memory-distill

name	memory-distill
description	Learn from user feedback to improve future content. Activate when user approves, rejects, edits, or gives feedback on topics or content drafts. Also runs periodically to consolidate learnings.

Memory Distill

Utility skill. Captures user preferences and writing feedback into persistent memory.

Memory Architecture

• L1 — ~/.autocrew/MEMORY.md (≤120 lines): Working memory. Brand profile, audience, account status, writing preferences summary.
• L2 — ~/.autocrew/memory/: Detailed logs and digests. Referenced on demand.

When to Activate

• User approves or rejects a topic
• User edits a draft significantly
• User gives explicit feedback ("太正式了", "多用emoji", "短一点")
• User shares performance data ("这条笔记1000赞")

Steps

Feedback Capture

1. Identify the feedback signal:

   • Approval: user says "好" / "可以" / "就这个" → positive signal
   • Rejection: user says "不行" / "太..." / "换一个" → negative signal
   • Edit: user provides a rewrite → compare original vs edit
   • Performance: user shares metrics → high/low performance signal

2. Analyze what the user liked or disliked. Be specific:

   • Bad: "user likes emoji"
   • Good: "user prefers 3-5 emoji per XHS post, placed at paragraph starts, favorites: 🔥 💡 ✨"

3. Formulate a learning as a concise preference statement.

4. Read ~/.autocrew/MEMORY.md. Append the learning to the appropriate section:

Prefer using autocrew_memory:

{
  "action": "capture_feedback",
  "content_id": "content-xxx",
  "signal_type": "edit",
  "feedback": "太正式了",
  "modified_text": "用户改后的版本"
}

## Writing Preferences
- [Date] XHS posts: user prefers casual tone, 3-5 emoji, short paragraphs
- [Date] Titles: user likes curiosity-gap style, dislikes clickbait

## Content Edit Preferences
- [Date] Shortened opening from 3 sentences to 1 — user prefers immediate hooks
- [Date] Replaced formal "综上所述" with casual "所以说" — user wants conversational tone

## Performance Insights
- [Date] "AI工具真香清单" got 1000 likes on XHS — list format + "真香" hook works

Capacity Check

If MEMORY.md exceeds 150 lines:

1. Identify entries that can be consolidated (similar learnings → one summary)
2. Move detailed case studies to ~/.autocrew/memory/archive-{date}.md
3. Replace with a one-line summary in MEMORY.md

Guidelines

• Be specific — actionable preferences, not vague observations
• Don't overwrite previous learnings, accumulate them
• Date every entry for tracking evolution
• Consolidate when file gets long, don't let it grow unbounded

Error Handling

Failure	Action
MEMORY.md doesn't exist	Create it with initial structure
File write fails	Log error, continue without saving

Changelog

• 2026-03-31: v1 — Adapted from Qingmo memory-distill.md v3. Simplified to two-layer architecture (no LanceDB dependency). File-based storage only.