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 agents/alirezarezvani/claude-code-skill-factory/prompts-guidegit 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.svg" alt="Measured on agentmods" 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 | $0.00030 | $0.01154 |
| Sonnet 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 6d 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.
- 6d 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 (856 stars, last pushed 9mo ago), licensed MIT. It adds 60 tokens to every session and 2,308 once invoked, about $0.0003 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.
Other agents, from other repositories
prompt-debugger
Evaluates why a prompt produced bad, unexpected, or suboptimal output and suggests targeted fixes. Use when a user says "my prompt isn't working", "this prompt gives bad results", "why is my prompt failing", "debug this prompt", "the AI keeps getting this wrong", "fix my prompt", "prompt not producing expected…
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.
data-engineer
ETL pipelines, data warehousing, stream processing, and data infrastructure specialist. Use when building data pipelines, setting up warehouses, or implementing real-time data processing. Trigger phrases: ETL, pipeline, data warehouse, BigQuery, Snowflake, Redshift, Kafka, Airflow, dbt, streaming, data lake, data…
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.