Comprime arquivos de memória em linguagem natural (CLAUDE.md / AGENTS.md, tarefas, preferências) no formato 'caveman' para economizar tokens de entrada. Preserva todo o conteúdo técnico, código, URLs e estrutura. A versão comprimida substitui o arquivo original. Um backup legível é salvo como FILE.original.md. Ativação: /caveman-compress FILEPATH ou 'comprimir arquivo de memória'.
Esta skill comprime arquivos de memória em linguagem natural (como CLAUDE.md, AGENTS.md, listas de tarefas e preferências) no formato 'caveman', reduzindo drasticamente o consumo de tokens de entrada sem perder informações essenciais. É especialmente útil para desenvolvedores que usam agentes de IA (ex.: Claude Code) e precisam manter contexto extenso dentro dos limites de tokens. A skill preserva todo conteúdo técnico, incluindo código, URLs e estrutura de markdown, garantindo que o agente continue entendendo comandos, dependências e configurações. Após a compressão, o arquivo original é sobrescrito pela versão comprimida, e um backup legível é salvo com a extensão .original.md. Pode ser acionada pelo comando /caveman-compress caminho/do/arquivo ou pela frase 'comprimir arquivo de memória'.
npx skills add https://github.com/geovanejunior92-debug/claude-skills/tree/main/skills/caveman-compressInstalled? Explore more Escrita e Edição skills: steipete/notion, langchain-ai/langchain, bytedance/podcast-generation · View all 6 →
Comprime arquivos de memória em linguagem natural (CLAUDE.md / AGENTS.md, tarefas, preferências) no formato 'caveman' para economizar tokens de entrada. Preserva todo o conteúdo técnico, código, URLs e estrutura. A versão comprimida substitui o arquivo original. Um backup legível é salvo como FILE.original.md. Ativação: /caveman-compress CAMINHO/DO/ARQUIVO ou "comprimir arquivo de memória".
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