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Memory Engineering Skill — Give Your Agent a Memory You Can Trust

Everyone is posting the same diagram: agents need working, episodic, semantic, and procedural memory. The diagram is right — and it is a map, not a house. This skill is the house: an installable set of operating rules that decides what your agent writes down, where it goes, and when it gets deleted, plus two commands (bootstrap and audit) that apply those rules to your actual setup in minutes.

What you get after installing

Concretely, three things change:

  1. "Remember this" stops being a lie. When you tell your agent to remember something, it now runs an admission test (would forgetting this cause a mistake?), routes it to the right layer, records the why, and tells you what it wrote and where — instead of pasting another line onto a 300-line instruction file.
  2. You can ask for a memory audit and get a concrete report: duplicated facts, contradictions, zombie entries nothing has used in months, task-specific bulk taxing every session.
  3. Your always-loaded context gets smaller, not bigger, over time — because deletion is half the system, not an afterthought.

The mental model: the popular terms, mapped to real files

The term you saw on X What it means Where this skill puts it
Working memory What matters right now The session itself — deliberately not persisted
Episodic memory What happened, when An append-only journal (a few lines a day)
Semantic memory Facts, decisions, preferences One fact per file + a one-line-per-fact index; every fact lives in exactly one place
Procedural memory How to do things Instruction files, layered: always-loaded rules kept brutal, task-specific procedure demoted to skills

Three verbs run the whole system:

  • WRITE — the admission test: "if this line were gone, would the agent make a mistake?" No → don't write it. Yes → write it with its why.
  • ROUTE — the decision tree above: session / journal / fact file / instruction layer.
  • PRUNE — wrong → delete now; done → archive; unused for two months → challenge it. Index capped so recall stays sharp.

Before / After (the honest picture)

Before — one 312-line CLAUDE.md that absorbed everything for six months: a critical "never push without asking" buried between a stale meeting note and a fixed-in-May bug; two contradictory Postgres decisions; 40 lines of deploy steps taxing every session including the ones that never deploy.

After — the same knowledge, layered:

project/
├── CLAUDE.md                        28 lines: always-true rules + pointers
├── memory/
│   ├── MEMORY.md                    index — one line per fact
│   ├── never-push-without-asking.md (feedback, carries its why)
│   ├── postgres-14-freeze.md        (the contradiction, resolved to ONE fact)
│   └── staging-dashboard.md         (reference)
├── .claude/skills/deploy/SKILL.md   the 40 deploy lines, loaded only when deploying
├── journal.md                       what happened, newest first
└── archive/                         done things, out of the agent's context

And what a single memory looks like — small enough to delete safely, complete enough to trust:

---
name: never-push-without-asking
type: feedback
description: Never git push unless the user explicitly asks.
---
Never push to any remote unless the user explicitly asks in this session.

**Why:** 2026-05-03 an auto-push broke a teammate's deploy.
**How to apply:** commit freely when asked; stop before any `git push`.

Full walkthrough with the failure-mode analysis: examples/before-after.md.

The two commands

bootstrap — you have no memory system yet

Say: "Bootstrap a memory system for me."

The agent interviews you first (it does not dump templates):

  1. What do you use this agent for, mostly?
  2. What do you find yourself re-explaining every session?
  3. What has it gotten wrong that you never want repeated?

Then it creates the minimum structure — an index plus the 3–8 memories your answers actually justified, each with its why — and explains every line it wrote and every candidate it rejected. Subtraction is the point: a memory system that starts small gets used; one that starts as twenty empty template files gets abandoned.

audit — you already have instruction files and they've grown

Say: "Audit my memory." The agent reads your existing instruction files and memory directory, then reports — with file-and-line evidence:

MEMORY AUDIT — 2026-08-13
Duplicates (2): "use vitest" appears in CLAUDE.md:41 and testing.md:3
Contradictions (1): CLAUDE.md:12 says Postgres 15 migration planned;
  CLAUDE.md:97 says frozen on 14. Which is current?
Zombies (5): 5 entries unused since May — challenge or archive list below
Misrouted (3): 40-line deploy procedure in the always-loaded file →
  demote to a skill; 2 meeting notes → journal
Missing why (4): rules that cannot be safely pruned later because no
  one recorded the reason they exist
Proposed actions: 12 (apply none without your approval)

You approve; it applies. Nothing is deleted without you.

Daily use — mostly invisible

You do not invoke this skill day to day. It changes what "remember this" does, and once a week you can say "run the memory sweep" for the 15-minute maintenance pass (journal-to-fact sync, archive done items, contradiction scan, zombie pass).

Installation

git clone https://github.com/danyuchn/memory-engineering-skill ~/.claude/skills/memory-engineering

Works with any agent that reads instruction files and a memory directory; examples use Claude Code conventions (CLAUDE.md, ~/.claude/skills/).

What this skill is not

  • Not a vector database, and not a plug for one. Most personal and team agent setups fail on curation, not retrieval — that is the part this skill fixes, with plain files you can read and diff.
  • Not a note-taking method for humans. Every rule optimizes for what the agent should carry, which is why deletion gets equal billing with writing.

中文簡介

這個 skill 把網路上流行的「智慧代理四種記憶」分類,落地成一套裝了就能用 的記憶作業規則:該記什麼(入場測試:拿掉這條會不會出錯?)、記到哪 (工作/日誌/事實檔/指令分層的路由決策樹)、怎麼忘(錯了立刻刪、完成 歸檔、兩個月沒用到就挑戰它)。裝完之後說「幫我建記憶系統」會得到訪談式 的最小建置;說「健檢我的記憶」會得到一份指出重複、矛盾、殭屍條目的具體 報告。所有規則檔以英文撰寫。

License

MIT — see LICENSE.

Companion skills by the same author: asd-ste100-skill (controlled English for agent-facing text) · iso-24495-skill (plain language for human-facing text, English + Traditional Chinese).

About

Memory engineering for AI agents as an installable skill — what to write, where it goes, when to delete. Bootstrap interview + memory audit. Working/episodic/semantic/procedural mapped to real files.

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