feat: add reminders page, BMad skills upgrade, MCP server refactor

- Add reminders page with navigation support
- Upgrade BMad builder module to skills-based architecture
- Refactor MCP server: extract tools and auth into separate modules
- Add connections cache, custom AI provider support
- Update prisma schema and generated client
- Various UI/UX improvements and i18n updates
- Add service worker for PWA support

Made-with: Cursor

keep-notes/app/actions/custom-provider.ts

@@ -0,0 +1,114 @@
+'use server'
+
+interface OpenAIModel {
+  id: string
+  object: string
+  created?: number
+  owned_by?: string
+}
+
+interface OpenAIModelsResponse {
+  object: string
+  data: OpenAIModel[]
+}
+
+async function fetchModelsFromEndpoint(
+  endpoint: string,
+  apiKey?: string
+): Promise<{ success: boolean; models: string[]; error?: string }> {
+  try {
+    const headers: Record<string, string> = {
+      'Content-Type': 'application/json',
+    }
+    if (apiKey) {
+      headers['Authorization'] = `Bearer ${apiKey}`
+    }
+
+    const response = await fetch(endpoint, {
+      method: 'GET',
+      headers,
+      signal: AbortSignal.timeout(8000),
+    })
+
+    if (!response.ok) {
+      throw new Error(`API returned ${response.status}: ${response.statusText}`)
+    }
+
+    const data = await response.json() as OpenAIModelsResponse
+
+    const modelIds = (data.data || [])
+      .map((m) => m.id)
+      .filter(Boolean)
+      .sort()
+
+    return { success: true, models: modelIds }
+  } catch (error: any) {
+    console.error('Failed to fetch provider models:', error)
+    return {
+      success: false,
+      models: [],
+      error: error.message || 'Failed to connect to provider',
+    }
+  }
+}
+
+/**
+ * Fetch all models from a custom OpenAI-compatible provider.
+ * Uses GET /v1/models (standard endpoint).
+ */
+export async function getCustomModels(
+  baseUrl: string,
+  apiKey?: string
+): Promise<{ success: boolean; models: string[]; error?: string }> {
+  if (!baseUrl) {
+    return { success: false, models: [], error: 'Base URL is required' }
+  }
+
+  const cleanUrl = baseUrl.replace(/\/$/, '').replace(/\/v1$/, '')
+  return fetchModelsFromEndpoint(`${cleanUrl}/v1/models`, apiKey)
+}
+
+/**
+ * Fetch embedding-specific models from a custom provider.
+ * Tries GET /v1/embeddings/models first (OpenRouter-specific endpoint that returns
+ * only embedding models). Falls back to GET /v1/models filtered by common
+ * embedding model name patterns if the dedicated endpoint is unavailable.
+ */
+export async function getCustomEmbeddingModels(
+  baseUrl: string,
+  apiKey?: string
+): Promise<{ success: boolean; models: string[]; error?: string }> {
+  if (!baseUrl) {
+    return { success: false, models: [], error: 'Base URL is required' }
+  }
+
+  const cleanUrl = baseUrl.replace(/\/$/, '').replace(/\/v1$/, '')
+
+  // Try the OpenRouter-specific embeddings models endpoint first
+  const embeddingsEndpoint = await fetchModelsFromEndpoint(
+    `${cleanUrl}/v1/embeddings/models`,
+    apiKey
+  )
+
+  if (embeddingsEndpoint.success && embeddingsEndpoint.models.length > 0) {
+    return embeddingsEndpoint
+  }
+
+  // Fallback: fetch all models and filter by common embedding name patterns
+  const allModels = await fetchModelsFromEndpoint(`${cleanUrl}/v1/models`, apiKey)
+
+  if (!allModels.success) {
+    return allModels
+  }
+
+  const embeddingKeywords = ['embed', 'embedding', 'ada', 'e5', 'bge', 'gte', 'minilm']
+  const filtered = allModels.models.filter((id) =>
+    embeddingKeywords.some((kw) => id.toLowerCase().includes(kw))
+  )
+
+  // If the filter finds nothing, return all models so the user can still pick
+  return {
+    success: true,
+    models: filtered.length > 0 ? filtered : allModels.models,
+  }
+}