“Prompt engineering”

Diagnose context stuffing vs.

PLAN phase agent for the ArtOps pack.

This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty…

Generates Spring Boot 3.x configurations, creates REST controllers, implements Spring Security 6 authentication flows, sets up Spring Data JPA repositories…

Orchestrates substantial frontend and UI work with audit-first context, product-aware design direction, shadcn-aware component engineering, Storybook state…

Expert API design and delivery: resource modeling, versioning, consistent error envelopes, pagination/filtering, idempotency, OpenAPI/contracts, and boundary…

Resolve complex literal requirements, data semantics, provenance, and acceptance against the production engineering workflow.

Universal architectural standard for autonomous coding agents to build any software system from scratch.

Expert flyer prompt creator for ChatGPT Image 2.0.

Build editable, responsive and accessible SVG graphics and vector exports.

Transform poor or overly simple prompts with expert-level framing.

Design, audit, run, or improve bounded agent loops whose feedback changes the next action.

Understand the components, mechanics, and constraints of context in agent systems.

Create a handoff brief for another LLM to implement.

Integrate systems after frontend direction.

8-phase agile workflow — strategy→ideate→brainstorm→plan→work→review→compound→pulse.

Prove a system survives failure by injecting it deliberately — game days, fault injection, and steady-state hypotheses.

Compose installed agent skills into one orchestrated multi-agent run: an agent graph where each node is a sub-agent that loads exactly one skill, edges carry…

Run bounded, evidence-driven loops for repeated, resumable, delegated, or scheduled work.

Use for ingestion, ETL or ELT, data pipelines, mapping, cleansing, validation, reconciliation, lineage, warehousing, dimensional models, semantic layers, file…

Use when improving prompts for any LLM.

Production bundle combining baseline, issue, contract, deployment, data, configuration, observability, and security controls.

精通机器学习模型开发与部署的 AI 工程专家，擅长从数据处理到模型上线的全链路工程化，专注构建可靠、可扩展的 AI 系统。

Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features