Built this AI agent to find, organize, and manage leads through Gmail, helping automate lead handling and outreach.

n8n is absolute gold for orchestrating lead pipelines like this! Connecting Gmail parsing with LLM qualification saves teams dozens of hours weekly. As someone who builds custom agentic lead automation workflows, seeing clean visual architectures like this is super satisfying. Top-tier build, Talha!.

If you're paying for Claude, Cursor, or Codex and you're still pasting the same vague prompt into every new chat, the model isn't your bottleneck. Here's the tell: you type some version of "we don't do it that way" more than once a week. That correction is real engineering knowledge. Right now it lives in your head and dies when the conversation ends. Two things fix that. Knowledge is the context you make available; your architecture, your business rules, your API docs, your constraints, and the reasons behind the decisions you already argued about once. Skills are the recurring work written down once: the steps, not the explanation. Adding an API endpoint, concretely. Knowledge tells the agent how auth works, what the data model looks like, and why the team picked the conventions it did. The skill tells it what to do: read the two closest existing endpoints, follow the same validation pattern, update the API contract, run the tests that cover it. You still review the diff. You still own the decision. You've just stopped re-explaining your own codebase five times a week. Start with one task you repeat. Write the steps. Supply the context. Then check the thing that actually matters: did the rework go down, or does it only feel more organized? A subscription buys access. Getting useful work out of it is engineering.

The "we don't do it that way" test is a great way to put it. Every correction you repeat should end up in a rules file, not in the next chat. Making that a habit changed how I work with agents too.