logophile_skill

This skill tightens wording and compresses text while preserving meaning, boosting clarity and scan-ability across prompts, docs, and emails.
  • Python

42

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill tkersey/dotfiles --skill logophile

  • SKILL.md5.1 KB

Overview

This skill edits copy for clarity and semantic density, tightening wording while preserving intent and tone. It compresses verbose text, sharpens names and labels, and produces faster-to-scan output. Default mode returns the revised text only; optional modes add inline edits or reduction deltas.

How this skill works

On request, it distills the source into a one-sentence intent and preserves required tokens (numbers, code, quotes). It removes filler, verbifies nominalizations, and applies a precision ladder to replace vague words with exact actions or properties. The output is reshaped to lead with actions, keep sentences atomic, and parallelize lists; verification ensures obligations, risks, and facts remain unchanged.

When to use it

  • Tighten long emails, docs, prompts, or specs that are slow to scan
  • Compress repetitive or filler-heavy paragraphs without losing meaning
  • Refine names, titles, labels, or headings for clarity and impact
  • Sharpen prompts for LLMs to increase signal per token
  • Produce compact versions for UIs with space constraints

Best practices

  • Provide must_keep tokens (numbers, code, exact phrasing) when critical
  • Specify desired tone or length target if different from the source
  • Use the default fast mode for quick rewrites, annotated for review
  • Avoid asking for creative rewording that would change technical meaning
  • Request format constraints (bullets, table, single sentence) up front

Example use cases

  • Shrink a three-paragraph feature spec into a one-paragraph summary while keeping acceptance criteria
  • Rewrite onboarding UI labels and headings to fit mobile space without losing clarity
  • Condense a long support email into a concise response that preserves required steps and deadlines
  • Compress a verbose prompt into a token-efficient version for cost-sensitive model calls
  • Generate multiple short title options from a long article headline

FAQ

No. The process verifies that facts, obligations, risks, and required tokens remain intact; it only trims wording and reshapes structure.

How much reduction can I expect?

Typical reductions vary; the skill yields modest shrinkage for short copy and larger compression for filler-heavy text. Request a delta mode or a length_target for explicit thresholds.

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