lesson

name	lesson
description	Store a lesson learned from the current conversation. Triggered by /lesson command. Use when Master signals that the recent conversation contains a pitfall, fix, or key insight that should be persisted to long-term memory.

Lesson Extraction & Storage

When triggered by /lesson command, extract and store lessons from the recent conversation context following the Dual-Layer Memory Storage rule.

When to Use

• Encountered and fixed a bug/pitfall
• Learned something important about how a system works
• Made a design decision that should be remembered
• User explicitly says "记住这个教训" or similar

Steps

1. Scan Context

Identify from recent messages:

• Symptom: What went wrong / what was observed
• Root Cause: Why it happened
• Solution: How it was fixed
• Prevention: How to avoid in the future

2. Store Technical Layer (fact, importance ≥ 0.8)

Pitfall: [symptom]. Cause: [root cause]. Fix: [solution]. Prevention: [how to avoid].

Example:

Pitfall: AI Daily Digest 脚本调用 DeepSeek API 时无限卡住。Cause: fetch 请求没有设置超时。Fix: 添加 AbortController + setTimeout(60s)。Prevention: 所有外部 API 调用必须设置超时。

3. Store Principle Layer (decision, importance ≥ 0.85)

Decision principle ([tag]): [behavioral rule]. Trigger: [when]. Action: [what to do].

Example:

Decision principle (api-timeout): 所有外部 API 调用必须设置超时。Trigger: 编写任何 fetch 调用外部服务的代码时。Action: 使用 AbortController + setTimeout(30-60s)，并在 catch 中处理 AbortError。

4. Verify Storage

Use memory_recall with anchor keywords to confirm both entries are retrievable:

• Call with keywords from the pitfall (e.g., "timeout fetch API")
• Verify both entries appear in results

5. Report to Master

Brief summary of what was stored:

✅ 已存储今天的教训

📌 **技术层** (fact): [one-line summary]
📌 **原则层** (decision): [one-line summary]

✅ 验证通过，两条记忆均可召回。

Rules

• Keep entries short and atomic (< 500 chars each)
• Use English tags for decision principles (e.g., api-timeout, lancedb-dual-store)
• Include keywords for retrieval (tool names, error messages, concepts)
• If lesson affects a SKILL.md or checklist, update those files too
• If no clear lesson found, ask Master what to store

Example Session

User: /lesson

Agent: 扫描最近的对话，发现以下教训：

**问题**: AI Daily Digest 脚本调用 DeepSeek API 时无限超时
**原因**: callOpenAICompatible 函数没有设置 fetch 超时
**修复**: 添加 AbortController + 60秒超时

正在存储双层记忆...

[stores fact layer]
[stores decision layer]
[verifies with memory_recall]

✅ 已存储今天的教训

📌 **技术层**: API 调用无限超时 → 需设置 AbortController
📌 **原则层**: api-timeout 原则 → 所有 fetch 必须有超时

✅ 验证通过。

Related

• See MEMORY.md section "Dual-Layer Memory Storage" for the iron law
• See MEMORY.md section "Auto-Capture vs Manual Storage" for when to use /lesson vs auto-capture