client-memory-synthesizer
Client Memory Synthesizer
Convert fragmented information — SOWs, QBRs, meeting notes, campaign data, emails, escalations — into a structured, reusable memory asset that prevents institutional knowledge loss across teams, quarters, and renewals. Generate the tenant context that makes every Claude Project conversation client-aware from message one.
How This Skill Orchestrates
- Script execution (
scripts/memory_synthesizer.py): Processes client data into 7 memory layers (business/commercial/operational/performance/relationship/governance/strategic), scores confidence per item (8 source types), detects memory gaps (critical/important/nice-to-have), classifies risks, and generates the tenant context prompt for Claude Project Instructions - Reference lookup (
references/memory_framework.md): 7-layer memory taxonomy with required/optional fields, confidence scoring rules (8 source types), gap detection priorities, evidence classification standards (fact/inference/assumption/question/risk/recommendation), update protocol triggers - Reference lookup (
references/tenant_context_template.md): Ready-to-paste template for Claude Project Instructions with all sections, formatting rules, complete AcmeAuto MX example, and skill-to-context mapping showing how each OS skill reads the tenant context - Upstream context (Claude — PRIMARY): This skill consumes EVERYTHING — every skill output, every conversation, every decision made in the project. It's the memory crystallization point
- Visual output (Visualizer): Memory completeness dashboard + layer-by-layer status + gap detection + stakeholder map + tenant context preview
Model decision: Interpretation → document intelligence + Governance → risk scoring. Not ML — it's structured knowledge extraction + confidence scoring + gap detection. The LLM does the heavy lifting: reading documents, extracting facts, classifying evidence, and generating the narrative tenant context.
This is the only skill that WRITES to the OS's persistent state — the tenant context it generates becomes the Project Instructions that all other skills read. This makes it the institutional memory engine of the OS.