ArthurZakirov Curiosity Bio & Story Skill
Use this when
Use this skill to write or revise:
- LinkedIn About sections
- personal bios
- founder bios
- profile summaries
- article introductions
- story-driven posts
- portfolio homepage copy
- creator profile copy
Core principle
A good bio is not a chronological autobiography.
A good bio creates a curiosity gap:
How did this person achieve [specific result] despite [unlikely starting point or constraint]?
Then it answers that gap with a transferable mechanism and shows why the reader should care.
Recommended structure
Use this order:
- Numeric curiosity hook
- Unusual starting-point gap
- Question that creates the knowledge gap
- Reader benefit
- Operating model / mechanism
- Proof examples
- CTA or positioning close
1. Numeric curiosity hook
Open with numbers and contrast.
Template:
In [time period], I helped/contributed to [measurable outcomes] despite [constraint].
Examples:
In my first [time period] as [role], I contributed to [outcome 1], [outcome 2], and [outcome 3].
I helped move [metric] from [before] to [after] in [timeframe] by [mechanism].
Avoid weak openers:
I am passionate about...
I have always loved...
I am an innovative...
2. Unusual starting-point gap
Make the gap concise and dramatic.
Template:
The unusual part: I did this without [expected credential/experience/resource].
Keep it short. Do not over-describe everything the person lacked.
Good:
I did this without a formal background in the exact stack, prior full-time experience, or previous roles in the same domain.
Avoid:
I had no experience with tool A, tool B, tool C, tool D, framework E, framework F, process G, system H...
Use 1 sentence for the gap, 1 sentence for context.
3. Knowledge-gap question
Ask the question the reader now wants answered.
Template:
So what made the ramp-up possible?
Stronger template:
If this worked in an unfamiliar stack/domain, what made it transferable — and how could the same mechanism help your team?
This must do two jobs:
- explain the surprising result
- make the reader see a benefit for themselves
4. Operating model / mechanism
Give the method in named steps.
Keep steps short. Prefer 3–6 steps.
Good mechanism names:
- Decode the request behind the request
- Context-engineer the problem
- Find what already works
- Compose the solution
- Harden the learning
- Make the solution land
Avoid generic mechanism names:
- Work hard
- Communicate well
- Use AI
- Be proactive
5. Proof examples
Proof should be measurable.
Strong proof contains:
action + metric + timeframe + mechanism + why it mattered
Examples:
• Shifted a roadmap from 41 to 770 hours of saveable work — ~19× more leverage.
• Ran 4 workshops for ~20 engineers and supported targeted 1:1 onboarding for senior stakeholders.
6. CTA / close
A bio can ask for action. The CTA should connect to the reader's pain.
Templates:
Follow me for practical posts on [topic], [topic], and [topic] — or message me if you want to discuss how this applies to your situation.
If your team is dealing with [pain], I write about how to turn it into [outcome].
Word choice rules
Prefer:
- adopted systems
- measurable outcome
- real bottleneck
- manual work
- shipped capability
- reusable capability
- data-backed priority
- belief-creating demo
- make the solution land
Avoid:
- passionate about AI
- cutting-edge
- scale impact
- leverage synergies
- innovative solutions
- thought leader
- AI enthusiast
Output templates
About-section skeleton
In [time period], I contributed to [measurable outcomes].
The unusual part:
I did this without [expected credential/experience].
So the real question is:
If [surprising result] was possible in [unfamiliar context], what made it transferable — and how could the same operating system help [reader/audience]?
My loop:
1. [Step]
[Short explanation]
2. [Step]
[Short explanation]
...
[CTA]
Article/post intro skeleton
Most people approach [problem] by [common method].
That works until [failure mode].
The better question is not [surface question].
It is [deeper question].
Here is the framework I use:
Quality bar
The reader should know:
- what was achieved
- why it is surprising
- what mechanism explains it
- why it could apply beyond the original case
- what they should do next