Coordinate and track AI agent tasks across context windows.
npx skills add https://github.com/rigerc/repo-2 --skill td-task-management-rigerc
Please help me install this Agent Skill. Skill: td-task-management Source: https://github.com/rigerc/repo-2/tree/main/.agents/skills/td-task-management Command: npx skills add https://github.com/rigerc/repo-2 --skill td-task-management-rigerc
Task management for AI agents across context windows, tracking progress and preserving state between sessions to prevent context loss.
Run td usage --new-session to auto-rotate sessions and view the current state.
High-intent search queries and answers about installing and using this skill.
To preserve AI agent context across sessions, you can use structured task handoffs with done, remaining, decision, and uncertain fields. This maintains clear state and progress tracking via local serverless storage to prevent context loss between windows.
Managing multi-issue work sessions involves grouping related issues together with shared logging. This approach tracks progress across grouped tasks while maintaining session isolation to prevent context overlap during complex AI agent workflows.
Session isolation for AI agent task tracking works by automatically rotating sessions to keep contexts separated. Running a session rotation command auto-rotates the active session and displays the current state, preventing cross-context interference.
Yes, local serverless storage is used for persistent context in AI workflows. It enables deterministic logging and structured handoffs, ensuring task state and progress are preserved locally without requiring external server infrastructure.
To start a single-issue focused workflow for AI task handoffs, initiate a new session to auto-rotate and view current state. This allows you to begin logging, handing off, and reviewing tasks with structured context fields.
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