memory-retro

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

1. Reflect: What did I learn that would be useful in the future?
2. If nothing worth remembering, skip — not every task produces insights.
3. 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.