chore: re-enable the prefer-const ESLint rule

.changeset/fix-multiline-quoted-command-parsing.md

@@ -14,4 +14,4 @@ Quote masking is comment-aware: a quote character inside a `#` comment is not pa
 
 **Pattern selector (UI)**: the command pattern breakdown shown after execution now uses the same heredoc- and quote-aware parser (`parseCommand`) before extracting patterns, so an unterminated or terminated heredoc no longer produces spurious tokens like `EOF`, body-line words, or `<<` fragments in the allow/deny selector.
 
-Note: this change only prevents *auto-approval* of fragments from a malformed command; it does not reject malformed commands before execution, which will be addressed in a separate PR to keep the scope focused here.
+Note: this change only prevents _auto-approval_ of fragments from a malformed command; it does not reject malformed commands before execution, which will be addressed in a separate PR to keep the scope focused here.

README.md

@@ -89,7 +89,7 @@ for this exact support, so if you are having problems or if you have question, j
 - [简体中文](locales/zh-CN/README.md)
 - [繁體中文](locales/zh-TW/README.md)
 - ...
-      </details>
+  </details>
 
 ---
 

src/api/providers/__tests__/unbound.spec.ts

@@ -181,4 +181,24 @@ describe("UnboundHandler", () => {
 			}),
 		)
 	})
+
+	it("completePrompt returns the response text", async () => {
+		const mockCreate = (OpenAI as unknown as any)().chat.completions.create
+		mockCreate.mockResolvedValue({
+			choices: [{ message: { content: "completed text" } }],
+		})
+
+		const handler = new UnboundHandler({
+			unboundApiKey: "test-key",
+			unboundModelId: "openai/gpt-4o",
+		})
+
+		const result = await handler.completePrompt("Write a haiku")
+		expect(result).toBe("completed text")
+		expect(mockCreate).toHaveBeenCalledWith(
+			expect.objectContaining({
+				messages: [{ role: "system", content: "Write a haiku" }],
+			}),
+		)
+	})
 })

src/api/providers/anthropic-vertex.ts

@@ -71,7 +71,7 @@ export class AnthropicVertexHandler extends BaseProvider implements SingleComple
 		messages: Anthropic.Messages.MessageParam[],
 		metadata?: ApiHandlerCreateMessageMetadata,
 	): ApiStream {
-		let { id, info, temperature, maxTokens, reasoning: thinking, betas } = this.getModel()
+		const { id, info, temperature, maxTokens, reasoning: thinking, betas } = this.getModel()
 
 		const { supportsPromptCache } = info
 
@@ -210,7 +210,7 @@ export class AnthropicVertexHandler extends BaseProvider implements SingleComple
 
 	getModel() {
 		const modelId = this.options.apiModelId
-		let id = modelId && modelId in vertexModels ? (modelId as VertexModelId) : vertexDefaultModelId
+		const id = modelId && modelId in vertexModels ? (modelId as VertexModelId) : vertexDefaultModelId
 		let info: ModelInfo = vertexModels[id]
 
 		// Check if 1M context beta should be enabled for supported models
@@ -270,7 +270,7 @@ export class AnthropicVertexHandler extends BaseProvider implements SingleComple
 
 	async completePrompt(prompt: string) {
 		try {
-			let {
+			const {
 				id,
 				info: { supportsPromptCache },
 				temperature,

src/api/providers/anthropic.ts

@@ -54,7 +54,7 @@ export class AnthropicHandler extends BaseProvider implements SingleCompletionHa
 	): ApiStream {
 		let stream: AnthropicStream<Anthropic.Messages.RawMessageStreamEvent>
 		const cacheControl: CacheControlEphemeral = { type: "ephemeral" }
-		let {
+		const {
 			id: modelId,
 			betas = ["fine-grained-tool-streaming-2025-05-14"],
 			maxTokens,
@@ -352,7 +352,7 @@ export class AnthropicHandler extends BaseProvider implements SingleCompletionHa
 
 	getModel() {
 		const modelId = this.options.apiModelId
-		let id = modelId && modelId in anthropicModels ? (modelId as AnthropicModelId) : anthropicDefaultModelId
+		const id = modelId && modelId in anthropicModels ? (modelId as AnthropicModelId) : anthropicDefaultModelId
 		let info: ModelInfo = anthropicModels[id]
 
 		// If 1M context beta is enabled for supported models, update the model info
@@ -398,7 +398,7 @@ export class AnthropicHandler extends BaseProvider implements SingleCompletionHa
 	}
 
 	async completePrompt(prompt: string) {
-		let { id: model, temperature } = this.getModel()
+		const { id: model, temperature } = this.getModel()
 
 		let message
 		try {

src/api/providers/bedrock.ts

@@ -216,7 +216,7 @@ export class AwsBedrockHandler extends BaseProvider implements SingleCompletionH
 	constructor(options: ProviderSettings) {
 		super()
 		this.options = options
-		let region = this.options.awsRegion
+		const region = this.options.awsRegion
 
 		// process the various user input options, be opinionated about the intent of the options
 		// and determine the model to use during inference and for cost calculations
@@ -591,8 +591,11 @@ export class AwsBedrockHandler extends BaseProvider implements SingleCompletionH
 						//so that pricing, context window, caching etc have values that can be used
 						//However, we want to keep the id of the model to be the ID for the router for
 						//subsequent requests so they are sent back through the router
-						let invokedArnInfo = this.parseArn(streamEvent.trace.promptRouter.invokedModelId)
-						let invokedModel = this.getModelById(invokedArnInfo.modelId as string, invokedArnInfo.modelType)
+						const invokedArnInfo = this.parseArn(streamEvent.trace.promptRouter.invokedModelId)
+						const invokedModel = this.getModelById(
+							invokedArnInfo.modelId as string,
+							invokedArnInfo.modelType,
+						)
 						if (invokedModel) {
 							invokedModel.id = modelConfig.id
 							this.costModelConfig = invokedModel
@@ -934,7 +937,7 @@ export class AwsBedrockHandler extends BaseProvider implements SingleCompletionH
 		}
 
 		// Get cache point placements
-		let strategy = new MultiPointStrategy(config)
+		const strategy = new MultiPointStrategy(config)
 		const cacheResult = strategy.determineOptimalCachePoints()
 
 		// Store cache point placements for future use if conversation ID is provided
@@ -998,7 +1001,7 @@ export class AwsBedrockHandler extends BaseProvider implements SingleCompletionH
 		 */
 
 		const arnRegex = /^arn:[^:]+:(?:bedrock|sagemaker):([^:]+):([^:]*):(?:([^\/]+)\/([\w\.\-:]+)|([^\/]+))$/
-		let match = arn.match(arnRegex)
+		const match = arn.match(arnRegex)
 
 		if (match && match[1] && match[3] && match[4]) {
 			// Create the result object
@@ -1025,7 +1028,7 @@ export class AwsBedrockHandler extends BaseProvider implements SingleCompletionH
 			// Check if the original model ID had a region prefix
 			if (originalModelId && result.modelId !== originalModelId) {
 				// If the model ID changed after parsing, it had a region prefix
-				let prefix = originalModelId.replace(result.modelId, "")
+				const prefix = originalModelId.replace(result.modelId, "")
 				result.crossRegionInference = AwsBedrockHandler.isSystemInferenceProfile(prefix)
 			}
 

src/api/providers/fake-ai.ts

@@ -38,7 +38,7 @@ interface FakeAI {
  *
  * We use the ID to lookup the original FakeAI object in the mapping.
  */
-let fakeAiMap: Map<string, FakeAI> = new Map()
+const fakeAiMap: Map<string, FakeAI> = new Map()
 
 export class FakeAIHandler implements ApiHandler, SingleCompletionHandler {
 	private ai: FakeAI

src/api/providers/fetchers/ollama.ts

@@ -81,7 +81,7 @@ export async function getOllamaModels(
 
 		const response = await axios.get<OllamaModelsResponse>(`${baseUrl}/api/tags`, { headers })
 		const parsedResponse = OllamaModelsResponseSchema.safeParse(response.data)
-		let modelInfoPromises = []
+		const modelInfoPromises = []
 
 		if (parsedResponse.success) {
 			for (const ollamaModel of parsedResponse.data.models) {

src/api/providers/fetchers/opencode-go.ts

@@ -72,7 +72,9 @@ export async function getOpencodeGoModels(apiKey?: string): Promise<Record<strin
 		const data = Array.isArray(rawData) ? rawData : []
 
 		if (!result.success) {
-			console.warn(`Opencode Go models response did not match expected schema; falling back to per-item parsing: ${JSON.stringify(result.error.format())}`)
+			console.warn(
+				`Opencode Go models response did not match expected schema; falling back to per-item parsing: ${JSON.stringify(result.error.format())}`,
+			)
 		}
 
 		for (const rawModel of data) {

src/api/providers/gemini.ts

@@ -678,7 +678,7 @@ export class GeminiHandler extends BaseProvider implements SingleCompletionHandl
 		// Bill both completion and reasoning ("thoughts") tokens as output.
 		const billedOutputTokens = outputTokens + reasoningTokens
 
-		let cacheReadCost = cacheReadTokens > 0 ? cacheReadsPrice * (cacheReadTokens / 1_000_000) : 0
+		const cacheReadCost = cacheReadTokens > 0 ? cacheReadsPrice * (cacheReadTokens / 1_000_000) : 0
 
 		const inputTokensCost = inputPrice * (uncachedInputTokens / 1_000_000)
 		const outputTokensCost = outputPrice * (billedOutputTokens / 1_000_000)

src/api/providers/lite-llm.ts

@@ -185,7 +185,7 @@ export class LiteLLMHandler extends RouterProvider implements SingleCompletionHa
 		}
 
 		// Required by some providers; others default to max tokens allowed
-		let maxTokens: number | undefined = info.maxTokens ?? undefined
+		const maxTokens: number | undefined = info.maxTokens ?? undefined
 
 		// Check if this is a GPT-5 model that requires max_completion_tokens instead of max_tokens
 		const isGPT5Model = this.isGpt5(modelId)

src/api/providers/minimax.ts

@@ -81,7 +81,6 @@ export class MiniMaxHandler extends BaseProvider implements SingleCompletionHand
 		messages: Anthropic.Messages.MessageParam[],
 		metadata?: ApiHandlerCreateMessageMetadata,
 	): ApiStream {
-		let stream: AnthropicStream<Anthropic.Messages.RawMessageStreamEvent>
 		const cacheControl: CacheControlEphemeral = { type: "ephemeral" }
 		const { id: modelId, info, maxTokens, temperature } = this.getModel()
 
@@ -113,7 +112,7 @@ export class MiniMaxHandler extends BaseProvider implements SingleCompletionHand
 			tool_choice: convertOpenAIToolChoice(metadata?.tool_choice),
 		}
 
-		stream = await this.client.messages.create(requestParams)
+		const stream = await this.client.messages.create(requestParams)
 
 		let inputTokens = 0
 		let outputTokens = 0

src/api/providers/openai-codex.ts

@@ -1117,7 +1117,7 @@ export class OpenAiCodexHandler extends BaseProvider implements SingleCompletion
 	override getModel() {
 		const modelId = this.options.apiModelId
 
-		let id = modelId && modelId in openAiCodexModels ? (modelId as OpenAiCodexModelId) : openAiCodexDefaultModelId
+		const id = modelId && modelId in openAiCodexModels ? (modelId as OpenAiCodexModelId) : openAiCodexDefaultModelId
 
 		const info: ModelInfo = openAiCodexModels[id]
 

src/api/providers/openai-native.ts

@@ -674,8 +674,8 @@ export class OpenAiNativeHandler extends BaseProvider implements SingleCompletio
 		const decoder = new TextDecoder()
 		let buffer = ""
 		let hasContent = false
-		let totalInputTokens = 0
-		let totalOutputTokens = 0
+		const totalInputTokens = 0
+		const totalOutputTokens = 0
 
 		try {
 			while (true) {
@@ -1435,7 +1435,7 @@ export class OpenAiNativeHandler extends BaseProvider implements SingleCompletio
 	override getModel() {
 		const modelId = this.options.apiModelId
 
-		let id =
+		const id =
 			modelId && modelId in openAiNativeModels ? (modelId as OpenAiNativeModelId) : openAiNativeDefaultModelId
 
 		const info: ModelInfo = openAiNativeModels[id]

src/api/providers/openrouter.ts

@@ -575,7 +575,7 @@ export class OpenRouterHandler extends BaseProvider implements SingleCompletionH
 	}
 
 	async completePrompt(prompt: string) {
-		let { id: modelId, maxTokens, temperature, reasoning } = await this.fetchModel()
+		const { id: modelId, maxTokens, temperature, reasoning } = await this.fetchModel()
 
 		const completionParams: OpenRouterChatCompletionParams = {
 			model: modelId,

src/api/providers/requesty.ts

@@ -206,7 +206,7 @@ export class RequestyHandler extends BaseProvider implements SingleCompletionHan
 	async completePrompt(prompt: string): Promise<string> {
 		const { id: model, maxTokens: max_tokens, temperature } = await this.fetchModel()
 
-		let openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [{ role: "system", content: prompt }]
+		const openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [{ role: "system", content: prompt }]
 
 		const completionParams: RequestyChatCompletionParams = {
 			model,

src/api/providers/unbound.ts

@@ -194,7 +194,7 @@ export class UnboundHandler extends BaseProvider implements SingleCompletionHand
 	async completePrompt(prompt: string): Promise<string> {
 		const { id: model, maxTokens: max_tokens, temperature } = await this.fetchModel()
 
-		let openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [{ role: "system", content: prompt }]
+		const openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [{ role: "system", content: prompt }]
 
 		const completionParams: UnboundChatCompletionParams = {
 			model,

src/api/providers/vertex.ts

@@ -14,7 +14,7 @@ export class VertexHandler extends GeminiHandler implements SingleCompletionHand
 
 	override getModel() {
 		const modelId = this.options.apiModelId
-		let id = modelId && modelId in vertexModels ? (modelId as VertexModelId) : vertexDefaultModelId
+		const id = modelId && modelId in vertexModels ? (modelId as VertexModelId) : vertexDefaultModelId
 		let info: ModelInfo = vertexModels[id]
 		const params = getModelParams({
 			format: "gemini",

src/api/transform/cache-strategy/__tests__/cache-strategy.spec.ts

@@ -342,9 +342,8 @@ describe("Cache Strategy", () => {
 				convertToBedrockConverseMessagesMock.lastConfig = config
 
 				// Create a strategy based on the config
-				let strategy
 				// Use MultiPointStrategy for all cases
-				strategy = new MultiPointStrategy(config as any)
+				const strategy = new MultiPointStrategy(config as any)
 
 				// Store the result
 				const result = strategy.determineOptimalCachePoints()
@@ -484,9 +483,8 @@ describe("Cache Strategy", () => {
 				convertToBedrockConverseMessagesMock.lastConfig = config
 
 				// Create a strategy based on the config
-				let strategy
 				// Use MultiPointStrategy for all cases
-				strategy = new MultiPointStrategy(config as any)
+				const strategy = new MultiPointStrategy(config as any)
 
 				// Store the result
 				const result = strategy.determineOptimalCachePoints()

src/api/transform/cache-strategy/multi-point-strategy.ts

@@ -43,7 +43,7 @@ export class MultiPointStrategy extends CacheStrategy {
 
 		const placements = this.determineMessageCachePoints(minTokensPerPoint, remainingCachePoints)
 		const messages = this.messagesToContentBlocks(this.config.messages)
-		let cacheResult = this.formatResult(systemBlocks, this.applyCachePoints(messages, placements))
+		const cacheResult = this.formatResult(systemBlocks, this.applyCachePoints(messages, placements))
 
 		// Store the placements for future use (to maintain consistency across consecutive messages)
 		// This needs to be handled by the caller by passing these placements back in the next call

src/api/transform/caching/vercel-ai-gateway.ts

@@ -19,7 +19,7 @@ export function addCacheBreakpoints(systemPrompt: string, messages: OpenAI.Chat.
 
 		if (Array.isArray(msg.content)) {
 			// Find the last text part in the message content
-			let lastTextPart = msg.content.filter((part) => part.type === "text").pop()
+			const lastTextPart = msg.content.filter((part) => part.type === "text").pop()
 
 			if (lastTextPart && lastTextPart.text && lastTextPart.text.length > 0) {
 				// @ts-ignore-next-line

src/api/transform/model-params.ts

@@ -98,7 +98,7 @@ export function getModelParams({
 	let temperature = customTemperature ?? model.defaultTemperature ?? defaultTemperature
 	let reasoningBudget: ModelParams["reasoningBudget"] = undefined
 	let reasoningEffort: ModelParams["reasoningEffort"] = undefined
-	let verbosity: VerbosityLevel | undefined = customVerbosity
+	const verbosity: VerbosityLevel | undefined = customVerbosity
 
 	if (shouldUseReasoningBudget({ model, settings })) {
 		// Check if this is a Gemini 2.5 Pro model

src/api/transform/openai-format.ts

@@ -346,7 +346,7 @@ export function convertToOpenAiMessages(
 				)
 
 				// Process tool result messages FIRST since they must follow the tool use messages
-				let toolResultImages: Anthropic.Messages.ImageBlockParam[] = []
+				const toolResultImages: Anthropic.Messages.ImageBlockParam[] = []
 				toolMessages.forEach((toolMessage) => {
 					// The Anthropic SDK allows tool results to be a string or an array of text and image blocks, enabling rich and structured content. In contrast, the OpenAI SDK only supports tool results as a single string, so we map the Anthropic tool result parts into one concatenated string to maintain compatibility.
 					let content: string
@@ -462,7 +462,7 @@ export function convertToOpenAiMessages(
 				}
 
 				// Process tool use messages
-				let tool_calls: OpenAI.Chat.ChatCompletionMessageToolCall[] = toolMessages.map((toolMessage) => ({
+				const tool_calls: OpenAI.Chat.ChatCompletionMessageToolCall[] = toolMessages.map((toolMessage) => ({
 					id: normalizeId(toolMessage.id),
 					type: "function",
 					function: {

src/core/auto-approval/commands.ts

@@ -1,6 +1,5 @@
 import { parseCommand } from "../../shared/parse-command"
 
-
 /**
  * Detect dangerous parameter substitutions that could lead to command execution.
  * These patterns are never auto-approved and always require explicit user approval.

src/core/config/CustomModesManager.ts

@@ -775,7 +775,7 @@ export class CustomModesManager {
 				? path.join(baseDir, `rules-${slug}`)
 				: path.join(baseDir, ".roo", `rules-${slug}`)
 
-			let rulesFiles: RuleFile[] = []
+			const rulesFiles: RuleFile[] = []
 			try {
 				const stats = await fs.stat(modeRulesDir)
 				if (stats.isDirectory()) {

src/core/config/ProviderSettingsManager.ts

@@ -806,7 +806,7 @@ export class ProviderSettingsManager {
 							existingNames.delete(existingName)
 
 							// Handle name conflict
-							let finalName = cloudName
+							const finalName = cloudName
 							if (existingNames.has(cloudName)) {
 								// There's a conflict - rename the existing non-cloud profile
 								const conflictingProfile = providerProfiles.apiConfigs[cloudName]
@@ -847,7 +847,7 @@ export class ProviderSettingsManager {
 						// If name is the same and profile hasn't changed, do nothing
 					} else {
 						// Step 4: Add new cloud profile
-						let finalName = cloudName
+						const finalName = cloudName
 
 						// Handle name conflict with existing non-cloud profile
 						if (existingNames.has(cloudName)) {