agentic▌

Iterative evaluation and refinement patterns for improving AI agent outputs through self-critique loops. \n \n Provides three core patterns: basic reflection (self-critique loops), evaluator-optimizer (separated generation and evaluation), and code-specific test-driven refinement \n Supports multiple evaluation strategies including outcome-based assessment, LLM-as-judge comparison, and rubric-based scoring with weighted dimensions \n Includes practical Python implementations with structured JSON

Core principles for effective AI agent collaboration: divide tasks, manage context, choose abstraction levels, automate patterns, and validate results. \n \n Decompose large tasks into small, explicit steps; AI performs 39% better on focused instructions than vague, complex requests \n Keep context fresh and single-purpose using separate conversations or HANDOFF.md documentation to avoid context drift and performance degradation \n Switch between high-level \"vibe coding\" for prototyping and de

AWS Bedrock AgentCore provides a complete platform for deploying and scaling AI agents with seven core services. This skill guides you through service selection, deployment patterns, and integration workflows using AWS CLI.

Transform a full-stack app into one where agents are primary actors — reasoning, coordinating, remembering, and communicating alongside humans.

Load with: base.md + llm-patterns.md + [language].md

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Standard multi-agent pipeline for implementation tasks.

Static security analysis for GitHub Actions workflows invoking AI coding agents. \n \n Detects nine attack vectors where attacker-controlled input reaches AI agents in CI/CD pipelines, including env var intermediaries, direct expression injection, CLI data fetches, dangerous sandbox configs, and wildcard user allowlists \n Scans .github/workflows/ for Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference steps; resolves one level of composite actions and reusable workflows \n Cap

AI-driven engineering workflows with eval-first execution, task decomposition, and cost-aware model routing. \n \n Defines an eval-first loop: establish baseline evals before implementation, then re-run post-execution to measure deltas and catch regressions \n Decomposes work into 15-minute units with single dominant risks, independent verifiability, and clear done conditions \n Routes tasks by complexity: Haiku for classification and boilerplate, Sonnet for implementation, Opus for architecture

Wallet operations: authentication, balance, token transfers, transaction history, and smart contract calls.