{"product_id":"optimize-agent-memory-with-ai-master-using-todo-in-lwc","title":"Optimize Agent Memory with AI: Master Using-Todo in LWC","description":"\u003ch3\u003eOptimize Agent Memory with AI: Master Using-Todo in LWC\u003c\/h3\u003e\n\n\u003cp\u003eThis skill enables AI agents using Claude Code, Cursor, or Codex to efficiently capture, manage, and track deferred work through the Using-Todo system in LWC. It facilitates operations such as adding, finding, updating, finishing, canceling, or reopening tasks, allowing agents to handle work items that are independent from the current execution plan.\u003c\/p\u003e\n\n\u003ch3\u003eWhat this skill does\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eVerify configuration by ensuring `todo.setting` is enabled using `lwc config show`. Enable Todo with `lwc config set --todo enabled` if necessary.\u003c\/li\u003e\n  \u003cli\u003eAdd tasks with `lwc todo add TITLE --tag TAG --cue TEXT --target-at RFC3339 --request-id ID`, including organizing tasks with parent-child relationships through `--parent TODO_ID`.\u003c\/li\u003e\n  \u003cli\u003eAccess a list of tasks either generally or within parent categories using queries like `lwc todo list` or `lwc todo search QUERY --limit 20`.\u003c\/li\u003e\n  \u003cli\u003eInspect tasks prior to any update operations with `lwc todo show TODO_ID`. Ensure tasks are reviewed to avoid conflicts by using `--if-revision` when applying changes.\u003c\/li\u003e\n  \u003cli\u003eReschedule tasks using `todo update ... --target-at RFC3339`, or clear scheduled times with `--clear-target-at`.\u003c\/li\u003e\n  \u003cli\u003eConclude tasks with a defined result or reason, and reopen tasks as needed before applying modifications.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eWho it is for\u003c\/h3\u003e\n\n\u003cp\u003eThis skill is designed for developers and teams utilizing AI coding agents such as Claude Code, Cursor, or Codex, who require a structured approach for managing future-oriented or deferred work tasks.\u003c\/p\u003e\n\n\u003ch3\u003eUse cases\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eOrganizing and prioritizing long-term development tasks without altering the current project workflow.\u003c\/li\u003e\n  \u003cli\u003eHandling delayed action requests, enabling teams to revisit items when the associated time or conditions are met.\u003c\/li\u003e\n  \u003cli\u003eSeparating tasks that require specific scheduling from ongoing execution plans.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eTechnical details\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eUtilizes the agent-memory-system for improved task management.\u003c\/li\u003e\n  \u003cli\u003eIntegrates with tools using the `aiapplication` and `using-todo` capabilities within LWC.\u003c\/li\u003e\n  \u003cli\u003eRequires manual task organization and management through clearly defined command structures.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c!-- mcpcart:static-blocks:start --\u003e\n\u003chr\u003e\n\u003ch3\u003eSource \u0026amp; Licence\u003c\/h3\u003e\n\u003cp\u003eThis package is built on open-source work published by \u003cstrong\u003eJanYork\u003c\/strong\u003e (\u003ca href=\"https:\/\/github.com\/JanYork\/llm-wiki-cli\" rel=\"nofollow noopener\" target=\"_blank\"\u003eJanYork\/llm-wiki-cli\u003c\/a\u003e) and distributed under \u003cstrong\u003eApache-2.0\u003c\/strong\u003e. The original licence text and copyright notice are included in your download.\u003c\/p\u003e\n\u003cp\u003ePersonal and commercial use, modification and redistribution are permitted under the Apache License 2.0, which also includes an express patent grant. Attribution and any NOTICE file must be retained.\u003c\/p\u003e\n\u003cp\u003eYour purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.\u003c\/p\u003e\n\u003ch3\u003eDelivery \u0026amp; Support\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eDelivery:\u003c\/strong\u003e instant — a secure download link is emailed to you as soon as payment is confirmed.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFormat:\u003c\/strong\u003e ZIP archive containing the skill files, documentation and the original licence.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSupport:\u003c\/strong\u003e \u003ca href=\"mailto:support@mcpcart.com\"\u003esupport@mcpcart.com\u003c\/a\u003e — we aim to reply within 2 business days.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUpdates:\u003c\/strong\u003e updates are included only where stated on this page.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3\u003eRefunds\u003c\/h3\u003e\n\u003cp\u003eThis is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.\u003c\/p\u003e\n\u003cp style=\"font-size:0.85em;color:#666;\"\u003eClaude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.\u003c\/p\u003e\n\u003c!-- mcpcart:static-blocks:end --\u003e","brand":"MCP Cart","offers":[{"title":"Default Title","offer_id":52881034576183,"sku":"MCP-JANYORK-LLM-WIKI-CLI-USING-TODO","price":30.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0981\/3950\/4951\/files\/LMnO2YTg3kiRWDZCkcV7R_3873227e25dd4244ae65ac5493d05df8.jpg?v=1787400127","url":"https:\/\/mcpcart.com\/products\/optimize-agent-memory-with-ai-master-using-todo-in-lwc","provider":"SPF PRO","version":"1.0","type":"link"}