Skill UI

Use the Recommendation Canvas to vet AI product concepts by mapping business and customer outcomes, hypotheses, positioning, risks, and success metrics so you can make defensible investment recommendations.

A comprehensive expert for deploying and scaling AI agents using AWS Bedrock AgentCore. This guide covers the full lifecycle of agent management, including service selection (Gateway, Runtime, Memory, Identity, etc.), deployment patterns, credential management, and advanced integration workflows required for enterprise-scale AI solutions.

Soft Predict Future is a structured foresight engine that triggers whenever the user asks about future outcomes or scenarios. It enforces a nine-step analytical pipeline (validation, signal collection, scoring, driver extraction, etc.), highlights required searches and inputs, and directs the user to web-enabled reasoning for predictive analysis.

This guide provides a comprehensive framework for debugging and analyzing AgentCore runtime sessions using CloudWatch Logs Insights. It details advanced techniques like OpenTelemetry (OTEL) span correlation, resolving session IDs to trace IDs, and structuring complex query queries. Use this when investigating agent execution flows, diagnosing tool call failures, analyzing latency, or building detailed behavioral timelines.

This skill transforms a course syllabus (PDF, DOCX, etc.) into a highly curated, supplementary academic reading list. It parses topics and learning outcomes, calibrates content difficulty based on the target audience (undergraduate, graduate, etc.), and searches academic databases (Consensus) for recent, peer-reviewed papers. The output is a professional .docx file containing summaries, links, and advanced discussion questions, making it invaluable for educators and students needing structured, up-to-date course resources.

This skill provides utilities to manage and drain the deliberation queue (brain_frames) of the ACC system. It allows users to list waiting frames, retrieve necessary context, perform deliberation on open tasks, and submit continuations using `acc_act(runtime="continue")`. It is essential for checkpointed work saved by headless runs, ensuring continuous task resolution and calibration against predicted outcomes in complex AI workflows.