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Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Use when analyzing or migrating Databricks CDC/Delta workflow scripts into a Huawei Cloud Chile MRS + OBS + Apache Hudi demo, including synthetic CDC data generation, notebook-triggered automation, Delta-to-Hudi replacement, deployment troubleshooting, and smoke validation. Also use when continuing the dockone ExampleApp demo package under the Codex outputs folder.
Configure Claude Code to use Huawei Cloud MaaS or ModelArts MaaS through an OpenAI-compatible endpoint, and optionally add Z.ai web-search-prime MCP search. Use when Codex needs to add a side-by-side claude-glm command that routes to Huawei MaaS glm-5.1 while preserving the original claude command on Anthropic, migrate claude itself to Huawei MaaS, install or configure claude-code-router, set API_KEY-based authentication, adjust context length, verify that Claude Code is actually backed by MaaS, or configure Z.ai MCP search with Z_API_KEY.
Deploy, configure, validate, troubleshoot, or extend an OpenAI-compatible API proxy backed by PostgreSQL, Prometheus, and Grafana, routing through Huawei ModelArts MaaS (ap-southeast-1) with multi-key load balancing. TRIGGER when the task involves LiteLLM proxy deployment, Docker Compose stack with litellm_config.yaml, Huawei MaaS model routing, virtual key or budget management, Prometheus/Grafana observability for LLM traffic, custom_callbacks.py TTFT/TPOT/ITL metrics, multi-key load balancing, or any reference to `LITELLM_MASTER_KEY`, `HUAWEI_MAAS_API_KEY`, or `docker compose` with this stack.
Bootstrap AI coding stack: deploy LiteLLM proxy (via LiteLLM-Huawei-MaaS-Proxy skill), install opencode + oh-my-opencode-slim, mint virtual key, wire everything. Supports multi-key MaaS load balancing. TRIGGER on: opencode + Huawei MaaS setup, full-stack bootstrap, oh-my-opencode-slim-huawei-maas, deploy-litellm.
End-to-end screenshot-to-dashboard pipeline powered by MaaS vision models. Analyze a dashboard screenshot via OpenRouter free vision models, extract design tokens with anydesign, generate design.md + design-tokens.json, plan via CCPM, implement React+Ant Design+ECharts dashboard code, and iterate with screenshot comparison until ≥85% similarity. TRIGGER when the user provides a dashboard screenshot and wants to reconstruct it as a working web app, or says 'rebuild this dashboard', 'convert this screenshot to code', 'reproduce this BI dashboard', 'screenshot to React dashboard'. Do NOT use for: non-dashboard screenshots, Figma-to-code without screenshot, general UI development without a reference image.
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
| name | grill-me |
| description | Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me". |
Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.
Ask the questions one at a time.
If a question can be answered by exploring the codebase, explore the codebase instead.