agent-prompt-behavior

Design agent system prompts with explicit behavior rules and cognitive control.

2|Updated Jan 1, 2026
One-click install
npx skills add https://github.com/HasnainCodeHub/todo-evaluation --skill agent-prompt-behavior
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-prompt-behavior
Source: https://github.com/HasnainCodeHub/todo-evaluation/tree/main/.claude/skills/agent-prompt-behavior
Command: npx skills add https://github.com/HasnainCodeHub/todo-evaluation --skill agent-prompt-behavior

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Design agent system prompts with explicit behavior rules and cognitive control.

Core Features & Use Cases

  • Design agent system instructions with explicit behavior rules
  • Create intent-to-tool mappings (what user says → what agent does)
  • Define confirmation policies (when to ask before acting)
  • Establish error handling behaviors
  • Prevent hallucinated actions through grounding rules
  • Generate complete, production-ready system prompts

Quick Start

Draft a complete system prompt for a hypothetical task assistant that enforces grounding rules and confirms destructive actions.

Frequently Asked Questions about agent-prompt-behavior

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write an agent system prompt that prevents hallucinated actions?

Agent system prompts prevent hallucinated actions by enforcing explicit grounding rules and cognitive control. You establish deterministic behavior by defining strict identity constraints, intent-to-tool mappings, and confirmation policies for destructive tasks.

What are grounding rules in prompt engineering for AI agents?

Grounding rules in prompt engineering are explicit instructions that bind an agent to verifiable context and capabilities. They prevent hallucinated actions by forcing the agent to rely on defined intent-to-tool mappings rather than making unsupported assumptions.

How do I design confirmation policies for destructive agent actions?

Confirmation policies are designed by defining explicit rules in the system prompt that require the agent to ask for user permission before executing destructive actions. This cognitive control mechanism ensures safety by mapping high-risk intents to mandatory confirmation steps.

What is the best way to structure intent-to-tool mappings in agent prompts?

The best way to structure intent-to-tool mappings is to explicitly define what user inputs trigger specific agent actions. This architecture ensures deterministic behavior by linking natural language intents directly to corresponding tool executions within the system prompt.

How do I add error handling behaviors to my agent system prompt?

Error handling behaviors are added to agent system prompts by defining explicit safety patterns and fallback instructions. This ensures the agent reacts deterministically to tool failures or invalid inputs instead of hallucinating a response.

Can I generate a production-ready system prompt for a task assistant?

Yes, you can generate a complete production-ready system prompt for a task assistant. The architecture includes comprehensive references for implementation, covering identity, capabilities, grounding rules, and error handling for a deployable solution.