ai-context-primer

# 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 Ask for these if not provided: - **The task** — what you want the AI to do - **The background it can't guess** — your situation, audience, goal, prior context - **What good looks like** — an example, a reference, or the standard you're holding it to - **Constraints** — must-haves, must-avoids, length, to...