Explorar skills

Use when auditing iOS/Android app submissions for compliance with Apple App Store Review Guidelines or Google Play Developer Policies. Scan .ipa, .apk, or .zip files against official store policies, generate structured compliance reports, and identify violations with remediation steps.

Standardized cost-estimation framework for great_cto plans. Forces explicit LLM cost, infra cost, human-supervision time, and the (defensible) human-equivalent comparison. Output format is parsable by the board's /api/cost path — must follow exactly.

Reusable 3-round self-challenge + arbiter pattern for filtering false positives from findings/verdicts. Use when the cost of a false-positive gate block exceeds the cost of ~4 extra LLM turns.

Reference patterns for graph algorithm problems including BFS, DFS, Dijkstra, topological sort, union-find, MST, Bellman-Ford, and bipartite checking. Provides recognition signals, Python templates, edge cases, and common mistakes for each technique. Use when solving problems involving graphs, trees, shortest paths, connectivity, or dependency ordering.

ox 6-phase AI security review pipeline. Combines deterministic OSS scanners (OpenGrep, govulncheck, OSV-Scanner, Syft+Grype, gitleaks) with parallel Claude hunter/validator subagents to find CLI input handling bugs, secret/credential redaction bypasses, daemon IPC authz holes, supply-chain risks, and LLM trust-boundary issues. Diff-scoped (vs origin/main by default). Never blocks merge. Use when a

Use when the user wants to set up the evolver in their project, optimize an LLM agent, improve agent performance, or mentions evolver for the first time in a project without .evolver.json.

Technical SEO for AI search engines. Checks AI crawler accessibility, llms.txt compliance, robots.txt configuration, server-side rendering. Outputs problems + fixes (generates llms.txt, robots.txt modifications). Use when user says "AI crawlers", "llms.txt", "GPTBot", "GEO technical".

Opere o banco de dados vetorial Milvus usando o SDK Python pymilvus para conectar, gerenciar coleções, inserir vetores, realizar buscas por similaridade, híbridas e de texto completo, além de gerenciar índices, partições, bancos de dados ou RBAC.

Monitor brand visibility across AI platforms (Google AI Overview, ChatGPT, Claude, Gemini, Perplexity). Track LLM mentions, analyze AI-generated citations, identify AI search opportunities. Use when user says "AI visibility", "LLM mentions", "AI Overview", "ChatGPT visibility", "AI search", "generative search", "AI citations", or "brand in AI".

Build and maintain a persistent, interlinked wiki from source documents. Knowledge is compiled once and kept current. Based on Andrej Karpathy's LLM Wiki pattern. Commands: /wiki (dashboard), /wiki-ingest, /wiki-query, /wiki-lint, /wiki-save, /wiki-graph, /wiki-review.

Construa agentes de IA com estado e fluxos de trabalho agentivos usando LangGraph em Python. Abrange agentes que utilizam ferramentas com loops LLM-ferramenta, fluxos de trabalho ramificados, memória de conversação, supervisão humana e monitoramento de produção, ideal para tarefas multi-etapas e conversas persistentes.

Interact with 80+ websites and desktop apps from the CLI via Chrome session reuse — zero LLM cost, deterministic JSON/YAML/CSV output. Covers Twitter/X, Reddit, YouTube, HN, Instagram, Spotify, Amazon, LinkedIn, and more; also automates browser actions and registers local CLI tools. Triggers on 'opencli', 'CLI for website', 'scrape without API', 'browser automation', 'operate command'.