Comprime archivos de memoria en lenguaje natural (CLAUDE.md / AGENTS.md, tareas, preferencias) al formato cavernícola para ahorrar tokens de entrada. Conserva toda la sustancia técnica, código, URLs y estructura. La versión comprimida sobrescribe el archivo original. Copia de seguridad legible guardada como ARCHIVO.original.md. Disparador: /caveman-compress RUTA o
Esta habilidad comprime archivos de memoria en lenguaje natural, como CLAUDE.md, AGENTS.md, listas de tareas y preferencias, en un formato apodado 'cavernícola' (caveman), reemplazando frases verbosas con abreviaturas técnicas minimalistas. El objetivo es reducir drásticamente el consumo de tokens de entrada al interactuar con asistentes de IA como Claude Code, preservando al mismo tiempo todo el contenido técnico esencial: nombres de variables, comandos CLI, rutas de archivos, URLs y la estructura general del documento. La compresión sobrescribe el archivo original, mientras que una copia de seguridad legible se guarda con la extensión .original.md. Se activa mediante el comando /caveman-compress seguido de la ruta del archivo, o simplemente diciendo 'comprimir archivo de memoria'. Es útil para desarrolladores que trabajan con asistentes de IA y necesitan mantener contexto extenso sin exceder los límites de tokens.
npx skills add https://github.com/geovanejunior92-debug/claude-skills/tree/main/skills/caveman-compressInstalled? Explore more Redacción y edición skills: steipete/notion, langchain-ai/langchain, bytedance/podcast-generation · View all 6 →
Comprime archivos de memoria en lenguaje natural (CLAUDE.md / AGENTS.md, tareas, preferencias) al formato cavernícola para ahorrar tokens de entrada. Conserva toda la sustancia técnica, código, URLs y estructura. La versión comprimida sobrescribe el archivo original. Copia de seguridad legible guardada como ARCHIVO.original.md. Disparador: /caveman-compress RUTA o "comprimir archivo de memoria"
This repo contains 3 individual skills — each has its own dedicated page.
Close the loop on a Caveman learn report — review the ranked token sinks and apply cost-lowering fixes (trim config, offload recurring context to cavemem) with per-edit consent. Use when the user runs "caveman learn", asks to lower their agent's token cost, wants to trim a heavy CLAUDE.md / AGENTS.md, or wants to offload context they re-paste every session into cavemem.
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.
Monitors task execution for skill improvement opportunities. Use this skill during ANY multi-step task, agentic workflow, or substantive work session where the agent is using tools and producing deliverables. It captures patterns, user corrections, workflow insights, and methodology worth preserving as reusable skills. Also triggers during post-task feedback discussions and when the user explicitly mentions skill observations, improvements, the observation log, skill taxonomy, or asks the agent to watch for skill opportunities. Also known as "One Skill to Rule Them All" — trigger on this phrase too. IMPORTANT: this skill should be invoked at the start of every task-oriented session — if you are about to use tools to produce deliverables, invoke this skill first. For reliable activation, pair this description with a CLAUDE.md / AGENTS.md instruction or harness-level session-start hook (see Recommended Activation Setup) — description-level matching alone is not enforceable.
Notion CLI/API for pages, Markdown content, data sources, files, comments, search, Workers, and raw API calls.
The agent engineering platform.
Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working. Helps inspire and improve your own ad campaigns.
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors