Designs a Microsoft Foundry (formerly Azure AI Foundry) footprint on Azure — surface map, model catalog (1,900+ via Azure OpenAI + partners), Agent Service (GA Mar 16 2026), private networking (BYO VNet), Foundry IQ + Azure AI Search, safety stack (Prompt Shields + Groundedness), cost model (PTU / PAYG / Batch), lock-in posture. Use when scoping a new Foundry deployment, choosing between Foundry and Azure ML for a workload, or auditing an existing footprint. Adjacent to `/bedrock-design` (AWS), `/vertex-ai-design` (GCP), `/sagemaker-design` (AWS classical ML).
Designs an Amazon Bedrock footprint on AWS — service split (Models / AgentCore / Knowledge Bases / Guardrails / Flows / Prompt Mgmt / IPR / Eval / Custom Import), model selection (Claude / Nova 2 / Llama / Mistral / Cohere / AI21), inference pattern (on-demand / PT / batch / cross-region), agent runtime (AgentCore vs Bedrock Agents vs Flows), guardrails posture (incl. Automated Reasoning), routing + cost (IPR), MLOps wiring, and lock-in posture. Use when scoping a new Bedrock deployment, choosing between Bedrock services for a workload, or auditing an existing footprint. Adjacent to `/sagemaker-design` (AWS classical ML side), `/vertex-ai-design` (GCP), `/azure-foundry-design` (Azure).
Designs an OpenAI platform footprint — API surface (Responses API as default; Assistants sunset 2026-08-26; Chat Completions feature-frozen on GPT-5.4+ reasoning models), model catalog (GPT-5.5/5.4/4.1/o3/o4-mini), Agents SDK (Handoffs + Guardrails + Tracing), Realtime API (gpt-realtime GA 2025-08-28), Vector Stores + File Search, tools (Web Search + Computer Use + Code Interpreter + Structured Outputs strict), Deep Research cost trap, prompt caching automatic ≥1024 tokens, fine-tune trio (SFT/DPO/RFT), **Evals sunset 2026-11-30**. Use when building on OpenAI directly (not Azure OpenAI). Adjacent to `/azure-foundry-design` (OpenAI on Azure), `/bedrock-design` (AWS), `/vertex-ai-design` (GCP), `/anthropic-api-design` (when authored).
Designs a Vertex AI footprint on Google Cloud — service split (Workbench / Pipelines / Training / Endpoints / Feature Store / Model Garden / Model Monitoring), compute selection, MLOps wiring, deployment pattern (online vs batch vs streaming), cost guardrails, and observability. Use when scoping a new Vertex deployment, choosing between Vertex services for a workload, or auditing an existing Vertex footprint. Adjacent to `/databricks-asset-bundles` + `/databricks-model-serving` (Databricks side) and `/sagemaker-design` (AWS side).
Change-Data-Capture Architect — designs the capture-side of a CDC pipeline from an OLTP source into the lake/warehouse. Selects capture mechanism (log-based vs query-based vs trigger-based), designs the initial-snapshot → incremental cutover without gaps or duplicates, handles deletes/tombstones, propagates schema/DDL changes, and specifies idempotent dedup into the sink. Use when asked to "capture changes from a database", "set up CDC", "Debezium", "logical decoding / binlog / WAL", "stream a Postgres/MySQL/SQL Server table into the lake", or when an existing replica/ETL keeps missing deletes or double-counting rows. Defers stream-processing topology to /streaming-pipeline, sink-side APPLY CHANGES / SCD merge to /delta-live-tables, and batch-vs-stream framing to /pipeline-design.
Designs distribution-free uncertainty quantification via conformal prediction — prediction SETS for classification and prediction INTERVALS for regression with a finite-sample coverage guarantee at 1−alpha. Use when asked for "conformal prediction", "prediction sets", "guaranteed coverage", "distribution-free uncertainty", "calibrated prediction intervals", "how big should my calibration set be", or when a downstream decision needs a coverage guarantee that probability calibration alone cannot give. Distinct from `/model-calibration` (Platt/isotonic/temperature/ECE — trustworthy probability, NO set, NO coverage guarantee); pairs with `/decision-threshold-policy` (owns the abstain/route action); defers parametric forecast intervals to `/time-series-forecasting`.
Doc-CI gate for repos whose deliverable includes a document set. Scans for (1) count drift across README/CLAUDE.md/NEXT_SESSION.md/prompts indices, (2) broken relative links, (3) skill↔prompt↔CLAUDE.md↔README↔prompts/README parity, (4) ungraded external facts, (5) markdown table pipe-integrity (stray-pipe phantom tables). Severity-grouped report. Use BEFORE shipping any docs commit, AFTER adding a skill/prompt/hook, and as a pre-commit gate. Pair with `.github/workflows/doc-ci.yml` for CI enforcement.
Designs a Data Loss Prevention (DLP) system — data classification, egress-surface enumeration, detection technique per class, enforcement per (class × surface), policy engine, and response/audit. Use when asked to prevent data exfiltration/leakage, design DLP controls, stop secrets/PII/PHI/PCI from leaving a system, or gate data egress (prompts, model output, tool actions, logs, network, files, third-party APIs).