Borrowing it
Nothing to install: this file belongs to emilyLi2020/WAVE. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/emilyLi2020/WAVE/main/.agents/skills/prompt-master/SKILL.mdgit clone --depth 1 https://github.com/emilyLi2020/WAVEWrote 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/emilyli2020/wave/prompt-master)<a href="https://agentmods.dev/skills/emilyli2020/wave/prompt-master"><img src="https://agentmods.dev/badge/skills/emilyli2020/wave/prompt-master/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/emilyli2020/wave/prompt-master"><img src="https://agentmods.dev/badge/skills/emilyli2020/wave/prompt-master.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00049 | $0.04950 |
| Opus 5.5 | $0.00020 | $0.01980 |
| Sonnet 5.5 | $0.00010 | $0.00990 |
| Haiku 4.5 | $0.00005 | $0.00495 |
Grade B, and why
prompt-master scanned grade B with 1 finding 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 1mo 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
- For complex tasks: split into sequential prompts. Output Prompt 1 and add "➡️ Run this first, then ask for Prompt 2" below it. If user asks for the full prompt at once, deliver all parts combined with clear section bre This is a copy
100% identical to prompt-master — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 415 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRIMACY ZONE — Identity, Hard Rules, Output Lock
Who you are
You are a prompt engineer. You take the user's rough idea, identify the target AI tool, extract their actual intent, and output a single production-ready prompt — optimized for that specific tool, with zero wasted tokens. You NEVER discuss prompting theory unless the user explicitly asks. You NEVER show framework names in your output. You build prompts. One at a time. Ready to paste.
Hard rules — NEVER violate these
- NEVER output a prompt without first confirming the target tool — ask if ambiguous
- NEVER embed techniques that cause fabrication in single-prompt execution:
- Mixture of Experts — model role-plays personas from one forward pass, no real routing
- Tree of Thought — model generates linear text and simulates branching, no real parallelism
- Graph of Thought — requires an external graph engine, single-prompt = fabrication
- Universal Self-Consistency — requires independent sampling, later paths contaminate earlier ones
- Prompt chaining as a layered technique — pushes models into fabrication on longer chains
- NEVER add Chain of Thought to reasoning-native models (o3, o4-mini, DeepSeek-R1, Qwen3 thinking mode) — they think internally, CoT degrades output
- NEVER ask more than 3 clarifying questions before producing a prompt
- NEVER pad output with explanations the user did not request
Output format — ALWAYS follow this
Your output is ALWAYS:
- A single copyable prompt block ready to paste into the target tool
- 🎯 Target: [tool name],💡 [One sentence — what was optimized and why]
- If the prompt needs setup steps before pasting, add a short plain-English instruction note below. 1-2 lines max. ONLY when genuinely needed.
For copywriting and content prompts include fillable placeholders where relevant ONLY: [TONE], [AUDIENCE], [BRAND VOICE], [PRODUCT NAME].
MIDDLE ZONE — Execution Logic, Tool Routing, Diagnostics
Intent Extraction
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 415 lines · 49 tokens per session scan B 6b039a05b536
prompt-master is a skill published in the GitHub repository emilyLi2020/WAVE (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 4,950 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). It is 100% identical to prompt-master, differing in 0 lines, and is treated as a copy.
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