Capture user feedback and distill it into durable memory files.
npx skills add https://github.com/alextangson/AutoCrew --skill memory-distill-alextangson
Please help me install this Agent Skill. Skill: memory-distill Source: https://github.com/alextangson/AutoCrew/tree/main/skills/memory-distill Command: npx skills add https://github.com/alextangson/AutoCrew --skill memory-distill-alextangson
This memory-distill skill captures user feedback (approvals, rejections, edits, or performance signals) and distills it into a durable memory store to guide future content creation.
Enable memory distillation by activating the autocrew workflow to capture feedback and consolidate learnings into durable memory.
High-intent search queries and answers about installing and using this skill.
Memory distillation captures feedback into durable memory by applying a two-layer architecture and structured logging workflow to persist learnings, storing brand profiles and writing preferences in working memory while archiving detailed digests locally.
Saving writing preferences from edits and rejections is best handled by synthesizing feedback into explicit, reusable writing rules, periodically consolidating these learnings to adapt to evolving audience preferences over time.
Two-layer memory architecture works by maintaining an L1 working memory file for active brand profiles and audience preferences, while an L2 directory archives detailed digests and logs for on-demand reference and periodic distillation.
Memory distillation requires no external dependencies to persist learning logs, utilizing a structured workflow to write and consolidate feedback directly into local memory files within the designated configuration directory.
You can use periodic distillation to adapt content to evolving preferences by consolidating archived feedback logs over time, converting historical performance signals and edit patterns into updated, durable writing rules.
Writing preference memory fails to capture rejected content feedback when the trigger-based learning workflow is not activated, preventing the structured logging required to parse rejections into durable rules.
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boringdataExtract audience persona profiles from content and save them to rules/personas/.
gauravprwl14Convert raw .md or .txt notes into standardized source.md files with generated metadata.
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