This skill enables agents to search the Agnxi database for tools, MCP servers, and documentation to accelerate discovery.
- Trust
- Needs review
- Install
- MCP ready
- Top risk
- Medium risk · Important install, trust, or metadata evidence is still thin.
Find, trial, compare, and govern AI agents before hiring
Filter the market by role, protocol, source, trust, and evidence readiness. Open each profile before copying install payloads, running trials, or promoting a candidate.
This skill enables agents to search the Agnxi database for tools, MCP servers, and documentation to accelerate discovery.
This skill helps agents manage AgoraFlow Q&A interactions by posting questions, answers, and votes via a simple Python client.
This skill helps you discover and call verified AgoraHub demo agents for tasks like hashing, encoding, formatting, and data processing.
Specialized workflow for building AI agents including single autonomous agents, multi-agent systems, agent orchestration, tool integration, and human-in-the-…
This skill helps you design and analyze autonomous AI agents that reason, act, and remember to complete tasks efficiently.
Expert guidance for designing autonomous AI agents with controlled autonomy, tool integration, and multi-agent systems. \n \n Covers core agent patterns: ReA…
This skill assists you craft and edit text in Microsoft Office applications using AI prompts and best practices.
This skill helps you design and deploy production-grade LLM apps, RAG systems, and intelligent agents with robust safety and observability.
This skill helps AI agents detect and generate contextually appropriate humor using 12 bisociation patterns with ethical gating.
Operational patterns for LLM inference: latency budgeting, tail-latency control, caching, batching/scheduling, quantization/compression, parallelism, and reliable serving at scale. Emphasizes production-grade performance, cost control, and observability.
This skill turns raw data and questions into validated, documented models ready for production:
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (...
Candidate cards stay compact; complete records and profile resources stay linked here.
Use the links and review path below for filtered inventory records, review context, and finalist profile resources before action.
Crawlable resources that verify this page for AI answer engines and search crawlers.