cli-vstash
vstash CLI
Local document memory with instant semantic search. Drop any file, ask anything.
Core Commands
# Ingest documents (PDF, DOCX, PPTX, XLSX, Markdown, code, URLs)
vstash add paper.pdf notes.md https://example.com/article
vstash add ./docs --collection research --project ml-survey
# Semantic search (free, no LLM needed)
vstash search "what's the main argument about X?"
# Ask with LLM (requires inference backend)
vstash ask "summarize the key findings"
# Interactive chat session
vstash chat
More from jr2804/prompts
python-ultimate
>-
35output-quality
Detect and eliminate generic, low-quality "AI slop" patterns in natural language, code, and design. Use when REVIEWING existing content (text, code, or visual designs) for quality issues, cleaning up generic patterns, or establishing quality standards. Focuses on pattern detection—not content creation.
11coding-discipline
Language-agnostic behavioral guidelines to reduce common LLM coding mistakes. Use for ANY coding task (all languages) to avoid overcomplication, make surgical changes, surface assumptions before coding, and define verifiable success criteria. Applies behavioral rigor—separate from language-specific technical standards.
10code-deduplication
Pre-write workflow to prevent semantic code duplication. Use BEFORE creating new utility functions, shared modules, or helper code to verify equivalent capabilities don't already exist in the codebase. Requires maintaining CODE_INDEX.md as a capability index organized by purpose (not file location).
6mcp-vstash
MCP server integration for vstash document memory. Use when configuring Claude Desktop or other MCP-compatible AI assistants with persistent document memory, setting up vstash MCP tools for semantic search and Q&A, or integrating vstash with AI assistant workflows via Model Context Protocol.
5sqlmodel
Comprehensive guide for working with SQLModel, PostgreSQL, and SQLAlchemy in FastAPI projects. Use when working with database operations in FastAPI including: (1) Defining SQLModel models and relationships, (2) Database connection and session management, (3) CRUD operations, (4) Query patterns and filtering, (5) Database migrations with Alembic, (6) Testing with SQLite, (7) Performance optimization and connection pooling, (8) Transaction management and error handling, (9) Advanced features like cascading deletes, soft deletes, and event listeners, (10) FastAPI integration patterns. Covers both basic and advanced database patterns for production-ready FastAPI applications.
1