claude-skills/docs/commands/cs-memory-engineering.md
Claude abd9c9d8de
docs(site): generate agent-launcher pages (18th domain) + nav
generate-docs.py learns the agent-launcher domain (5 hardcoded maps extended);
regenerated docs tree: 343 skill pages / 96 agent pages / 122 command pages
(561 total). mkdocs.yml nav gains the Agent Launcher skill section (7 pages),
4 cs-agent-* agent entries, and 8 /cs:* command entries; all nav targets verified
to exist.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012FwXG6TqCXKZQvF4iD69cv
2026-08-24 17:26:12 +00:00

3.3 KiB

title description
/cs-memory-engineering — Slash Command for AI Coding Agents Price, choose, audit and gate an agent memory system — the full four-lens memory-engineering pass.. Slash command for Claude Code, Codex CLI, Gemini CLI.

/cs-memory-engineering

:material-console: Slash Command :material-github: Source

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.