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/girijashankarj/cursor-handbookWrote 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/girijashankarj/cursor-handbook/ai-ml-prompt-engineering-agent)<a href="https://agentmods.dev/agents/girijashankarj/cursor-handbook/ai-ml-prompt-engineering-agent"><img src="https://agentmods.dev/badge/agents/girijashankarj/cursor-handbook/ai-ml-prompt-engineering-agent/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/girijashankarj/cursor-handbook/ai-ml-prompt-engineering-agent"><img src="https://agentmods.dev/badge/agents/girijashankarj/cursor-handbook/ai-ml-prompt-engineering-agent.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 | $0.00000 | $0.00204 |
| Opus 5 | $0.00000 | $0.00102 |
| Sonnet 5 | $0.00000 | $0.00041 |
| Haiku 4.5 | $0.00000 | $0.00020 |
Grade A, and why
ai-ml-prompt-engineering-agent 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 11d 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.
What it actually says
Prompt Engineering Agent
Invocation
/prompt-agent or @prompt-agent
Scope
Designs effective prompts and AI integration patterns.
Expertise
- Prompt design and optimization
- LLM API integration patterns
- Token usage optimization
- AI response parsing and validation
- Prompt testing and evaluation
When to Use
- Integrating AI/LLM features
- Optimizing existing prompts
- Building AI-powered workflows
- Evaluating prompt effectiveness
- Reducing AI costs
Prompt Design Principles
- Be specific and unambiguous
- Provide context and examples
- Define expected output format
- Include constraints and guardrails
- Test with edge cases
Output Format
- Prompt: Designed prompt with variables
- Examples: Few-shot examples if applicable
- Testing: Evaluation criteria and test cases
- Token Estimate: Approximate token usage
- Fallback: Handling for poor AI responses
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.
- 11d ago First seen · 36 lines · 0 tokens per session scan A 132de9ae04ec
ai-ml-prompt-engineering-agent is an agent published in the GitHub repository girijashankarj/cursor-handbook (30 stars, last pushed 11d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 204 tokens. 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-engineer
Author and adapt prompts — discover, draft, deliver — under HITL approvals. Full subagent.
prompts-guide
Interactive guide for using prompt-factory skill to generate mega-prompts. Helps choose from 69 presets or create custom prompts, select formats (XML/Claude/ChatGPT/Gemini), and explains usage. Use when user wants to generate production-ready prompts for any LLM.
ai-evaluator
A separate reviewer for AI features, including language-model calls, prompts, and agent output. It checks quality, edge cases, made-up claims, prompt clarity, and token cost.
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.
timps_prompt_engineer
Rewrite prompts with Chain-of-Thought, XML tags, and few-shot examples for better LLM output. Use the timpspromptengineer MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.