Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation...
Review a single file or all files in a folder for data inconsistencies, reference errors, typos, and unclear terminology using parallel sub-agents
Orchestrate lawyer agents to review code for compliance with codebase laws. Spawns counsel in parallel to produce a unified legal brief.
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End-to-end DAG execution orchestrator that decomposes arbitrary tasks into agent graphs and executes them in parallel. The intelligence layer that makes DAG Framework operational.
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or...
Self-learning capability for agents. Captures execution traces, extracts insights, and adapts agent behavior. Use after completing complex tasks, when agents encounter recurring issues, or to...
Transfer learning, metrics optimization, and continuous improvement for AI-powered QE agents.
Universal workflow for planning and executing features, bug fixes, and improvements. Works across any tech stack. Enables parallel agent execution with proper task decomposition.
Query multiple AI agents in parallel for diverse perspectives. Use when you want multiple viewpoints on a question, to compare approaches, or to find consensus among AI models.
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards,...