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npx skills add langfuse/skills --skill "langfuse-prompt-migration"
Install specific skill from multi-skill repository
# Description
Migrate hardcoded prompts to Langfuse for version control and deployment-free iteration. Use when user wants to externalize prompts, move prompts to Langfuse, or set up prompt management.
# SKILL.md
name: langfuse-prompt-migration
description: Migrate hardcoded prompts to Langfuse for version control and deployment-free iteration. Use when user wants to externalize prompts, move prompts to Langfuse, or set up prompt management.
Langfuse Prompt Migration
Migrate hardcoded prompts to Langfuse for version control, A/B testing, and deployment-free iteration.
Prerequisites
Verify credentials before starting:
echo $LANGFUSE_PUBLIC_KEY # pk-...
echo $LANGFUSE_SECRET_KEY # sk-...
echo $LANGFUSE_HOST # https://cloud.langfuse.com or self-hosted
If not set, ask user to configure them first.
Migration Flow
1. Scan codebase for prompts
2. Analyze templating compatibility
3. Propose structure (names, subprompts, variables)
4. User approves
5. Create prompts in Langfuse
6. Refactor code to use get_prompt()
7. Link prompts to traces (if tracing enabled)
8. Verify application works
Step 1: Find Prompts
Search for these patterns:
| Framework | Look for |
|---|---|
| OpenAI | messages=[{"role": "system", "content": "..."}] |
| Anthropic | system="..." |
| LangChain | ChatPromptTemplate, SystemMessage |
| Vercel AI | system: "...", prompt: "..." |
| Raw | Multi-line strings near LLM calls |
Step 2: Check Templating Compatibility
CRITICAL: Langfuse only supports simple {{variable}} substitution. No conditionals, loops, or filters.
| Template Feature | Langfuse Native | Action |
|---|---|---|
{{variable}} |
✅ | Direct migration |
{var} / ${var} |
⚠️ | Convert to {{var}} |
{% if %} / {% for %} |
❌ | Move logic to code |
{{ var \| filter }} |
❌ | Apply filter in code |
Decision Tree
Contains {% if %}, {% for %}, or filters?
├─ No → Direct migration
└─ Yes → Choose:
├─ Option A (RECOMMENDED): Move logic to code, pass pre-computed values
└─ Option B: Store raw template, compile client-side with Jinja2
└─ ⚠️ Loses: Playground preview, UI experiments
Simplifying Complex Templates
Conditionals → Pre-compute in code:
# Instead of {% if user.is_premium %}...{% endif %} in prompt
# Use {{tier_message}} and compute value in code before compile()
Loops → Pre-format in code:
# Instead of {% for tool in tools %}...{% endfor %} in prompt
# Use {{tools_list}} and format the list in code before compile()
For external templating details, fetch: https://langfuse.com/faq/all/using-external-templating-libraries
Step 3: Propose Structure
Naming Conventions
| Rule | Example | Bad |
|---|---|---|
| Lowercase, hyphenated | chat-assistant |
ChatAssistant_v2 |
| Feature-based | document-summarizer |
prompt1 |
| Hierarchical for related | support/triage |
supportTriage |
Prefix subprompts with _ |
_base-personality |
shared-personality |
Identify Subprompts
Extract when:
- Same text in 2+ prompts
- Represents distinct component (personality, safety rules, format)
- Would need to change together
Variable Extraction
| Make Variable | Keep Hardcoded |
|---|---|
User-specific ({{user_name}}) |
Output format instructions |
Dynamic content ({{context}}) |
Safety guardrails |
Per-request ({{query}}) |
Persona/personality |
Environment-specific ({{company_name}}) |
Static examples |
Step 4: Present Plan to User
Format:
Found N prompts across M files:
src/chat.py:
- System prompt (47 lines) → 'chat-assistant'
src/support/triage.py:
- Triage prompt (34 lines) → 'support/triage'
⚠️ Contains {% if %} - will simplify
Subprompts to extract:
- '_base-personality' - used by: chat-assistant, support/triage
Variables to add:
- {{user_name}} - hardcoded in 2 prompts
Proceed?
Step 5: Create Prompts in Langfuse
Use langfuse.create_prompt() with:
- name: Your chosen name
- prompt: Template text (or message array for chat type)
- type: "text" or "chat"
- labels: ["production"] (they're already live)
- config: Optional model settings
Labeling strategy:
- production → All migrated prompts
- staging → Add later for testing
- latest → Auto-applied by Langfuse
For full API: fetch https://langfuse.com/docs/prompts/get-started
Step 6: Refactor Code
Replace hardcoded prompts with:
prompt = langfuse.get_prompt("name", label="production")
messages = prompt.compile(var1=value1, var2=value2)
Key points:
- Always use label="production" (not latest) for stability
- Call .compile() to substitute variables
- For chat prompts, result is message array ready for API
For SDK examples (Python/JS/TS): fetch https://langfuse.com/docs/prompts/get-started
Step 7: Link Prompts to Traces
If codebase uses Langfuse tracing, link prompts so you can see which version produced each response.
Detect Existing Tracing
Look for:
- @observe() decorators
- langfuse.trace() calls
- from langfuse.openai import openai (instrumented client)
Link Methods
| Setup | How to Link |
|---|---|
@observe() decorator |
langfuse_context.update_current_observation(prompt=prompt) |
| Manual tracing | trace.generation(prompt=prompt, ...) |
| OpenAI integration | openai.chat.completions.create(..., langfuse_prompt=prompt) |
Verify in UI
- Go to Traces → select a trace
- Click on Generation
- Check Prompt field shows name and version
For tracing details: fetch https://langfuse.com/docs/prompts/get-started#link-with-langfuse-tracing
Step 8: Verify Migration
Checklist
- [ ] All prompts created with
productionlabel - [ ] Code fetches with
label="production" - [ ] Variables compile without errors
- [ ] Subprompts resolve correctly
- [ ] Application behavior unchanged
- [ ] Generations show linked prompt in UI (if tracing)
Common Issues
| Issue | Solution |
|---|---|
PromptNotFoundError |
Check name spelling |
| Variables not replaced | Use {{var}} not {var}, call .compile() |
| Subprompt not resolved | Must exist with same label |
| Old prompt cached | Restart app |
Out of Scope
- Prompt engineering (writing better prompts)
- Evaluation setup
- A/B testing workflow
- Non-LLM string templates
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.