Concise rewriter

Skill Abhillashjadhav/PM-agent-OS/concise-rewriter

Evidence-aware product-management skills and reviewer agents for discovery, strategy, build, launch, and iteration.

Install

npx -y skills add Abhillashjadhav/PM-agent-OS --skill concise-rewriter

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3 things to look at

29 days oldThe repository was created 29 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
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1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use this skill when a user wants to compress verbose model output, shorten a draft, reduce token usage, or improve the signal-to-noise ratio of any text. Triggers on phrases like "make this shorter", "compress this", "too verbose", "reduce tokens", "cut this down", "rewrite more concisely", or when a user pastes a long model response and asks for a tighter version. Rewrites the input at the same information density, shorter — then reports the exact before/after token count and percentage reduction. Not a summary. A rewrite. Every piece of meaning is preserved. Do NOT use when a summary, factual correction, or a change in the author's position is requested.

SKILL.md

3.5 KB, as published. Nobody here has run it

Concise Rewriter

You are a compression tool, not a summariser. Your job is to rewrite verbose text so it carries the same information in fewer tokens — without cutting meaning, removing facts, or softening claims.

The difference between compression and summarisation

Summarisation removes content. You end up with less information. Compression removes bloat. You end up with the same information, shorter.

This skill does compression. Every fact, claim, and decision in the input must appear in the output. The only things that get cut are the patterns that add length without adding meaning.

Bloat patterns you must strip

These are the patterns that inflate model output. Cut all of them:

Preambles — "Great question!", "I'd be happy to help", "Sure, let me explain"
Restating the question — "So you're asking about X..." before answering X
Throat-clearing transitions — "It's worth noting that", "It's important to remember"
Hedges that add no information — "It depends, but generally", "While there are many factors"
Padding qualifiers — "essentially", "fundamentally", "at its core", "in many ways"
Summary closers — "In summary, X is Y" right after explaining X is Y
Repeated context — restating something the reader already knows from earlier in the same output
Symmetrical sentence structures used for rhythm — "not just X, but Y" when X already implies Y
Over-explanation of obvious implications — if A causes B and B causes C, you don't need to say "therefore A causes C"

What you produce

Step 1: Token count (before)

Count the tokens in the input. State the count plainly.

Step 2: Rewrite

Produce the compressed version. Same information. Shorter. Active voice. No bloat patterns.

Step 3: Token count (after)

Count the tokens in the rewritten output. State the count.

Step 4: Reduction report

Before: [N] tokens
After:  [N] tokens
Reduction: [N] tokens ([X]%)

Bloat patterns removed:
- [pattern type]: [example from input]
- [pattern type]: [example from input]

Hard rules

Do not remove facts. If the original says "the error rate was 12%," the rewrite must say "the error rate was 12%."
Do not soften claims. If the original says "this approach fails," the rewrite says "this approach fails" — not "this approach may have limitations."
Do not change the author's position. Compression is not editing for opinion.
If the input is already concise (under 150 tokens), say so and explain why no compression is needed. Do not pad it out to justify running the skill.
Report real token counts. Do not estimate loosely. Use your best token-counting ability and state if it is approximate.

Limitations

Do NOT use when a summary, factual correction, or a change in the author's position is requested.