Prompt Optimization

Prompt Optimization Skill

This skill allows you to view and modify the system prompts of the current AI agent (Team).

Available Tools

• get_team_prompt() — Get the current prompt and source mapping for the team
• submit_prompt_changes(changes) — Send optimized prompts to the user for review

Workflow

Step 1: Get Current Prompts

Call get_team_prompt(). It returns:

• team_id: The team's database ID
• assembled_prompt: The full assembled prompt
• sources: Array of prompt sources, each with:
  • type: "ghost" (base prompt) or "member" (team member prompt)
  • id: Resource ID (Ghost ID or Team ID)
  • name: Display name (Ghost name or Bot name)
  • field: Field name (e.g., "systemPrompt" or "prompt")
  • content: The actual prompt text
  • index: Member index (only for "member" type)

Step 2: Analyze and Rewrite

Based on the user's request, determine which source(s) need modification:

• If the user wants to change the agent's core behavior → modify the ghost source
• If the user wants to change a specific team member's role → modify the member source
• You may modify multiple sources if needed

Rules:

• Only modify the sources that are relevant to the user's request
• Preserve the overall structure and intent of unrelated parts
• Write complete, production-quality prompts (not just appending text)
• If the original prompt is in Chinese, write the modification in Chinese
• If the original prompt is in English, write the modification in English

Step 3: Submit Changes

Call submit_prompt_changes(changes=[...]) with your changes. Each change must include:

{
  "type": "ghost",          // or "member"
  "id": 561,                // from source.id
  "name": "Ghost名称",      // from source.name, used for display
  "field": "systemPrompt",  // from source.field
  "original": "原始内容",    // from source.content
  "suggested": "修改后内容",  // your optimized version
  "index": 0                // only for "member" type, from source.index
}

This will display interactive cards to the user showing the original vs modified prompt. The user can then apply or cancel each change independently.

Example

User: "让这个AI更关注Python编程"

1. Call get_team_prompt()
   → sources: [{ type: "ghost", id: 561, name: "code-helper", field: "systemPrompt", content: "You are a code assistant." }]

2. Rewrite the prompt:
   "You are a code assistant specializing in Python programming. Focus on Python best practices, common patterns, and debugging Python code."

3. Call submit_prompt_changes(changes=[{
     type: "ghost",
     id: 561,
     name: "code-helper",
     field: "systemPrompt",
     original: "You are a code assistant.",
     suggested: "You are a code assistant specializing in Python programming. Focus on Python best practices, common patterns, and debugging Python code."
   }])

Important Notes

• The user must have Developer+ permission on the target resources to apply changes
• Changes are NOT applied automatically — the user reviews and clicks "Apply" on each card
• After the user applies changes, the tool returns __silent_exit__ to end silently