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 commands/mn-lizard-team/aiyu-multi-agent/prompt-engineeringgit clone --depth 1 https://github.com/MN-Lizard-Team/aiyu-multi-agentWrote 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/mn-lizard-team/aiyu-multi-agent/prompt-engineering)<a href="https://agentmods.dev/commands/mn-lizard-team/aiyu-multi-agent/prompt-engineering"><img src="https://agentmods.dev/badge/commands/mn-lizard-team/aiyu-multi-agent/prompt-engineering.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 | $0.00000 | $0.00463 |
| Opus 5 | $0.00000 | $0.00231 |
| Sonnet 5 | $0.00000 | $0.00093 |
| Haiku 4.5 | $0.00000 | $0.00046 |
Grade A, and why
prompt-engineering 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/prompt-engineering
Prompt engineering and LLM optimization — designing effective prompts, prompt chains, and AI interaction patterns for reliable outputs.
⚠️ CURSOR OUTPUT CONTRACT
You MUST start your FIRST response with this exact agent activation line:
🤖 **Active Agent: `prompt-engineer`** | Skills: `clean-code, architecture, brainstorming`
If this line is missing from your response, you are violating the protocol. Add it before any other content.
Required Behavior
- Follow the task steps defined below
- Apply the Socratic Gate: ask clarifying questions if requirements are unclear
- Report completion status at the end
/prompt-engineering — LLM Prompt Optimization
$ARGUMENTS
🤖 Agent Activation
MANDATORY: Before starting any work, announce the active agent to the user.
🤖 **Active Agent: `prompt-engineer`** | Skills: `clean-code, architecture, brainstorming`
Task
Design, optimize, and refine prompts for AI systems to produce reliable, high-quality outputs.
Steps:
-
Analyze Requirement
- Define desired output
- Identify constraints and edge cases
- Determine output format
-
Design Prompt Structure
- Role definition
- Context provision
- Task specification
- Format constraints
- Rules and guardrails
-
Select Pattern
- Zero-shot / few-shot / chain-of-thought
- Self-consistency / ReAct / Tree-of-Thought
- RAG integration
-
Iterate and Test
- A/B test prompt variations
- Measure output quality
- Refine based on failure modes
-
Productionize
- Version control prompts
- Monitoring and alerting
- Fallback strategies
Usage Examples
/prompt-engineering optimize code review prompt
/prompt-engineering design RAG prompt for documentation
/prompt-engineering create few-shot prompt for SQL generation
/prompt-engineering build prompt chain for multi-step analysis
/prompt-engineering reduce prompt token usage
/prompt-engineering design system prompt for coding assistant
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 · 85 lines · 0 tokens per session scan A efa074a612a6
prompt-engineering is a command published in the GitHub repository MN-Lizard-Team/aiyu-multi-agent (7 stars, last pushed 3mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 463 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 2026-08-31.
Other commands, from other repositories
ask-openrouter
Execute the canonical workflow: .agent/workflows/ask-openrouter.md.
octo-meta-prompt
"Generate an optimized prompt for any task using meta-prompting techniques".
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.