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Found 22 Skills
Google Gemini File Search for managed RAG with 100+ file formats. Use for document Q&A, knowledge bases, or encountering immutability errors, quota issues, polling failures. Supports Gemini 3 Pro/Flash (Gemini 2.5 legacy).
Modern file and content search using fd, ripgrep (rg), and fzf. Triggers on: fd, ripgrep, rg, find files, search code, fzf, fuzzy find, search codebase.
Build document Q&A with Gemini File Search - fully managed RAG with automatic chunking, embeddings, and citations. Upload 100+ file formats, query with natural language. Use when: document Q&A, searchable knowledge bases, semantic search. Troubleshoot: document immutability, storage quota (3x), chunking config, metadata limits (20 max), polling timeouts, displayName dropped (Blob uploads), grounding lost (JSON mode), tool conflicts (googleSearch + fileSearch).
Use when searching codebases (text, structural/AST, files by name, PDFs/archives, code stats) or building context before a task.
This skill should be used when agents need to search codebases for text patterns or structural code patterns. Provides fast search using ripgrep for text and ast-grep for syntax-aware code search.
Find and document file locations in the codebase. Use when you need to locate implementation files, tests, configurations, or any code artifacts by feature or topic.
Find LinkedIn profiles of decision makers at target companies using Extruct's company_people_finder. Takes a company table, adds a people finder column, and produces a linked child people table with names, roles, and LinkedIn URLs. No external API credits — uses Extruct's index only. Triggers on: "find linkedin", "find people", "find contacts", "find decision makers", "people search", "linkedin search", "who to contact", "find profiles".
Context-gathering for finding files to read. Maps codebase structure, returns overview + prioritized file list with line ranges. Thoroughness: quick for lookups, medium for bugs/features, thorough for multi-area, very-thorough for architecture audits. Triggers: explore, find files, where is, how does X work.
Vector-powered CLI for semantic file search with a Claude/Codex skill
Complete guide for OpenAI's Assistants API v2: stateful conversational AI with built-in tools (Code Interpreter, File Search, Function Calling), vector stores for RAG (up to 10,000 files), thread/run lifecycle management, and streaming patterns. Both Node.js SDK and fetch approaches. ⚠️ DEPRECATION NOTICE: OpenAI plans to sunset Assistants API in H1 2026 in favor of Responses API. This skill remains valuable for existing apps and migration planning. Use when: building stateful chatbots with OpenAI, implementing RAG with vector stores, executing Python code with Code Interpreter, using file search for document Q&A, managing conversation threads, streaming assistant responses, or encountering errors like "thread already has active run", vector store indexing delays, run polling timeouts, or file upload issues. Keywords: openai assistants, assistants api, openai threads, openai runs, code interpreter assistant, file search openai, vector store openai, openai rag, assistant streaming, thread persistence, stateful chatbot, thread already has active run, run status polling, vector store error
Fast file finding using fd command-line tool with smart defaults, gitignore awareness, and parallel execution. Use when searching for files by name, extension, or pattern across directories.
Use 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".