Price, choose, audit and gate an agent memory system — the full four-lens memory-engineering pass.
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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.
Establish these before running anything. If the user cannot answer 1 or 2, that gap is the first finding — say so rather than guessing:
llm-wiki. Nightly consolidation loop → skillopt-sleep. Bounding a task loop → agent-harness.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.
Report in this order — cost before quality, always:
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.