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.
npx agentmods add skills/github/gh-aw/prompt-token-efficiencynpx skills add github/gh-aw --skill prompt-token-efficiencygit clone --depth 1 https://github.com/github/gh-awWhat 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.
| Model | Per session | Once 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 |
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.
What it actually says
Prompt Token Efficiency
Use this skill to compress prompts while preserving intent and output quality.
Goals
- Minimize token count
- Maximize clarity
- Minimize ambiguity
- 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:
- Start with the objective in one short sentence.
- Keep only facts needed to complete the task.
- Replace long qualifiers with concrete limits.
- Remove filler words that do not change behavior.
- 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.
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.
- yesterday First seen · 65 lines · 22 tokens per session scan A 8c8bf2a9fcc0
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.
Other skills, from other repositories
c-github
Interact with GitHub using the gh CLI and jq. Manage PRs, issues, repositories, and Actions workflows. Make raw API calls with gh api for anything not covered by built-in commands.
watch-pr
Watch a GitHub pull request for CI status, reviews, comments, merge conflicts, and terminal states using the gh-watch extension. Use when the user wants to monitor a PR, wait for CI, or track PR progress.
watch-tag
Watch a GitHub repository for new tags using the gh-watch extension. Use when the user wants to be notified when a tag is created, when a release is cut, or when a tag that includes a specific commit appears (e.g. "tell me when my merge ships in a release").
watch-branch
Watch a GitHub branch for new commits using the gh-watch extension. Use when the user wants to be notified when new commits are pushed to a branch, monitor main for merges, or track branch activity.
watch-commit
Watch a GitHub commit for CI status changes using the gh-watch extension. Use when the user wants to monitor a commit's CI checks, wait for a build to finish, or track CI progress on a specific SHA.
update-architecture-docs
Generate or update the architecture documentation in docs/content/architecture/. Use on "update architecture docs", "generate architecture documentation", "regenerate architecture docs", or after any structural change to the codebase.