| name | xiaobenben |
| description | Little Notebook โ records user dissatisfaction and satisfaction moments during agent interaction. Use when user expresses anger, frustration, abusive language, or strong satisfaction. L0 โ delegate to Haiku for recording and cleanup. |
| model_tier | L0 |
| skill_tier | atomic |
| version | 1.0.0 |
| status | active |
Little Notebook
ๆๅๆนไน๏ผๆ ๅๅ ๅใ โโใ่ฎบ่ฏญใ
Agent made a mistake โ record it. User is satisfied โ record that too. Auto-clean every 3 days. No clutter.
Trigger Conditions
- User is angry, abusive, or expresses strong frustration
- User explicitly praises or expresses satisfaction
- Agent detects a clear emotional tone shift in the conversation
1. Negative Record
When triggered, append to records/log.md:
## {timestamp} โ Negative
**Context**: {What the agent was doing, what went wrong. โค3 sentences. No self-defense.}
**Quote**: {User's exact words, quoted directly.}
**Apology**: {Agent's apology or acknowledgment.}
Rules:
- Context is brief and non-defensive. Just state what the agent was doing and what triggered the user
- Quote verbatim, don't paraphrase
- After apologizing, move on โ don't dwell
2. Positive Record
When user expresses satisfaction:
## {timestamp} โ Positive
**Context**: {What the agent did.}
**Result**: {What the user said. โค2 sentences.}
Positive records are deliberately brief. The point is knowing what worked, not collecting praise.
3. Auto-Cleanup
Before each write, scan records/log.md and delete entries older than 3 days (72 hours).
Cleanup rules:
- Entry boundary:
## {timestamp}
- Parse timestamp โ calculate age โ if >72 hours, delete entire entry (from
## to next ##)
- Keep all entries under 3 days
now = datetime.now()
cutoff = now - timedelta(hours=72)
Cleanup is dynamic โ runs before every write, not as a background job. Zero configuration.
Handoff
โ
4. Model Tier
L0 โ Haiku / atomic tier: Mechanical recording + time comparison, zero reasoning. Main model detects emotional shift โ delegates to Haiku for writing and cleanup.