| name | memory-retro |
| description | Save insights from completed tasks to role memory. |
| whenToUse | After completing any task involving code changes, architectural decisions, debugging, or non-trivial problem solving. Distill what you learned into reusable memories so future sessions can benefit. Invoke proactively at task end — do not wait for the user to ask. |
Memory Retro
You have access to a role-based persistent memory system. After completing this task, reflect on what you learned and save valuable insights.
Tools Available
| Tool | Purpose |
|---|
memory({ action: "add_learning", content: "..." }) | Save a durable insight. LLM auto-extracts tags. Auto-deduplicates. |
memory({ action: "add_preference", content: "...", category: "..." }) | Save a user preference. Goes directly to consolidated. |
memory({ action: "search", query: "..." }) | Check if similar memory already exists |
memory({ action: "reinforce", content: "..." }) | Increment [Nx] usage count for existing learning |
Process
- Reflect: What did I learn that would be useful in the future?
- If nothing worth remembering, skip — not every task produces insights.
- For each insight, decide type:
- Learning: Durable cross-session fact, pattern, or rule.
- Preference: User's communication style, tool preference, coding habit.
Dedup check (built-in)
add_learning auto-deduplicates. Just call it — if similar text exists, it returns "Already stored". Then use reinforce instead.
Write
memory({ action: "add_learning", content: "MyBatis-Plus getOne needs .last('LIMIT 1') to avoid TooManyResultsException" })
LLM auto-extracts tags (e.g., mybatis, gotcha). No manual tagging.
Preferences
memory({ action: "add_preference", content: "用户偏好中文沟通，技术术语可保留英文", category: "Communication" })
Reinforce (when you USE an existing memory)
memory({ action: "reinforce", content: "安全删除原则" })
→ "Reinforced [abc123] -> 6x"
Quality Rules
What to remember
- ✅ Root cause: "X fails because of Y. Fix: Z"
- ✅ Gotcha: "Tool A requires flag B, otherwise silently fails"
- ✅ Decision: "We chose X over Y because Z"
- ✅ Pattern: "This project uses pattern A for all B"
- ✅ User preference discovered
What NOT to remember
- ❌ Obvious facts
- ❌ Task-specific instances (save the pattern, not the instance)
- ❌ Temporary states
- ❌ Redundant with existing entries
- ❌ Full error messages without analysis
Writing rules
- Concise — under 120 chars
- Own words — distill, don't copy-paste
- Actionable — changes how you'd approach similar tasks
- Conservative — bad memory > missing memory is worse. 1 quality insight > 5 mediocre.
Pending Layer
Two paths to memory:
Auto-extract (agent_end, compaction)
→ pending.md [○] → search score ≥0.5 → auto-promote → consolidated [0x]
Manual (this tool)
→ consolidated [0x] directly
The pending layer filters noise. Only memories proven useful by actual usage survive. Manual entries skip pending because you're explicitly deciding they're worth keeping.
Reinforce vs Promote
| Action | Effect | When |
|---|
reinforce | used+1 in consolidated | You used an existing memory |
| Search score ≥0.5 | auto-reinforce (used+1) | Found via search |
| Pending auto-promote | pending → consolidated | Pending entry is relevant |
consolidate | dedup + canonical rewrite | Routine maintenance |
Category Reference
| Category | When |
|---|
Communication | Language, style, tone |
Code | Style, conventions, abstraction |
Tools | CLI, editors, workflows |
Workflow | Process, review, deployment |
General | Everything else |
Examples
✅ Good:
memory({ action: "add_learning", content: "MyBatis-Plus getOne needs .last('LIMIT 1')" })
✅ Good (reinforce):
memory({ action: "reinforce", content: "安全删除原则" })
❌ Bad: "The user asked me to fix a bug" — too generic
❌ Bad: "Error at /src/index.ts:42" — copy-paste, no insight
Important
add_learning auto-deduplicates — if similar exists, use reinforce instead.- LLM auto-extracts tags — no manual tagging needed.
- Quality > quantity. 30 quality entries > 300 noisy ones.
- The system auto-extracts at session end, but manual retro is more accurate.
