Decide which skills to build and which to drop this year
A prioritized 12-month skills plan: what to master, what to learn before it loses value, and what to stop investing in.
The prompt
Help me decide where to put my learning time over the next 12 months as AI changes my field.
My role, level, and industry: [DETAILS]
Where I want to be in 2-3 years: [GOAL - e.g. "lead an analytics team", "stay hands-on but more senior"]
My current strengths: [SKILLS]
Skills I spend most of my time using today: [SKILLS]
Time I can realistically give to learning: [HOURS PER WEEK]
Sort my skills and candidate skills into four groups:
1. Double down - work I should become exceptionally good at because it compounds and is hard to replicate.
2. Learn now - skills that are valuable today but likely to lose value, where learning them soon still pays off (or the window is closing).
3. Build new - skills my role will increasingly need (e.g. directing AI tools, data judgment, specific domain depth).
4. Let go - skills becoming less valuable that I should stop polishing, with what replaces them.
For each skill give: why it belongs in that group, how confident you are (high / medium / low), and the signal that would change your mind.
Return:
1. The four groups as a table: Skill | Group | Reason | Confidence | Signal to watch.
2. My top 3 priorities for the year, sequenced by quarter, with one real-work project that would practice each.
3. How I will know each skill is good enough (an observable result, not hours studied).
Do not invent labor-market statistics. If you reference trends, describe them as general direction and tell me to check current sources for my field and location.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Career & Job Search prompts
What to fill in
- DETAILS
- SKILLS
- HOURS PER WEEK
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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