Build the context an AI needs to do a task well — the background, constraints, examples, and format it can't guess — so you get a great result on the first try instead of a generic one you have to keep correcting.
SkillScore39Maintained
Automatic
◆AI Engineer pack
Last verified 8/31/2026
What it changes
Passed 9 of 23 test cases with the skill, and 3 of 23 without it.
Without skill13%
With skill39%
Checks it passed that the model alone missed
Contains the field **Example of good:**
Includes the core field **Example of good:**
Includes the structured field **Constraints:**
and 20 more in the full report
9 fixed0 passed either way11 still failing3 broke
Pass-rate lift
+26 pp
Tokens
-3%
Agent turns
0%
Response time
-26%
Model gemini-3.6-flashMeasured 8/31/2026v19 of 23 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 9
Prompt
I keep getting super vague responses when asking AI tools to write our company's internal API migration docs. I want you to construct a comprehensive context briefing structure for this task. Please breakdown the audience, goal, constraints, ideal output sample, and format required. Also include a reusable copy-paste primer block I can attach before my future prompts, identify what missing context caused the previous generic outputs, and highlight any unnecessary noise I should leave out.
What was checked, without and with the skill
Checked
Without skill
With skill
Formats output title using markdown header ### Context primer:
Missed
Passed
Includes the section Paste-ahead primer formatted inside blockquote syntax >
Missed
Passed
Includes the structured context field **Constraints:**
Missed
Passed
case-01 · gemini-3.6-flash · 8/31/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md66 linessingle file
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, tone, format
What went generic before — if you've tried, what was off (points at the missing context)
Framework: Brief It Like It Knows Nothing About You
Name what the AI can't know. It has no access to your situation, audience, standards, or prior work — list what it'd need to tailor the answer, because that's exactly what's missing.
Assemble the five pieces. Who (audience + you), what (goal + background), constraints (must/must-not), examples (what good looks like), format (structure/length/tone) — the reliable spine of good context.
Show, don't just tell. An example of the output you want, or a reference you like, teaches the AI more than a paragraph of description — include one where the task is fuzzy.
Cut the noise. More context isn't better — irrelevant detail dilutes the signal. Keep what changes the output, drop what doesn't.
Make it reusable. Package it as a primer block you can paste ahead of similar requests, not something you rebuild each time.
Output Format
Context primer: [the task]
Who: [audience + relevant about you].
What: [goal + the background it can't guess].
Constraints: [must-haves · must-avoids · length/tone].
Example of good: [a sample or reference — where the task is fuzzy].
Format: [structure / length / tone you want].