GitHub Agentic Workflows is a GitHub CLI extension that lets developers define AI-assisted repository automation in Markdown and run it through GitHub Actions. It is intended for tasks requiring interpretation or reasoning, such as issue triage, pull-request review, CI investigation, documentation maintenance, and dependency analysis. The catalogue entries provide skills and agents for working with these workflows.
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 skills add github/gh-aw --skill prompt-token-efficiencygit clone --depth 1 https://github.com/github/gh-awWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/github/gh-aw/prompt-token-efficiency)<a href="https://agentmods.dev/skills/github/gh-aw/prompt-token-efficiency"><img src="https://agentmods.dev/badge/skills/github/gh-aw/prompt-token-efficiency/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/github/gh-aw/prompt-token-efficiency"><img src="https://agentmods.dev/badge/skills/github/gh-aw/prompt-token-efficiency.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00398 |
| Opus 5.5 | $0.00009 | $0.00159 |
| Sonnet 5.5 | $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 2mo ago.
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.
- 2mo ago 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 (5,579 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 398 once invoked, about $0.0001 per session on Opus 5.5. 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.
prompt-token-efficiency compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| prompt-token-efficiencygithub/gh-aw | Skill | 5.6k | yesterday | MIT | A | 22 | Claude Code, Codex |
| habit-hooks-promptinghabit-hooks/habit-hooks | Skill | 250 | 1mo ago | MIT | A | 61 | Claude Code, Codex |
| prompt-regression-gatealivirgo/Major-AI-Skills | Skill | 2 | yesterday | MIT | A | 53 | Claude Code |
| Prompt EngineeringRaidriar7170/hermes-skilleval | Skill | 29 | 1mo ago | MIT | A | 14 | Claude Code, Codex |
| prompt-optimizationlangwatch/langwatch | Skill | 5.0k | yesterday | Apache-2.0 | A | 105 | Claude Code, Codex |
| hve-buildermicrosoft/hve-core | Skill | 1.6k | yesterday | MIT | A | 54 | Claude Code |
| seedance-vocab-enEmily2040/seedance-2.0 | Skill | 8.0k | 1mo ago | MIT | A | 61 | Claude Code |