Define memory and settings at the project level to enable tailored configurations and persistent context for AI agents.

Structure your project folders and prompts strategically within coding environments to optimize efficiency and clarity in AI development.

Design recursive or layered AI systems similar to inception to enable multi-stage reasoning and orchestration among sub-agents.

Use VS Code to manage prompts and project folders, streamlining AI development workflows by centralizing prompt handling and file organization.

Design AI systems where sub-agents can instantiate their own sub-agents for hierarchical task execution.

Organize prompts in project folders to enable quick reuse and testing of additional agents in the workflow.

Hide or manage AI reasoning traces to improve debugging clarity and reduce cognitive overload.

Press Control+R in the Claude Code CLI to inspect current process details or data during agent execution.

Use explicit language in prompts to reference exact documentation sections or instructions for agent tasks.

Clarify and configure agent search strategies to differentiate between full database searches and context-based retrieval.

Create and initiate a custom team of agents to manage and execute multi-step AI tasks and evaluate their performance.

Define specialist sub-agents for handling academic papers, code implementation, trend analysis, and technical integration to streamline AI workflows.

Use Claude Code sub-agents to decompose complex tasks like academic trend analysis by assigning each sub-agent a specialized role.

Use higher-level meta prompts to steer AI behavior and break down tasks systematically into sub-prompts.

Explicitly designing and mapping out each task’s inputs and outputs brings structure and clarity to complex AI workflows.

Leverage embeddings and semantic similarity search to determine when to reuse existing sub-agents or generate new ones based on task decomposition log...Show more

Use an SQL database to store metadata and definitions for sub-agents, enabling efficient organization and retrieval of agent files.

Keeping the number of sub-agents manageable is crucial for reducing coordination overhead and maintaining performance.

Developed a quick hack to inject custom features at the application layer, emphasizing rapid prototyping and on-the-fly customization of AI agents.

Implemented a system that scans a machine for sub-agent markdown files, inventories them in SQLite, and uses embeddings for semantic search to retriev...Show more