Compression and iteration

Compression & Iteration Skill

Use this when

Use this skill when copy is over a character limit or feels too dense.

Common limits:

• LinkedIn headline
• LinkedIn About section
• LinkedIn Experience description
• GitHub bio
• resume one-page constraints
• social posts
• image prompt text limits

Core principle

Compression should not remove the reason to read.

Protect these elements:

1. hook
2. proof
3. unusual contrast
4. mechanism
5. reader benefit
6. CTA

Cut everything else first.

Step 1 — Count and classify

Identify:

• current character count
• target character count
• required reduction
• highest-signal sentences
• low-signal/redundant sections

Step 2 — Protect the spine

For a bio/profile, protect:

proof → unusual starting point → curiosity question → mechanism → reader benefit → CTA

For resume bullets, protect:

metric → outcome → mechanism → scope

For visual prompts, protect:

layout → emotional contrast → mechanism → aspect ratio → text constraints

Step 3 — Cut in the right order

Cut in this order:

1. filler phrases
2. repeated words
3. unnecessary adjectives
4. long setup explanations
5. redundant examples
6. overly detailed starting-point descriptions
7. secondary tools in long tech-stack lists
8. less measurable claims
9. optional CTA modifiers

Do not cut first:

• numbers
• timeframes
• before/after metrics
• strong mechanism phrases
• reader-benefit line
• proof of adoption

Common compression replacements

"in order to" → "to"
"the reason why" → "why"
"was able to" → "could" or direct verb
"helped to secure" → "helped secure"
"a very high amount of" → "high"
"the people closest to the work" → "operators/users"
"with the goal of" → "to"
"made it possible for" → "enabled"
"used operational data to see the bigger picture" → "used operational data"

Compressing the starting-point gap

Bad:

Before this role, most of my coding experience came from many different internships where I mainly wrote Jupyter notebooks and scripts, and I had little experience with Java, AWS, Databricks, Spark, CI/CD, production testing, and large legacy services.

Better:

Before this, my coding was mostly ML notebooks and scripts. I had to ramp up on production engineering, cloud, CI/CD, testing, and legacy services almost from scratch.

Compressing mechanisms

Verbose:

I use active listening, operational data, and direct manual exposure to uncover the real bottleneck, risk, deadline, or hidden constraint.

Shorter:

Use active listening, data, and hands-on exposure to uncover the real bottleneck, risk, or constraint.

Compression output format

Return:

## Current estimate
- Characters: [count]
- Limit: [limit]
- Need to cut: [number]

## Revised version
[text]

## What I cut
- [cut]
- [cut]

## What I protected
- [protected]
- [protected]

Iteration rules

When revising based on feedback:

• Preserve the user's underlying conceptual correction, not just the surface wording.
• If the user rejects a phrase, infer the principle behind the rejection.
• Do not reintroduce previously rejected claims.
• Distinguish between factual correction and style preference.
• Keep a compact list of protected phrases.

Quality bar

A compressed version is successful if:

• it fits the limit
• the hook still works
• proof remains specific
• the mechanism remains distinct
• the reader benefit remains clear
• no factual nuance was reversed