/prose:distill

Distill verbose text to its concentrated essence — the art of compression without loss.

When to Use This Skill

Use this skill when...	Use something else when...
Text is wordy and needs tightening	You need to change tone or register (use prose-tone)
Redundant phrases need removing	You need to restructure document flow (use prose-structure)
User says "condense", "tighten", "shorten"	You need to adapt for a specific audience (use prose-audience)
Preserving all meaning while reducing length	Summarizing (lossy) rather than distilling (lossless)

Core Principles

Distillation is lossless compression of natural language. Every sentence in the output must preserve the information content of the input. The goal is approaching the Shannon limit of the message — boiling off redundancy to leave concentrated meaning.

The Hierarchy of Cuts

Apply in this order. Each level removes less essential material:

Redundant phrases — saying the same thing twice in different words
Filler words — "actually", "basically", "essentially", "really", "very", "quite", "rather"
Hedge words — "somewhat", "arguably", "it could be said that", "in a sense"
Throat-clearing — opening phrases that delay the point ("It is worth noting that", "It should be mentioned that")
Nominalizations — noun forms where verbs are stronger ("make a decision" → "decide", "perform an analysis" → "analyze")
Passive constructions — where active is clearer and shorter
Prepositional chains — "the result of the analysis of the data" → "the data analysis result"
Weak verbs + adverbs — replace with a single precise verb ("moved quickly" → "darted")

What to Preserve

Technical precision and domain terminology
Necessary qualifications and nuance
Logical structure and argument flow
Voice and character (distill the style, don't flatten it)
Specific details, numbers, names, references

Parameters

Parse $ARGUMENTS:

If text is provided inline, distill it directly
If a file path is provided, read and distill the file contents
If no arguments, ask the user for text to distill

Execution

Execute this distillation workflow:

Step 1: Assess the input

Read the provided text. Identify:

Approximate word count
Density of redundancy (light, moderate, heavy)
Whether the text has a distinctive voice worth preserving

Step 2: Apply the hierarchy of cuts

Work through the text applying cuts in order from the hierarchy above. For each sentence:

Can two sentences merge into one without losing meaning?
Are there redundant phrases?
Can filler/hedge words be removed?
Can nominalizations become verbs?
Can passive become active without changing emphasis?
Can prepositional chains compress?

Step 3: Verify lossless compression

Compare the distilled version against the original. Confirm:

No information was lost
No meaning was altered
Qualifications and nuance survived
The logical flow is intact

Step 4: Present the result

Output the distilled text. Follow with a brief summary:

---
Original: ~N words
Distilled: ~N words
Reduction: ~N%

If any meaning was ambiguous and required interpretation, note it.

Examples

Filler and hedge removal

Before: "It is essentially worth noting that the system actually performs quite well in basically all of the scenarios that were tested."

After: "The system performs well in all tested scenarios."

Nominalization to verb

Before: "We performed an investigation into the cause of the failure and made a determination that the configuration was incorrect."

After: "We investigated the failure and determined the configuration was incorrect."

Redundancy elimination

Before: "The end result of this process is that each and every individual component is tested and verified to ensure and confirm that it meets the required specifications and standards."

After: "This process verifies each component meets the required specifications."

Preserving necessary nuance

Before: "While the approach generally works well in most common scenarios, there are some edge cases, particularly those involving concurrent access patterns, where the current implementation may exhibit degraded performance characteristics."

After: "The approach works well in common scenarios but may degrade under concurrent access patterns."

Note: "may" is preserved — it's a genuine qualification, not a hedge.

Agentic Optimizations

Context	Approach
Short text (< 100 words)	Distill inline, show before/after
Medium text (100-500 words)	Distill in sections, show word count reduction
Long text or file (> 500 words)	Read file, distill, write result, show stats
Preserving technical accuracy	Flag any cuts that might alter technical meaning

prose-distill