Update prompts.mdx
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Justin Spahr-Summers
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1 changed files with 156 additions and 206 deletions
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@ -3,7 +3,7 @@ title: "Prompts"
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description: "Create resuable prompt templates and workflows"
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description: "Create resuable prompt templates and workflows"
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---
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---
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MCP Prompts enable servers to define reusable prompt templates and workflows that clients can easily surface to users and LLMs. They provide a powerful way to standardize and share common LLM interactions.
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Prompts enable servers to define reusable prompt templates and workflows that clients can easily surface to users and LLMs. They provide a powerful way to standardize and share common LLM interactions.
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## Overview
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## Overview
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@ -78,15 +78,13 @@ To use a prompt, clients make a `prompts/get` request:
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// Response
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// Response
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{
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{
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description?: string, // Optional context about the prompt
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description: "Analyze Python code for potential improvements",
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messages: [ // Messages to send to the LLM
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messages: [
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{
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{
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role: "user" | "assistant",
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role: "user",
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content: {
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content: {
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type: "text" | "image",
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type: "text",
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text?: string,
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text: "Please analyze the following Python code for potential improvements:\n\n```python\ndef calculate_sum(numbers):\n total = 0\n for num in numbers:\n total = total + num\n return total\n\nresult = calculate_sum([1, 2, 3, 4, 5])\nprint(result)\n```"
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data?: string, // base64 for images
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mimeType?: string
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}
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}
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}
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}
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]
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]
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@ -97,57 +95,63 @@ To use a prompt, clients make a `prompts/get` request:
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Prompts can be dynamic and include:
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Prompts can be dynamic and include:
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### Context integration
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### Embedded resource context
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```typescript
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```json
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const analyzeLogsPrompt = {
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name: "analyze-logs",
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description: "Analyze recent system logs",
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arguments: [
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{
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{
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name: "timeframe",
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"name": "analyze-project",
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description: "Time period to analyze",
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"description": "Analyze project logs and code",
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required: true
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"arguments": [
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{
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"name": "timeframe",
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"description": "Time period to analyze logs",
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"required": true
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},
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{
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"name": "fileUri",
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"description": "URI of code file to review",
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"required": true
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}
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}
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]
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]
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};
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server.setRequestHandler(GetPromptRequestSchema, async (request) => {
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if (request.params.name === "analyze-logs") {
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const logs = await getSystemLogs(request.params.arguments?.timeframe);
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return {
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messages: [
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{
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role: "user",
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content: {
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type: "text",
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text: `Analyze these system logs and identify any issues:\n\n${logs}`
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}
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}
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}
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]
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};
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}
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});
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```
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```
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### Resource references
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When handling the `prompts/get` request:
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```typescript
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```json
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const codeReviewPrompt = {
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name: "review-code",
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async getMessages(fileUri: string) {
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const code = await readResource(fileUri);
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return [
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{
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{
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role: "user",
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"messages": [
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content: {
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{
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type: "text",
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"role": "user",
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text: `Review this code for best practices:\n\n${code}`
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"content": {
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"type": "text",
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"text": "Analyze these system logs and the code file for any issues:"
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}
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},
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{
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"role": "user",
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"content": {
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"type": "resource",
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"resource": {
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"uri": "logs://recent?timeframe=1h",
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"text": "[2024-03-14 15:32:11] ERROR: Connection timeout in network.py:127\n[2024-03-14 15:32:15] WARN: Retrying connection (attempt 2/3)\n[2024-03-14 15:32:20] ERROR: Max retries exceeded",
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"mimeType": "text/plain"
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}
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}
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}
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}
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];
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},
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{
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"role": "user",
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"content": {
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"type": "resource",
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"resource": {
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"uri": "file:///path/to/code.py",
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"text": "def connect_to_service(timeout=30):\n retries = 3\n for attempt in range(retries):\n try:\n return establish_connection(timeout)\n except TimeoutError:\n if attempt == retries - 1:\n raise\n time.sleep(5)\n\ndef establish_connection(timeout):\n # Connection implementation\n pass",
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"mimeType": "text/x-python"
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}
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}
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}
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]
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}
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}
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};
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```
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```
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### Multi-step workflows
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### Multi-step workflows
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@ -187,6 +191,8 @@ const debugWorkflow = {
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Here's a complete example of implementing prompts in an MCP server:
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Here's a complete example of implementing prompts in an MCP server:
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<Tabs>
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<Tab title="TypeScript">
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```typescript
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```typescript
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import { Server } from "@modelcontextprotocol/sdk/server";
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import { Server } from "@modelcontextprotocol/sdk/server";
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import {
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import {
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@ -227,6 +233,10 @@ const PROMPTS = {
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const server = new Server({
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const server = new Server({
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name: "example-prompts-server",
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name: "example-prompts-server",
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version: "1.0.0"
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version: "1.0.0"
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}, {
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capabilities: {
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prompts: {}
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}
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});
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});
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// List available prompts
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// List available prompts
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@ -275,6 +285,13 @@ server.setRequestHandler(GetPromptRequestSchema, async (request) => {
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throw new Error("Prompt implementation not found");
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throw new Error("Prompt implementation not found");
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});
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});
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```
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```
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</Tab>
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<Tab title="Python">
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```python
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# Python implementation coming soon
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```
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</Tab>
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</Tabs>
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## Best practices
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## Best practices
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@ -324,70 +341,3 @@ When implementing prompts:
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- Implement timeouts
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- Implement timeouts
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- Consider prompt injection risks
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- Consider prompt injection risks
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- Document security requirements
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- Document security requirements
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## Advanced features
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### Prompt chaining
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```typescript
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const analyzePRPrompt = {
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async getMessages(prId: string) {
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// Chain multiple prompts
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const diffPrompt = await getPrompt("analyze-diff");
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const testsPrompt = await getPrompt("review-tests");
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const securityPrompt = await getPrompt("security-review");
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return [
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...diffPrompt.messages,
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...testsPrompt.messages,
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...securityPrompt.messages
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];
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}
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};
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```
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### Context enrichment
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```typescript
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const troubleshootPrompt = {
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async getMessages(error: string) {
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// Enrich with system context
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const systemInfo = await getSystemInfo();
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const recentLogs = await getRecentLogs();
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const stackTrace = await parseError(error);
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return [
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{
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role: "user",
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content: {
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type: "text",
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text: `Help troubleshoot this error:
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Error: ${error}
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System: ${systemInfo}
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Logs: ${recentLogs}
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Stack: ${stackTrace}`
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}
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}
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];
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}
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};
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```
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### Interactive workflows
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```typescript
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const debugWorkflow = {
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async getMessages(stage: string, context: any) {
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switch (stage) {
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case "initial":
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return promptForError();
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case "analysis":
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return analyzeError(context.error);
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case "solution":
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return suggestFixes(context.analysis);
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case "verification":
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return verifyFix(context.solution);
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}
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}
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};
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```
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