Total 52,836 skills, AI & Machine Learning has 8860 skills
Showing 12 of 8860 skills
Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.
Run /architect when choosing between approaches, designing a feature or page, picking a tech stack, or when /develop says a decision is owed, anytime a load bearing technical decision is unmade. Asks deep questions, recommends an answer, and writes a build spec to docs/specs/. Owns all spec files.
Use when asked to find skill opportunities in a codebase, audit a repo for automatable workflows, decide what skills to write, or mine git history and existing automation for recurring multi-step procedures worth turning into Claude Code skills.
Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition) using triplet / contrastive losses. Use when training, evaluating, exporting, or running inference for a TAO metric-learning recognition model. Trigger phrases include "train metric learning", "ml-recog", "retrieval embeddings", "triplet loss recognition", "fine-grained matching".
Runs the DEFT embed-then-mine workflow for VCN AOI iterations — embeds the gap-analysis target parquet, embeds a source pool, and mines nearest-neighbour source images for downstream augmentation. Use as the immediate next step after `tao-route-visual-changenet-samples` when expanding a real-image augmentation queue from the mining subset.
Generate a complete professional event plan from guest background material. Takes guest bio / CV / intro copy as input, runs a multi-turn clarification dialog (activity type, audience, duration, tone), and outputs a cohesive plan with title + promo copy, minute-level rundown, tiered host question set, and gift / takeaway suggestions. Trigger when user mentions "活动策划", "策划案", "嘉宾对谈", "沙龙策划", "主持人问题", "活动流程", "event planning", "host prep", "salon plan", or pastes a guest bio asking for an event plan.
Generate images using Gemini via ZenMux
Implement a task with automated LLM-as-Judge verification for critical steps
Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared state or dependencies - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates
Launch a sub-agent judge to evaluate results produced in the current conversation
Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.