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prompt-token-efficiency

Rewrite long prompts into shorter, clearer instructions for language models. Preserve essential requirements while removing repetition, defining scope, and making output formats and success criteria explicit to reduce ambiguity and unnecessary tokens.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Rewrite prompts for minimal tokens, maximal clarity, and low ambiguity for LLM consumption.

github/gh-aw
설치 방법 보기

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  • Shortening a long prompt while preserving requirements
  • Clarifying vague instructions and success criteria
  • Making output formats and length limits explicit
  • Removing repeated or conflicting instructions

포함된 파일(1)

  • SKILL.md1.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Prompt Token Efficiency

Use this skill to compress prompts while preserving intent and output quality.

Goals

  1. Minimize token count
  2. Maximize clarity
  3. Minimize ambiguity
  4. Optimize for LLM execution, not human prose style

Core Rules

  • Keep only task-critical information.
  • Remove pleasantries, repetition, and narrative framing.
  • Prefer short, concrete instructions over descriptive paragraphs.
  • Use explicit constraints and output format requirements.
  • Use stable terminology (one term per concept).
  • Replace vague words (appropriate, some, better) with measurable criteria.
  • Put required context before optional context.
  • Avoid conflicting instructions.

Prose Compression Pattern

Rewrite prose to be direct and compact:

  1. Start with the objective in one short sentence.
  2. Keep only facts needed to complete the task.
  3. Replace long qualifiers with concrete limits.
  4. Remove filler words that do not change behavior.
  5. End with explicit success criteria.

LLM-Optimized Writing Style

  • Use imperative statements.
  • Prefer bullets over long prose.
  • Keep each instruction atomic.
  • Avoid examples unless needed to prevent failure.
  • If examples are required, include one minimal example.

Ambiguity Checks

Before finalizing a prompt, verify:

  • Any undefined noun is resolved.
  • Any pronoun has a clear antecedent.
  • Scope limits are explicit (time range, file range, quantity limits).
  • Success criteria are testable.
  • Output format is unambiguous.

Prose Rewrite Checks

When rewriting, ensure the final prompt:

  • Uses fewer words than the original.
  • Preserves all required constraints.
  • Uses concrete nouns instead of pronouns where possible.
  • Avoids optional wording unless options are actually allowed.
  • States required output and any length bound in plain language.

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이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

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한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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Review git and gh operations in gh-aw workflows to check credential availability for each repository. Identify missing token handoffs, risky expansions of limited checkouts, and error handling needed where credentials are intentionally unavailable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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github/gh-aw5.4천2026년 10월 19일 업데이트

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