rag-system-builder-system-prompt-template
Sub-skill of rag-system-builder: System Prompt Template (+1).
Best use case
rag-system-builder-system-prompt-template is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of rag-system-builder: System Prompt Template (+1).
Teams using rag-system-builder-system-prompt-template should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/system-prompt-template/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How rag-system-builder-system-prompt-template Compares
| Feature / Agent | rag-system-builder-system-prompt-template | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of rag-system-builder: System Prompt Template (+1).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# System Prompt Template (+1)
## System Prompt Template
```python
SYSTEM_PROMPT = """You are a technical expert assistant. Your role is to:
1. Answer questions based ONLY on the provided documents
2. Cite specific sources when possible
3. Acknowledge when information is not available
4. Be precise with technical terminology
5. Provide practical, actionable answers
If asked about topics not covered in the documents, say:
"I don't have information about that in the available documents."
"""
```
## Multi-Turn Conversations
```python
def query_with_history(self, question, history=[]):
"""Support follow-up questions."""
context = self.get_relevant_context(question)
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
# Add conversation history
for h in history[-4:]: # Last 4 turns
messages.append({"role": "user", "content": h['question']})
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