Agent Skills: calci-prediction-market
Context and working knowledge for Calci’s prediction-market domain, which is powered by Kalshi. Use this skill whenever the user asks about Calci prediction markets, Kalshi markets, tickers, order books, pricing, settlement, or the Kalshi API/WebSocket.
ID: ratacat/claude-skills/calci-prediction-market
Install this agent skill to your local
Skill Files
The files that make up calci-prediction-market.
More agent skills
- brave-searchUse when user asks to search the web, look something up online, find current/recent/latest information, or needs cited answers. Triggers on "search", "look up", "find out about", "what is the current/latest", image searches, news lookups. NOT for searching code/files—only for web/internet searches.
- ripgrepUse when searching text in files, codebases, books, or documents. Use when finding files by pattern, searching large files that are too big to read fully, extracting specific content from many files, or when grep/find is too slow. Triggers on "search for", "find occurrences", "look for pattern", "search in files".
- medium-paywall-bypassUse when user shares a Medium article URL behind a paywall and wants to read the full content. Also use for articles on Medium-hosted publications like towardsdatascience.com, betterprogramming.pub, levelup.gitconnected.com, etc.
- design-patternsUse when designing software architecture, refactoring code structure, solving recurring design problems, or when code exhibits symptoms like tight coupling, rigid hierarchies, scattered responsibilities, or difficult-to-test components. Also use when choosing between architectural approaches or reviewing code for structural improvements.
- postgres-query-expertA comprehensive guide for interacting with PostgreSQL 16 databases. Use this skill for constructing standard and advanced SQL queries, optimizing performance, debugging errors, managing schema objects, and introspecting database structure.
- memory-systemsThis skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.