prompt-token-efficiency

A prompt-writing guide for making instructions to large language models shorter, clearer, and less ambiguous. Large language models are systems that generate text or code from written instructions.

In plain words
What is it for?
Use it to rewrite prompts, define explicit output formats, remove conflicting instructions, and set measurable success criteria.
Why use it?
Long or vague prompts can waste tokens and lead to inconsistent results. This guide helps retain the important requirements while removing repetition and unclear wording.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/github/gh-aw/prompt-token-efficiency
Any agent
npx skills add github/gh-aw --skill prompt-token-efficiency
Clone the repo
git clone --depth 1 https://github.com/github/gh-aw

Made for: Claude Code, Codex.

Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 398 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.00398
Opus 5 $0.00011 $0.00199
Sonnet 5 $0.00004 $0.00080
Haiku 4.5 $0.00002 $0.00040

Measured yesterday against content hash 8c8bf2a9fcc0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

prompt-token-efficiency scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.github/skills/prompt-token-efficiency/SKILL.md · 65 lines

What it actually says

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.
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 65 lines · 22 tokens per session scan A 8c8bf2a9fcc0

Subscribe to this mod's changes

prompt-token-efficiency is a skill published in the GitHub repository github/gh-aw (5,050 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 398 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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