dwsy/memory-retro

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.