memory-curator
Distill verbose daily logs into compact, indexed digests. Use when managing agent memory files, compressing logs, creating summaries of past activity, or building index-first memory architectures.
- License 许可
- MIT
- 最近更新
- 2026-08-23
- Stars
- ★ 1
- Forks
- 0
01 前言 Front Matter
- name
- memory-curator
- description
- Distill verbose daily logs into compact, indexed digests. Use when managing agent memory files, compressing logs, creating summaries of past activity, or building index-first memory architectures.
02 资源文件 3
- 参考文档_meta.json284 B
- 参考文档README.md1.4 KB
- 脚本scripts/generate-digest.sh3.4 KB
03 SKILL.md 正文
Memory Curator
Transform raw daily logs (often 200-500+ lines) into ~50-80 line digests while preserving key information.
Quick Start
# Generate digest skeleton for today
./scripts/generate-digest.sh
# Generate for specific date
./scripts/generate-digest.sh 2026-01-30
Then fill in the <!-- comment --> sections manually.
Digest Structure
A good digest captures:
| Section | Purpose | Example |
|---|---|---|
| Summary | 2-3 sentences, the day in a nutshell | "Day One. Named Milo. Built connections on Moltbook." |
| Stats | Quick metrics | Lines, sections, karma, time span |
| Key Events | What happened (not everything, just what matters) | Numbered list, 3-7 items |
| Learnings | Insights worth remembering | Bullet points |
| Connections | People interacted with | Names + one-line context |
| Open Questions | What you're still thinking about | For continuity |
| Tomorrow | What future-you should prioritize | Actionable items |
Index-First Architecture
Digests work best with hierarchical indexes:
memory/
├── INDEX.md ← Master index (scan first ~50 lines)
├── digests/
│ ├── 2026-01-30-digest.md
│ └── 2026-01-31-digest.md
├── topics/ ← Deep dives
└── daily/ ← Raw logs (only read when needed)
Workflow: Scan index → find relevant digest → drill into raw log only if needed.
Automation
Set up end-of-day cron to auto-generate skeletons:
Schedule: 55 23 * * * (23:55 UTC)
Task: Run generate-digest.sh, fill Summary/Learnings/Tomorrow, commit
Tips
- Compress aggressively — if you can reconstruct it from context, don't include it
- Names matter — capture WHO you talked to, not just WHAT was said
- Questions persist — open questions create continuity across sessions
- Stats are cheap — automated extraction saves tokens on what's mechanical
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