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Found 382 Skills
Add persistent, structured long-term memory to AI agents using Maximem Synap. Use this skill whenever the user is building, debugging, or evaluating an AI agent and mentions any of: "memory", "long-term memory", "persistent memory", "agent memory", "remember across sessions", "context window", "agent forgets", "user preferences", "personalization", "RAG over conversations", "multi-tenant memory", "memory layer", "Mem0", "Zep", "Letta", "SuperMemory", "Cognee", or asks how to integrate memory into LangChain, LangGraph, LlamaIndex, OpenAI Agents SDK, Pydantic AI, CrewAI, AutoGen, Google ADK, Haystack, Agno, Semantic Kernel, Microsoft Agent Framework, NVIDIA NeMo, LiveKit, Pipecat, Claude Agent SDK, Mastra, Vercel AI SDK, or MCP (no-code). Also trigger on direct mentions of "Synap", "Maximem", "maximem-synap", or `synap-*` package names. Covers SDK setup, scoping (User/Customer/Client), ingestion, retrieval, and one drop-in package per framework.
End-to-end work with UiPath Agents of all types: build, integrate with UiPath Products (e.g., Orchestrator, Flow, Maestro), design with UiPath Tools (e.g., Agent Builder/Studio Web), deploy, and configure/validate. Covers Coded Agents (e.g., LangGraph, LlamaIndex, OpenAI Agents) and Low-Code Agents (`agent.json` / Agent Builder). For deterministic Python Coded Functions (`uip function`, `uipath.json` functions map, no agent runtime/LLM)→uipath-functions.
Generate or edit images using OpenAI GPT Image API (gpt-image-2, gpt-image-1, etc). Use ONLY when the user explicitly names OpenAI or GPT as the provider: "gpt image", "openai image", "generate image with openai", "用 openai 画图", "用 GPT 生成图片". For generic image requests without a provider, use nanobanana-skill instead. Do NOT use for diagrams (架构图/流程图) — draw those with Mermaid or code.
Instrument an application with Sentry — detect the platform, install and initialize the SDK if needed, and wire up any signal — error monitoring, tracing/performance, logging, metrics, profiling, session replay, user feedback, cron check-ins, and AI/LLM monitoring (agent runs, token cost, and conversations for OpenAI, Anthropic, Vercel AI, LangChain, Google GenAI, Pydantic AI, and Laravel AI). Use to add Sentry to a project or to capture more than errors.
UiPath Coded Functions — deterministic Python units built via `uip function` (new/init/run/pack/publish); the `functions` map in `uipath.json`, `entry-points.json`, Pydantic Input/Output. Rule-based logic, data transforms, ERP/Integration Service connector calls — no LLM reasoning or agent loop. For LLM/agentic projects (LangGraph, LlamaIndex, OpenAI Agents, `agent.json`)→uipath-agents.
Integrates the SAP Cloud SDK for AI for Python (sap-ai-sdk-gen, formerly generative-ai-hub-sdk) into Python applications. Use when building Python apps with SAP AI Core, Generative AI Hub, or the Orchestration Service: chat completion, embeddings, streaming, LangChain integration, templating, content filtering, data masking, and document grounding. Supports OpenAI GPT models, Llama, Gemini, Amazon Nova, and other foundation models via SAP BTP.
Remove multi-vendor AI provenance marks: invisible Unicode (Layer A), statistical text watermarks via rewrite (Layer B, always offer), and C2PA/EXIF/XMP/container metadata on PNG/JPEG/WebP/SVG/PDF/DOCX/ODT/HTML/MD. Covers Claude, Gemini/SynthID-class, OpenAI provenance, and open-LLM sampling marks. Use when the user asks to strip watermarks, remove C2PA/Content Credentials, clean AI metadata, remove invisible Unicode, anti-detect clean AI output, or runs /remove-ai-marks (aliases: /remove-claude-marks).
Test/evaluate any LLM behind an OpenAI- or Anthropic-compatible endpoint: availability (max_tokens-aware), request fidelity (does system prompt/tools/history REACH the model, or does the gateway silently drop it), speed (TTFT+tok/s), concurrency (before a workshop), Anthropic protocol compliance, quality regression, vendor bug reports, deployment gates, resident canaries. Reach for this BEFORE hand-rolling a curl loop against any LLM endpoint — that instinct is the trap: it skips the N=10 sampling, Connection:close, and env-var key handling this bakes in. Use when someone tests/benchmarks/测评/压测 a model/endpoint, onboards a provider, decides whether to switch or temporarily fail over to an alternate channel (e.g. outage or quota exhaustion), writes a supported-models list, debugs "model ignores system prompt", or verifies a tok/s claim. Triggers on "benchmark this model", "测一下这个模型/渠道/API", "接入新模型先测一下", "system prompt 不生效", "这个渠道能不能用/稳不稳", "临时切换过去顶一阵子" — even without "eval", even wrapped in business narrative.
Configures TrueFoundry AI Gateway end-to-end. Covers unified OpenAI-compatible LLM access, provider account integrations, content safety guardrails, and observability (traces, costs, errors) via the spans query API.
Use when the user wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite. End-to-end: assesses the codebase, then rewrites SDK calls, evaluates output quality against Bedrock, and delivers a ready-to-merge git branch. Not for: agent runtime selection, agentic architecture decisions, or agent migration planning — use agent-advisor for those. Not for standalone Bedrock cost estimates or infrastructure-only migration. The Assess phase is handled by this plugin's own gcp-to-aws skill.
Analyze images using GPT-4 Vision for detailed description, OCR text extraction, object recognition, and visual Q&A. Use when the user needs to understand image content, extract text from screenshots, identify objects in photos, or ask questions about images via OpenAI GPT-4 Vision API.
Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, migrate App Engine to Elastic Beanstalk, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.