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Command

/cs-memory-engineering

Price, choose, audit and gate an agent memory system — the full four-lens memory-engineering pass.

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alirezarezvani-claude-skills
26k150 skills116 agents150 commands2 MCP
Install
$ npx -y skills add alirezarezvani/claude-skills --agent claude-code

How it fires

How this command gets triggered: by you, by Claude, or both.

  • Fires itselfClaude auto-loads it when your prompt matches the work.
  • You can call itInvoke it directly when you want it.
  • Slash command/cs-memory-engineering

Context preview

What this command does when you run it.

Price, choose, audit and gate an agent memory system — the full four-lens memory-engineering pass.

Command definition

cs-memory-engineering.md
description: Price, choose, audit and gate an agent memory system — the full four-lens memory-engineering pass.
argument-hint: "[memory dir, design spec JSON, or a question about a memory system]"

/cs:memory-engineering

Run the memory-engineering pass on `$ARGUMENTS`.

Load `engineering/memory-engineering/skills/memory-engineering/SKILL.md` and follow it. Report every script's exit code as a finding — a non-zero exit is a result to surface, never an error to swallow.

Pre-flight

Establish these before running anything. If the user cannot answer 1 or 2, that gap **is** the first finding — say so rather than guessing:

1. **Does a memory system exist yet, or is this a design?** Design → steps 1, 2, 4. Existing store → steps 1, 3, 4. 2. **What leaves the store today?** If the answer is "nothing", skip to step 4; the gate result is the headline. 3. **Is this actually a memory question?** Maintaining one markdown vault → `llm-wiki`. Nightly consolidation loop → `skillopt-sleep`. Bounding a task loop → `agent-harness`.

Pass

**1. Price the write path**

python skills/memory-engineering/scripts/memory_cost_profiler.py --spec <workload.json>

Lead the report with the construction/query split and **cost per correct answer**. Never present accuracy on its own.

**2. Choose which cost to pay**

python skills/memory-engineering/scripts/memory_architecture_picker.py --constraints <workload.json>

If it exits 2 (`AMBIGUOUS`), **stop and put the printed tie-breaking question to the user.** Do not pick for them — the tie is real, not a tooling limitation.

**3. Audit the real store** (skip if this is a greenfield design)

python skills/memory-engineering/scripts/memory_density_auditor.py --dir <path>

Report the FACT/SKILL/LOG/PROSE split. Users are routinely wrong about how much of their store is transcripts.

**4. Gate on forgetting** — blocking

python skills/memory-engineering/scripts/forgetting_policy_linter.py --policy <design.json>

Exit 4 is a **stop**. Name the failing check (F1 or F4) and its fix. Do not present a FAIL alongside a recommendation to proceed.

Output

Report in this order — cost before quality, always:

1. **Verdict** — one line, leading with the blocking result if there is one 2. **Cost** — construction/query split, cost per correct answer, amortization 3. **Architecture** — the family, and the cost it makes them pay 4. **What the store holds** — the FACT/SKILL/LOG/PROSE split, duplicates, staleness 5. **Forgetting gate** — PASS / CONDITIONAL / FAIL with the named failing checks 6. **Next step** — exactly one, sequenced per the ship order

Attribute every number to its source with a confidence level. Vendor customer figures are testimonials, not benchmarks — label them as such.

For a structured walkthrough, hand the user `skills/memory-engineering/assets/memory_engineer_worksheet.md` (the seven forcing questions) and walk them **one at a time**.

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