Teresa Torres defined a `/today` slash command in Claude Code. This shortcut triggers a detailed, pre-written prompt that scans her task files, checks for team updates, and generates a prioritized daily to-do list in Obsidian, automating a repetitive and complex morning routine.

Related Insights

The paradigm is shifting from using AI as a general chatbot to building a team of 'digital employees.' Claude Skills allow users to encapsulate a specific, repeatable workflow—like drafting a newsletter from tweets—into a tool that can be executed on demand, creating a specialized agent for that job.

Beyond using pre-made skills, users can simply prompt Claude to create a new skill for itself. The AI understands the required format and can generate the instructional text for a new capability, such as crafting marketing hooks that create FOMO. This democratizes the process of AI customization.

Create a reusable prompt (a "slash command") that explicitly asks your AI coding assistant to explain complex technical concepts. Frame the prompt with your current knowledge level (e.g., "explain this to a technical PM in the making using the 80/20 rule"). This transforms every coding session into a valuable learning opportunity.

Instead of managing prompts in a separate library, save them as custom commands directly within your Claude Code project folder. This lets you trigger complex, multi-file prompts with a simple command (e.g., `/meeting_notes`), embedding powerful, recurring workflows directly into your development environment.

The process of building AI tools is becoming automated. Claude features a 'Skill Creator,' a skill that builds other skills from natural language prompts. This meta-capability allows users to generate custom AI workflows without writing code, essentially asking the AI to build the exact tool they need for a task.

The term "agent" is overloaded. Claude Code agents excel at complex, immediate, human-supervised tasks (e.g., researching and writing a one-off PRD). In contrast, platforms like N8N or Lindy are better suited for building automated, recurring workflows that run on a schedule (e.g., daily competitor monitoring).

Teresa Torres created a system using Python scripts and Claude to automate her research workflow. The script searches preprint servers like arXiv for keywords daily, and Claude then generates detailed summaries of saved papers, delivering a "research digest" directly to her to-do list each morning.

Create a single command that triggers scripts for your AI to consolidate tasks from various sources (like Trello), generate a daily to-do list in a notes app, and pull in new research. This streamlines your morning routine and provides immediate focus for the day.

Codex lacks formal custom commands. You can achieve the same result by storing detailed prompts and templates in local files (e.g., meeting summaries, PRD structures). Reference these files with the '@' symbol in your prompts to apply consistent instructions and formatting to your tasks.

To gain data ownership and enable AI automation, Teresa Torres built a personalized task manager using Claude Code and local Markdown files. This allows her to prompt the AI to directly see and execute items from her to-do list, a capability not possible with third-party tools like Trello.