claude-skills/engineering/memory-engineering

memory-engineering

Your agent's problem was never that it forgets. It's that it never forgets on purpose.

A storer optimizes what a system remembers. A memory engineer optimizes what it forgets. This plugin makes that shift executable: four deterministic stdlib scripts that price the write path, choose which cost to pay, audit what a store actually holds, and refuse a design with no forgetting policy.

Why this exists

Everyone building agent memory optimizes retrieval. Almost nobody engineers what it costs to build, what is worth keeping, who can delete it, and where it lands on the hardware. Stanford's systems characterization of ten memory systems found the gap concretely:

• Construction energy exceeds total query-phase energy across 300 queries — the bill is paid on the write path you never watch.
• Energy per correct answer spreads more than 47× across systems (BM25 at 4,145 J; MIRIX at ~197 kJ).
• At 1M tokens, footprint varies up to 9× — and "none of the evaluated systems prune or forget by default."

If you did not build forgetting, you do not have it.

Install

/plugin marketplace add alirezarezvani/claude-skills
/plugin install memory-engineering

Use

/cs:memory-engineering ~/.claude/memory      # full four-lens pass
/cs:forgetting-audit design.json             # just the blocking gate

Or run the scripts directly — each has --help, --sample, and --output json:

cd skills/memory-engineering

python scripts/memory_cost_profiler.py --sample
python scripts/memory_architecture_picker.py --sample
python scripts/memory_density_auditor.py --dir ~/.claude/memory
python scripts/forgetting_policy_linter.py --sample-failing

The four scripts

Script	Lens	What it does	Exit codes
memory_cost_profiler.py	Stanford — what does it cost?	Splits construction vs query spend, computes cost per correct answer, flags under-amortized writes and construction co-located with live queries	0 · 2 finding · 3 bad input
memory_architecture_picker.py	Stanford — which cost to pay?	Scores long-context / flat RAG / structure-augmented RAG / agentic against constraints, disqualifies on hard limits, names the cost you're choosing, refuses to pick on a tie	0 · 2 ambiguous · 3 bad input · 4 none viable
memory_density_auditor.py	Microsoft — what's worth keeping?	Classifies records FACT / SKILL / LOG / PROSE, finds near-duplicates, flags stale and time-relative wording, scores knowledge density. Runs on a real directory or JSONL	0 dense · 2 finding · 3 bad input
forgetting_policy_linter.py	Anthropic + the gate	8 checks; F1 (explicit forgetting rule) and F4 (contradictions surfaced, never auto-merged) are blocking	0 PASS · 2 CONDITIONAL · 4 FAIL

Stdlib only. No network, no LLM calls, no dependencies.

The gate

$ python scripts/forgetting_policy_linter.py --sample-failing

VERDICT: FAIL  (0/8 checks pass)
This design does not forget on purpose. F1 failed. F4 failed.

  FAIL  F1  explicit forgetting rule [BLOCKING]
        No TTL, no capacity bound, no decay. The store only grows.
  FAIL  F4  contradictions surfaced, never auto-merged [BLOCKING]
        Contradiction policy is 'newest_wins', which resolves conflicts silently.

F4 is blocking on purpose. Two memories that disagree may both have been true in different contexts — "deploys go through Jenkins" and "deploys go through GitHub Actions" is not a contradiction to resolve, it is a migration to record. Auto-merging destroys the only evidence the conflict existed.

Evidence discipline

The four-lens framing synthesizes "How to be a Memory Engineer, from the perspective of Stanford, Microsoft, Anthropic and Nvidia" by @N01ennn.

Every quantitative claim is cited to the primary source, not to that article, and each carries an explicit confidence level. Two of the article's paraphrases are corrected in the references:

• The 47× energy figure is the spread across ten evaluated systems, not "two systems with identical accuracy" (memory_cost_canon.md §2).
• The 97% first-pass-error reduction is Rakuten's named, vendor-published customer testimonial — not a controlled study or a general property of building memory this way (memory_control_and_governance.md §4).

Not this plugin

You want	Use
Build and maintain one markdown knowledge vault	llm-wiki
A nightly self-improvement loop over transcripts	skillopt-sleep
Bound an agent's task loop	agent-harness
Price inference generally	llm-cost-optimizer

This bounds a store, not a loop and not a vault.

Primary sources

• Omri, Y. et al. — Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads, arXiv:2606.06448
• Microsoft Research — PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
• Kontonis, V. et al. — MEMENTO: Teaching LLMs to Manage Their Own Context, arXiv:2604.09852
• Anthropic — Built-in memory for Claude Managed Agents

Full citation lists (7 sources each) are in skills/memory-engineering/references/.

License

MIT.