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Cuts articles or passages to a target word count by removing redundancies and tightening prose while preserving key information, structure, and voice.
npx claudepluginhub ur-grue/autopunk-media-skills --plugin autopunk-media-skillsHow this skill is triggered — by the user, by Claude, or both
Slash command
/autopunk-media-skills:text-condenserThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Cuts an article or passage to a target word count by removing redundancies, tightening prose, and eliminating low-value sentences — while preserving the piece's key information, structure, and voice.
Reduce text to a specified word count or percentage while preserving essential meaning, key facts, and author's voice. Useful for adapting long-form content to shorter formats.
Distills verbose text to its concentrated essence without losing meaning or nuance. Triggers on 'distill', 'condense', 'tighten', 'shorten' requests for text or files.
Cuts prose to its bones by flagging adjectives, adverbs, qualifiers, redundancies, passive voice, and dead metaphors. Useful when a draft feels bloated or overwritten.
Share bugs, ideas, or general feedback.
Cuts an article or passage to a target word count by removing redundancies, tightening prose, and eliminating low-value sentences — while preserving the piece's key information, structure, and voice.
Required: The full text to be condensed; the target word count or the number of words to cut (e.g., "cut to 1,200 words" or "remove 400 words").
Optional: Passages or information that must be preserved (e.g., "keep the opening anecdote intact," "the statistics in paragraph 5 are essential"); the reason for the cut (layout space, pacing, editorial tightness) — this affects which strategy the assistant prioritises; whether you want a clean condensed version, a tracked-changes version showing what was cut, or both.
Measures the gap. Calculates the current word count and the number of words that need to be removed. This determines the strategy: a 10% cut calls for line-level tightening; a 30%+ cut requires removing entire passages or restructuring.
Identifies expendable material first. Before touching any prose, flags the lowest-value content: redundant restatements, throat-clearing openings, examples that duplicate a point already made, attribution padding ("he went on to say that"), and transitional filler. These are cut first because they cost nothing editorially.
Tightens at the sentence level. After removing expendable material, works through remaining prose to compress: replaces wordy constructions with tighter alternatives, converts passive to active where it saves words, eliminates adverbs and adjectives that add emphasis but not information. Preserves the writer's voice — tightens their style rather than replacing it.
Protects the architecture. Ensures the condensed version retains the article's structural logic: the lede still sets up the piece, evidence still follows claims, the ending still resolves or provokes. Does not cut a paragraph that serves as a structural hinge even if it is not the most interesting content. Preserves any passages the user flagged as essential.
Reports what was done. After delivering the condensed text, provides a brief cut log: how many words were removed, which passages were cut or significantly shortened, and any editorial trade-offs the writer should be aware of (e.g., "Removed the third expert quote — the piece now has two sources instead of three").
The condensed article text, followed by a cut summary. The condensed text is delivered clean — ready to use as-is. The cut summary is a short bulleted list (3-6 items) noting the major changes. If the user requested tracked changes, the output includes a version with removed text marked in strikethrough before the clean version. Word counts (original and final) are stated at the top.
**Original:** [X] words → **Condensed:** [Y] words ([Z] words cut)
[Clean condensed text]
---
**Cut Summary**
- [What was removed or shortened, and why]
- [What was removed or shortened, and why]
- [Any editorial trade-off to be aware of]
Target: Cut to 200 words Preserve: The opening sentence and the statistic about teacher turnover Text (310 words):
The school district has spent three years and $4.2 million trying to solve a problem that keeps getting worse. Teacher turnover in the district's lowest-income schools now exceeds 35 percent annually — more than double the state average and triple the rate at the district's wealthiest campuses.
The initiatives have been varied and, by most accounts, well-intentioned. A mentorship programme launched in 2023 paired new teachers with experienced colleagues. A retention bonus of $3,000 per year was introduced for staff at high-turnover schools. Professional development days were restructured to give teachers more autonomy over their training. An exit interview process was formalised to capture why people leave.
Despite all of this, the turnover rate has not improved. In fact, it has increased by two percentage points since the initiatives began. Superintendent Diana Levin acknowledged the results were disappointing but said the district was committed to continuing the programmes. "We believe the foundation we've built will show results over time," she said.
Teachers who have left tell a different story. In interviews conducted for this report, former staff consistently cited the same three factors: unmanageable class sizes, insufficient support for students with behavioural needs, and a pervasive sense that administrative decisions are made without consulting the people in classrooms. The mentorship programme and the bonus, several said, were appreciated but irrelevant to the core reasons they resigned.
"It's not about money or mentors," said one former teacher who left after six years. "It's about being asked to do an impossible job and being told you're not trying hard enough when you fail."
Original: 310 words → Condensed: 198 words (112 words cut)
The school district has spent three years and $4.2 million trying to solve a problem that keeps getting worse. Teacher turnover in the district's lowest-income schools now exceeds 35 percent annually — more than double the state average and triple the rate at the district's wealthiest campuses.
The initiatives have included a mentorship programme, a $3,000 annual retention bonus, restructured professional development, and formalised exit interviews. Despite this, turnover has increased by two percentage points since the programmes began. Superintendent Diana Levin said the district was committed to continuing. "We believe the foundation we've built will show results over time," she said.
Teachers who have left cite different reasons. In interviews conducted for this report, former staff consistently named unmanageable class sizes, insufficient support for students with behavioural needs, and a sense that decisions are made without consulting classroom teachers. The bonus and mentorship, several said, were appreciated but irrelevant to why they resigned.
"It's not about money or mentors," said one former teacher. "It's about being asked to do an impossible job and being told you're not trying hard enough when you fail."
Cut Summary