Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/jaycewalzer1/SilverKey-Inc.Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/jaycewalzer1/silverkey-inc./user-preferences-schema)<a href="https://agentmods.dev/rules/jaycewalzer1/silverkey-inc./user-preferences-schema"><img src="https://agentmods.dev/badge/rules/jaycewalzer1/silverkey-inc./user-preferences-schema/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/jaycewalzer1/silverkey-inc./user-preferences-schema"><img src="https://agentmods.dev/badge/rules/jaycewalzer1/silverkey-inc./user-preferences-schema.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.02017 |
| Opus 5.5 | $0.00000 | $0.00807 |
| Sonnet 5.5 | $0.00000 | $0.00403 |
| Haiku 4.5 | $0.00000 | $0.00202 |
Grade A, and why
user-preferences-schema scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Preferences Schema Contract
Overview
User preferences in SilverKey are stored in a normalized database schema across multiple related tables, but presented to the client as a flattened JSON structure. This document defines the contract between client and server, the database models, and the sync pipeline.
Database Models
Core User Model
Located at Server/app/models/user/user.py:
class User(db.Model):
__tablename__ = "users"
# Identity
id = db.Column(db.String(36), primary_key=True)
email = db.Column(db.String(120), unique=True, nullable=False)
name = db.Column(db.String(100), nullable=False)
# Preferences metadata
has_preferences = db.Column(db.Boolean, default=False)
preferences_version = db.Column(db.String(10), nullable=True) # e.g. "v1"
Related Preference Tables
Preferences are split across domain-specific tables (all located in Server/app/models/user/):
| Table | Fields | Purpose |
|---|---|---|
user_demographics |
age_range, household_size, household_income_range, move_in_timeline, etc. | Buyer demographics and timeline |
user_financials |
budget_min, budget_max, down_payment_percentage, credit_score_range, loan_pre_approval, etc. | Financial capacity and constraints |
user_search_intent |
preferred_bedrooms, preferred_bathrooms, preferred_housing_type, preferred_home_age, preferred_sqft_min/max, must_have, deal_breakers, listing_status, extended_buyer_preferences (JSON for v1+ fields) | Core search criteria and must-haves |
user_intent_attribute |
importance ratings for individual features (pool, garage, etc.) | Weighted feature preferences (used by MCDA scoring) |
user_important_location |
address, lat, lng, commute_tolerance, location_type (work/school/etc.) | Commute destinations and tolerances |
user_communication_prefs |
preferred_contact_method, email_notifications_enabled, etc. | Communication preferences |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 216 lines · 0 tokens per session scan A e2866c35f596
user-preferences-schema is a cursor rule published in the GitHub repository jaycewalzer1/SilverKey-Inc. (2 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,017 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-08.
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