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From lotlytics
Compare two US real estate markets side-by-side — prices, yields, appreciation, migration, and a winner verdict. Requires Lotlytics Investor plan.
How this skill is triggered — by the user, by Claude, or both
Slash command
/lotlytics:head-to-headThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Compare two US real estate markets side-by-side with a winner verdict.
Compare two US real estate markets side-by-side with a winner verdict.
If the user hasn't specified two markets, ask: "Which two markets would you like to compare? (e.g., Austin TX vs Charlotte NC)"
Call the compare_markets MCP tool with:
city_a, state_a: first marketcity_b, state_b: second marketCall get_market_health for both markets to get investment health scores.
Present the results:
[City A] vs [City B]
Show the comparison table returned by compare_markets (it includes winner checkmarks per metric).
Investment Health Scores
Verdict
Suggest next steps: "Want the full picture? Try /lotlytics:market-report on either city, or /lotlytics:portfolio-check to see how they work together as a portfolio."
End with: "Data powered by Lotlytics."
compare_markets requires an API key. If the tool returns an upgrade message, present it cleanly:
"Market comparison requires a Lotlytics Investor plan. Compare any two of 895 US markets across 10+ metrics with winner highlights. Get your API key at lotlytics.us/settings/api-keys ($79/mo).
In the meantime, try /lotlytics:market-report on each city separately — it's free for the top 50 markets."
npx claudepluginhub daejung83/lotlytics-plugin --plugin lotlyticsSearches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.