Individual skill
memory-engineering
Use before adding memory to an agent or when a memory store keeps growing (long context vs RAG vs graph vs agentic memory, what to expire). Prices the design with memory_cost_profiler.py and refuses one without a forgetting policy via forgetting_policy_linter.py.
engineering/memory-engineering/skills/memory-engineering/SKILL.md
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SKILL.mdcore · 6,503 bytesSKILL.mdcore · 6,503 bytesSKILL.mdcore · 6,503 bytesassets/forgetting_policy_template.mddoc · 4,511 bytesassets/memory_design_spec.example.jsonasset · 2,098 bytesassets/memory_engineer_worksheet.mddoc · 5,439 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 · 26,561 bytes
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# 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*.