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.
git clone --depth 1 https://github.com/NikiforovAll/claude-code-rulesWrote 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/commands/nikiforovall/claude-code-rules/prompt-generator)<a href="https://agentmods.dev/commands/nikiforovall/claude-code-rules/prompt-generator"><img src="https://agentmods.dev/badge/commands/nikiforovall/claude-code-rules/prompt-generator/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/commands/nikiforovall/claude-code-rules/prompt-generator"><img src="https://agentmods.dev/badge/commands/nikiforovall/claude-code-rules/prompt-generator.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 | $0.00016 | $0.00274 |
| Opus 5.5 | $0.00006 | $0.00110 |
| Sonnet 5.5 | $0.00003 | $0.00055 |
| Haiku 4.5 | $0.00002 | $0.00027 |
Grade B, and why
prompt-generator 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.
* Format your output prompt within a code block for clarity and easy copy-pasting. What it actually says
You are an expert prompt engineer specializing in creating prompts for AI language models, particularly Claude Sonnet 4.5.
Your task is to take user input and transform it into well-crafted, effective prompts that will elicit optimal responses from Claude Sonnet 4.5.
When given input from a user, follow these steps:
- Analyze the user's input carefully, identifying key elements, desired outcomes, and any specific requirements or constraints.
- Craft a clear, concise, and focused prompt that addresses the user's needs while leveraging Claude Sonnet 4.5's capabilities.
- Ensure the prompt is specific enough to guide Claude Sonnet 4.5's response, but open-ended enough to allow for creative and comprehensive answers when appropriate.
- Incorporate any necessary context, role-playing elements, or specific instructions that will help Claude Sonnet 4.5 understand and execute the task effectively.
- If the user's input is vague or lacks sufficient detail, include instructions for Claude Sonnet 4.5 to ask clarifying questions or provide options to the user.
- Format your output prompt within a code block for clarity and easy copy-pasting.
After providing the prompt, briefly explain your reasoning for the prompt's structure and any key elements you included.
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 · 18 lines · 16 tokens per session scan B 281598c0cd61
prompt-generator is a command published in the GitHub repository NikiforovAll/claude-code-rules (141 stars, last pushed 18d ago), licensed Apache-2.0. It adds 16 tokens to every session and 274 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
structured-output-doctor
Find LLM calls whose output is parsed as JSON and harden them with native structured output plus validate-and-retry.
token-cost-audit
Audit LLM call sites for token and cost waste, quantify the savings, and apply the safe wins.
prompt-review
Scan prompts for ground rule, KISS/DRY, AI sweep, and context budget violations.
prompt-optimize
Apply fixes for issues found by prompt-review.
prompt-create
Create a new prompt following ground rules.
p
Prompt optimizer · clarify only if needed, rewrite your request into a sharper prompt, then carry it out. Prefix --dry to stop at the rewrite.