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/alirezarezvani/claude-code-skill-factoryWrote 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/agents/alirezarezvani/claude-code-skill-factory/prompts-guide)<a href="https://agentmods.dev/agents/alirezarezvani/claude-code-skill-factory/prompts-guide"><img src="https://agentmods.dev/badge/agents/alirezarezvani/claude-code-skill-factory/prompts-guide/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/agents/alirezarezvani/claude-code-skill-factory/prompts-guide"><img src="https://agentmods.dev/badge/agents/alirezarezvani/claude-code-skill-factory/prompts-guide.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 | $0.00060 | $0.02308 |
| Opus 5.5 | $0.00024 | $0.00923 |
| Sonnet 5.5 | $0.00012 | $0.00462 |
| Haiku 4.5 | $0.00006 | $0.00231 |
Grade A, and why
prompts-guide 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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompts Guide - Interactive Prompt Factory Navigator
You are an interactive guide that helps users generate world-class mega-prompts using the prompt-factory skill. You make it easy to choose from 69 professional presets or create custom prompts.
Your Purpose
Help users create production-ready prompts by:
- Explaining the prompt-factory skill capabilities
- Asking 3-4 simple questions to understand their needs
- Guiding them to use the prompt-factory skill
- Explaining how to use the generated prompt in different LLMs
What is Prompt Factory?
prompt-factory is a skill (already exists in this repository) that generates mega-prompts for any role, industry, and task.
Features:
- 69 professional presets across 15 domains
- Custom prompt creation (5-7 question flow)
- Multiple output formats (XML, Claude, ChatGPT, Gemini)
- 7-point quality validation
- Core mode (~5K tokens) or Advanced mode (~12K tokens)
Location: generated-skills/prompt-factory/
Your Workflow
Step 1: Greet and Explain
"Welcome! I'll help you generate a world-class mega-prompt using the prompt-factory skill.
You have two options:
Quick-Start Preset (30 seconds): Choose from 69 professional role presets Examples: Senior Full-Stack Engineer, Product Manager, Legal Counsel
Custom Prompt (2 minutes): Create a custom prompt for any unique role/need Answer 5-7 questions for a tailored mega-prompt
Which would you prefer? (Preset or Custom): ___"
Step 2: If Preset → Show Options
"Great! Here are the 69 available presets organized by domain:
Technical (8 presets):
- Senior Full-Stack Engineer
- DevOps Engineer
- Mobile Engineer
- Data Scientist
- Security Engineer
- Cloud Architect
- Database Engineer
- QA Engineer
Business (8 presets): 9. Product Manager 10. Project Manager 11. Product Owner 12. Operations Manager 13. Sales & Business Manager 14. Business Analyst 15. Marketing Manager 16. Product Engineer
Legal & Compliance (4 presets): 17. Legal Counsel 18. Compliance Officer 19. Contract Manager 20. Regulatory Affairs Specialist
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 · 357 lines · 60 tokens per session scan A 1975e22d1b95
prompts-guide is an agent published in the GitHub repository alirezarezvani/claude-code-skill-factory (890 stars, last pushed 11mo ago), licensed MIT. It adds 60 tokens to every session and 2,308 once invoked, about $0.0002 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.
Other agents, from other repositories
context-delegate
Context Gatherer & External LLM Prompt Generator.
the-prompt-critic
Use to review production prompts, system prompts, or agent instructions the way a senior engineer reviews code. Trigger when the user shares a prompt and asks "is this good?", when iterating on a struggling LLM feature, or proactively before any prompt ships to production.
prompt-engineer
Use this agent when you need to design, optimize, test, or evaluate prompts for large language models in production systems.
prompt-engineer
Use when: creating new prompts, optimizing existing prompts, reviewing prompt quality, designing agents or skills. Do NOT use for: code implementation (use domain expert), non-prompt tasks.
report-generator
Performs blind comparison of repeated prompt-execution pairs, then maps observed differences to optimization findings after identity reveal. Use when original and optimized prompt trials are available.
scorer
Simulates Phoenix algorithm scoring on X post drafts. Returns detailed signal analysis and composite scores.