Archive an experiment line while preserving lessons, artifacts, and memory.
npx skills add https://github.com/LinkDry/Link-Research --skill archive-linkdry
Please help me install this Agent Skill. Skill: archive Source: https://github.com/LinkDry/Link-Research/tree/main/skills/archive Command: npx skills add https://github.com/LinkDry/Link-Research --skill archive-linkdry
Archive closes an experiment line without losing what it taught us, enabling safe preservation of evidence, artifacts, and reusable lessons for future reference and recovery.
Archive the current active line using the standard archive protocol.
High-intent search queries and answers about installing and using this skill.
To preserve experiment lessons without deleting active project state, you can archive the experiment line. This snapshots workspace artifacts and appends an archive record while keeping the active project state fully recoverable.
Closing an experiment line while promoting lessons involves archiving case details locally and moving reusable lessons into global memory. This ensures evidence preservation and future reference without losing project context.
You should use an archive protocol for experiment preservation when a judge verdict or explicit human request requires safe closure. It applies when you need to retain evidence and learning for future recovery without disrupting active workflows.
Yes, archiving an experiment coordinates directly with project-brief, STATE, and memory files. It follows a defined contract to append archive records accurately, ensuring workspace artifacts are safely snapshotted alongside existing project data.
Yes, you can recover an active project state after preservation because the process uses a move-or-snapshot oriented closure. This approach explicitly preserves evidence and lessons while leaving the active project state recoverable for future reuse.
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