Individual skill
memory-engineering
Use when designing, reviewing, or paying for an agent memory system — adding memory to an agent, choosing between long-context / RAG / graph / agentic memory, auditing what a CLAUDE.md or memory directory actually holds, deciding what to keep and what to expire, or when a memory store keeps growing and nobody has said what leaves it. Prices the write path, picks which cost to pay, classifies records as facts / skills / logs, and refuses a design that has no forgetting policy.
skills-imported/memory-engineering/SKILL.md
Source commit faf37c0859ebf627bfcb5225e8057d088691e0ae
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- Oct 10, 2026
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- MIT
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SKILL.mdcore · 6,743 bytesSKILL.mdcore · 6,743 bytesSKILL.mdcore · 6,743 bytesreferences/forgetting_policy_design.mddoc · 8,877 bytesreferences/memory_control_and_governance.mddoc · 7,544 bytesreferences/memory_cost_canon.mddoc · 7,030 bytesreferences/what_to_keep.mddoc · 7,786 bytesscripts/forgetting_policy_linter.pyruns · 15,509 bytesscripts/memory_architecture_picker.pyruns · 15,780 bytesscripts/memory_cost_profiler.pyruns · 13,675 bytesscripts/memory_density_auditor.pyruns · 26,557 bytes
Read excerpt
# Memory Engineering — engineer the forgetting, not just the remembering
> **Portability:** 4 stdlib scripts, no APIs/LLM calls/network. They measure and gate; you decide.
## What this does
Anyone can give an agent memory: vector store, pipe in the history, retrieve
top-k. That works until the history outgrows the context window, the write path
costs more than every query it serves, and the store fills with stale state
nobody removes. Memory is not a bucket — it is a system with a metabolism.
**The shift:** a storer optimizes what a system remembers; a memory engineer
optimizes what it forgets. The problem was never that an agent forgets — it is
that it never forgets *on purpose*.