AI-Context Primer

Generic AI answers are almost always a context problem, not a model problem — you asked for something the AI had no way to tailor, so it gave you the average of everything. The fix is priming: giving it the background, constraints, examples, and format it can't guess before you make the request. This builds that primer for your task, so the first result is close, not a starting point you spend five rounds correcting.

What This Skill Produces

• The context this task actually needs — the who (audience, you), the what (goal, background), the constraints (must/must-not), the examples (what good looks like), and the format (structure, length, tone)
• A reusable primer block — a clean paste-ahead of your request that briefs the AI properly, not a one-off
• The gap it fills — what the AI was missing that made earlier answers generic, made explicit
• What to leave out — the noise that dilutes rather than helps, so the primer stays sharp
• Starved vs briefed, shown — a quick before/after so you feel the difference context makes
• A primer habit — how to make briefing-before-asking your default for tasks that matter

Required Inputs