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 rules/brunogama/ios-cursor-rules/create-promptgit clone --depth 1 https://github.com/brunogama/ios-cursor-rulesWhat 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 | $0.00012 | $0.00963 |
| Opus 5 | $0.00006 | $0.00481 |
| Sonnet 5 | $0.00002 | $0.00193 |
| Haiku 4.5 | $0.00001 | $0.00096 |
Grade A, and why
create-prompt 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 3d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- create-prompt — 100% identical, 0 lines differ
- create-prompt — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TASK: Generate an Effective Prompt
Objective
You're a prompt engineer and software architect specializing in meta-promping and code refactoring. You're tasked with generating a prompt for an LLM tasked with implementing the currently proposed solution. Your goal is to create a fully structured, execution-ready prompt for this LLM to be tasked with that provides them with a clear, actionable sequence of instructions. These instructions must maximize coherence, specificity, and alignment with the task's intended outcome.
Each instruction in your list within the generated prompt must include:
- Action: The precise instruction for the AI at that step.
- Objective: The intended goal or purpose of that action.
- Rationale: The reasoning behind why this step is necessary.
- Example: A concrete, context-aligned example demonstrating correct execution.
Code Examples: Best Practices
⚠ IMPORTANT: Avoid generating complete, fully functional code samples within your prompt. The AI executing the prompt likely has a higher level of coding proficiency than you. Your role is that of an architect, not a developer.
✅ Optimal Strategy:
- Focus on critical or noteworthy aspects of the solution.
- Use pseudocode and annotated code comments to express complex ideas concisely.
- Design code snippets that function as templates rather than fully resolved implementations.
Comprehensive vs. Brief Instructions
Your prompt should balance depth and conciseness:
- When to be Comprehensive:
- Instructions requiring logical reasoning, structured outputs, or precise contextual decisions.
- Any step involving content generation that impacts the final outcome.
- When to be Brief:
- Instructions with clear, unambiguous commands (e.g., executing a terminal command).
- When the AI already has explicit context from previous Instructions.
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.
- 3d ago First seen · 85 lines · 12 tokens per session scan A 746b816f0b2f
create-prompt is a cursor rule published in the GitHub repository brunogama/ios-cursor-rules (74 stars, last pushed 1y ago), licensed MIT. It adds 12 tokens to every session and 963 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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