AI & LLM Integration for Claude Code

Search 1,300+ curated AI image prompts for inspiration

Visual research agent that searches the MeiGen gallery of 1,300+ curated AI-generated images. Finds references, extracts reusable prompts, and helps discover creative directions.

Create a multi-agent group chat with AG2 using configurable speaker selection patterns

Researches a chosen AI framework's official docs to produce implementation-ready guidance — best practices, syntax, core patterns, and pitfalls distilled for the specific use case. Writes the Framework Quick Reference and Implementation Guidance sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase orchestrator.

Researches the business domain and real-world application context of the AI system being built. Surfaces domain expert evaluation criteria, industry-specific failure modes, regulatory context, and what "good" looks like for practitioners in this field — before the eval-planner turns it into measurable rubrics. Spawned by /gsd:ai-integration-phase orchestrator.

Retroactive audit of an implemented AI phase's evaluation coverage. Checks implementation against the AI-SPEC.md evaluation plan. Scores each eval dimension as COVERED/PARTIAL/MISSING. Produces a scored EVAL-REVIEW.md with findings, gaps, and remediation guidance. Spawned by /gsd:eval-review orchestrator.

Designs a structured evaluation strategy for an AI phase. Identifies critical failure modes, selects eval dimensions with rubrics, recommends tooling, and specifies the reference dataset. Writes the Evaluation Strategy, Guardrails, and Production Monitoring sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase orchestrator.

Presents an interactive decision matrix to surface the right AI/LLM framework for the user's specific use case. Produces a scored recommendation with rationale. Spawned by /gsd:ai-integration-phase and /gsd-select-framework orchestrators.

Execute image generation via the MeiGen MCP server. Delegates all generate_image calls to keep the main conversation context clean. Use for single images, batch parallel generation, or serial workflows.

Visual and design specialist for UI layouts, color systems, design briefs, sprite specs, animation choreography, and AI image-gen prompts. Use PROACTIVELY for any task where the output is a visual specification or design brief. Part of the operator-kit five-agent starter kit.

Background agent that extracts user profiles, contact cards, and behavioral patterns from chat history. Runs as a daemon service every 30 min.

Scaffold an A2A-compliant AG2 agent with server wiring, card settings, and skill definitions

Scaffold a new AG2 ConversableAgent with tool functions, system prompt, and LLM config

Create a tool function for an AG2 agent with type annotations, docstrings, and JSON return contracts

Prompt optimizer — rewrite this request into an optimized prompt (ultrathink), then carry it out

Set up the Qwen Video Bridge (API key + dependencies)

Continue a completed/failed/cancelled Codex agent with a new prompt

Create a sequential multi-agent pipeline where each agent processes and passes results to the next

Launch a Codex agent in the background (implementer/reviewer/planner/generic)

Show status of Codex agents — one agent by id, or all

Design a complete multi-agent workflow from a natural language description, selecting the right orchestration pattern

AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。

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Predict protein structures using modern ML models including AlphaFold3, ESMFold, Chai-1, and Boltz-1. Use when predicting structures for novel proteins, protein complexes, or when comparing predictions across multiple methods.

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> Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.

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Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols. Implements suicide ideation detection, automated escalation, and crisis resource integration. Use for mental health apps, recovery platforms, support communities. Activate on "crisis detection", "suicide prevention", "mental health NLP", "intervention protocol". NOT for general sentiment analysis, medical diagnosis, or replacing professional help.

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Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

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