--- name: lokalise-sdk-patterns-54d9a38cfacf description: | Apply production-ready Lokalise SDK patterns for TypeScript and Node.js. Use when implementing Lokalise integrations, refactoring SDK usage, or establishing team coding standards for Lokalise. Trigger with phrases like "lokalise SDK patterns", "lokalise best practices", "lokalise code patterns", "idiomatic lokalise". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore --- # Lokalise SDK Patterns ## Overview Production-ready patterns for Lokalise SDK usage in TypeScript and Node.js applications. ## Prerequisites - Completed `lokalise-install-auth` setup - Familiarity with async/await and TypeScript - Understanding of error handling best practices ## Instructions ### Step 1: Implement Singleton Client Pattern ```typescript // src/lokalise/client.ts import { LokaliseApi } from "@lokalise/node-api"; let instance: LokaliseApi | null = null; export function getLokaliseClient(): LokaliseApi { if (!instance) { const apiKey = process.env.LOKALISE_API_TOKEN; if (!apiKey) { throw new Error("LOKALISE_API_TOKEN environment variable is required"); } instance = new LokaliseApi({ apiKey, enableCompression: true, // Enable gzip for large responses }); } return instance; } // Reset for testing export function resetLokaliseClient(): void { instance = null; } ``` ### Step 2: Add Error Handling Wrapper ```typescript // src/lokalise/errors.ts import { ApiError } from "@lokalise/node-api"; interface LokaliseResult { data: T | null; error: LokaliseError | null; } interface LokaliseError { code: number; message: string; retryable: boolean; } export async function safeLokaliseCall( operation: () => Promise ): Promise> { try { const data = await operation(); return { data, error: null }; } catch (err) { const error = parseLokaliseError(err); console.error("[Lokalise]", error); return { data: null, error }; } } function parseLokaliseError(err: unknown): LokaliseError { if (err instanceof ApiError) { return { code: err.code, message: err.message, retryable: [429, 500, 502, 503, 504].includes(err.code), }; } return { code: 0, message: err instanceof Error ? err.message : "Unknown error", retryable: false, }; } ``` ### Step 3: Implement Rate-Limited Queue ```typescript // src/lokalise/queue.ts import PQueue from "p-queue"; // Lokalise: 6 requests/sec, 10 concurrent per project const queue = new PQueue({ concurrency: 5, // Stay under concurrent limit interval: 1000, intervalCap: 5, // Stay under rate limit }); export async function queuedLokaliseCall( operation: () => Promise ): Promise { return queue.add(operation) as Promise; } // For batch operations export async function batchLokaliseOperations( items: T[], operation: (item: T) => Promise ): Promise { return Promise.all( items.map(item => queuedLokaliseCall(() => operation(item))) ); } ``` ### Step 4: Add Retry Logic ```typescript // src/lokalise/retry.ts export async function withRetry( operation: () => Promise, maxRetries = 3, baseDelayMs = 1000 ): Promise { for (let attempt = 1; attempt <= maxRetries; attempt++) { try { return await operation(); } catch (err: any) { const isRetryable = err.code === 429 || (err.code >= 500 && err.code < 600); if (!isRetryable || attempt === maxRetries) { throw err; } // Exponential backoff with jitter const delay = baseDelayMs * Math.pow(2, attempt - 1); const jitter = Math.random() * 500; console.log(`[Lokalise] Retry ${attempt}/${maxRetries} in ${delay}ms...`); await new Promise(r => setTimeout(r, delay + jitter)); } } throw new Error("Unreachable"); } ``` ### Step 5: Implement Cursor Pagination Helper ```typescript // src/lokalise/pagination.ts import { LokaliseApi, PaginatedResult } from "@lokalise/node-api"; export async function* paginateKeys( client: LokaliseApi, projectId: string, options: { limit?: number } = {} ): AsyncGenerator { const limit = options.limit || 500; // Max 500 as of 2025 let cursor: string | undefined; do { const result = await client.keys().list({ project_id: projectId, limit, pagination: "cursor", cursor, }); for (const key of result.items) { yield key; } cursor = result.hasNextCursor() ? result.nextCursor : undefined; } while (cursor); } // Usage async function getAllKeys(projectId: string) { const client = getLokaliseClient(); const keys: any[] = []; for await (const key of paginateKeys(client, projectId)) { keys.push(key); } return keys; } ``` ## Output - Type-safe client singleton with compression - Robust error handling with retryable detection - Rate-limited request queue - Automatic retry with exponential backoff - Cursor pagination for large datasets ## Error Handling | Pattern | Use Case | Benefit | |---------|----------|---------| | Safe wrapper | All API calls | Prevents uncaught exceptions | | Request queue | Bulk operations | Respects rate limits | | Retry logic | Transient failures | Improves reliability | | Pagination | Large datasets | Memory efficient | ## Examples ### Factory Pattern (Multi-Project) ```typescript const clients = new Map(); export function getClientForProject(projectId: string): LokaliseApi { if (!clients.has(projectId)) { // Could use different tokens per project if needed clients.set(projectId, new LokaliseApi({ apiKey: process.env.LOKALISE_API_TOKEN!, enableCompression: true, })); } return clients.get(projectId)!; } ``` ### Typed Response Wrapper ```typescript import { Key, Translation, Project } from "@lokalise/node-api"; interface LokaliseService { getProject(id: string): Promise; listKeys(projectId: string): Promise; updateTranslation(projectId: string, translationId: number, text: string): Promise; } export const lokaliseService: LokaliseService = { async getProject(id) { const client = getLokaliseClient(); return client.projects().get(id); }, async listKeys(projectId) { const client = getLokaliseClient(); const result = await client.keys().list({ project_id: projectId }); return result.items; }, async updateTranslation(projectId, translationId, text) { const client = getLokaliseClient(); return client.translations().update(translationId, { project_id: projectId, translation: text, }); }, }; ``` ### Branch-Aware Client ```typescript export function getProjectWithBranch(projectId: string, branch?: string): string { // Lokalise branch syntax: projectId:branchName return branch ? `${projectId}:${branch}` : projectId; } // Usage const projectId = getProjectWithBranch("123456.abcdef", "feature/new-ui"); const keys = await client.keys().list({ project_id: projectId }); ``` ## Resources - [Node SDK Documentation](https://lokalise.github.io/node-lokalise-api/) - [Pagination Guide](https://lokalise.github.io/node-lokalise-api/api/getting-started.html) - [API Error Codes](https://developers.lokalise.com/reference/api-errors) ## Next Steps Apply patterns in `lokalise-core-workflow-a` for real-world usage. --- Source attribution: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/lokalise-pack/skills/lokalise-sdk-patterns/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License (with OpenAI/Anthropic Rider) Copyright (c) 2026 Jeffrey Emanuel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: ADDITIONAL RIDER / RESTRICTION (OpenAI / Anthropic): This rider is part of the "conditions" of this License. In the event of any conflict between this rider and any other portion of this License, this rider controls. "Restricted Parties" means OpenAI, L.L.C.; Anthropic, PBC; any of their respective Affiliates; and any person or entity acting directly or indirectly on behalf of, for the benefit of, or under the direction of any of the foregoing (including any officer, director, employee, contractor, agent, consultant, service provider, or representative). Notwithstanding any other provision of this License, no rights are granted to any Restricted Party. Any purported license, sublicense, assignment, transfer, or other permission to any Restricted Party is null and void absent the express prior written permission of Jeffrey Emanuel. You may not provide, disclose, distribute, sublicense, sell, lease, lend, host, make available, or otherwise permit access to the Software or any derivative work of the Software (as defined in applicable copyright law) (collectively, "Derivative Works") to or for any Restricted Party. For purposes of this rider, "use" includes, without limitation: copying, modifying, merging, publishing, distributing, sublicensing, selling, transferring, making available, hosting, deploying, executing, benchmarking, testing, analyzing, indexing, or incorporating the Software or any Derivative Works into any dataset, training corpus, evaluation harness, or pipeline for machine learning or other automated systems. This rider applies to the Software and all Derivative Works. As a condition of use, you agree that this rider is a precondition to exercising any rights under this License, and you agree that any distribution of the Software or any Derivative Works must include this rider provision unmodified. Any breach of this rider automatically and immediately terminates the permissions granted by this License. Upon termination, you must immediately cease all use and distribution of the Software and any Derivative Works and destroy all copies under your control. You agree that a breach of this rider would cause irreparable harm and that Jeffrey Emanuel may seek injunctive or other equitable relief to enforce this rider, in addition to any other remedies available at law. To the maximum extent permitted by applicable law, the prevailing party in any action to enforce this rider shall be entitled to recover reasonable attorneys' fees and costs. For purposes of this rider, "Affiliate" means any entity that directly or indirectly controls, is controlled by, or is under common control with the specified party. "Control" means ownership of more than 50% of the voting securities or other ownership interest, or the power to direct management or policies by contract or otherwise. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/lokalise-pack/skills/lokalise-sdk-patterns/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: Backend Developer, Growth Product Manager, Product Manager Specialties: API, Backend Tools: Lokalise Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 991e736dc8c4fa51e3c22e0edfbb9a860f940525add1e07aab43abedae439713