1) choosing algorithms, (2) balancing bias/variance, (3) planning benchmark baselines. NOT for: final legal/compliance sign-off.

Search, explore, and run fal.ai generative AI models for image, video, audio, and 3D generation, including schema lookup, job submission, status polling, result retrieval, and file uploads.

Manage machine learning models in the Agent Platform Model Registry: list, describe, upload, update, and delete models and their versions.

Fine-tune open models or Gemini models using Agent Platform infrastructure, from environment setup through data preparation, job configuration, monitoring, and deployment.

Plan which model tier handles which work BEFORE execution begins — a high-cognition model deeply understands the problem, lays the foundations, then emits a modular plan assigning each module the cheapest tier that can safely execute it, with escalation tripwires and one-way-door protection. Advisory only: it announces "next module → tier X / model Y" at each boundary and the HUMAN switches models — harnesses like Cursor cannot switch mid-run. Load when the user asks which model to use, wants a model plan, model tiers, model-tier routing, assign models to tasks or modules, says "cheap model got stuck", "which model for this task", "cost-efficient model choice", or when implementation-plan / problem-to-plan need a model: tier column. NOT dynamic-routing (plan-path selection after failure) — this skill assigns cognition tiers to work.

Rank a topic's user-flow branches by proof priority (value × risk × frequency) right after user-flow-map, ordering the branches, gating variation breadth, and promoting or pruning flows so state-model and ux-variations grow the tree in proof order — writes only existing flow-tree ordering fields, no schema change.

Agent skill for data-ml-model - invoke with $agent-data-ml-model

Cross-model benchmark for gstack skills. (gstack)

TRAK: Attributing Model Behavior at Scale

Evaluate model quality with task-appropriate metrics and systematic error analysis. Use when: (1) comparing models, (2) analyzing failures, (3) setting go/no-go thresholds. NOT for: production monitoring implementation.

Use the model-recommender skill, Workflow D (Setup Questionnaire), to configure model access and project-level preferences.

Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.

database-model-standards

Guides users through participating in the SOMA decentralized training network, covering data submission, model training, reward claiming, and strategic optimization.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Build logic models linking activities to impact. TRIGGERS - Use when user needs help with logic-model related tasks.