Free web and local search via Brave Search API. Use when user says "brave search", "search with brave", "brave web search", "brave local search", "local search", "find businesses near", or "near me".
| name | brave-search |
| description | Free web and local search via Brave Search API. Use when user says "brave search", "search with brave", "brave web search", "brave local search", "local search", "find businesses near", or "near me". |
| allowed-tools | Bash, Read |
| triggers | ["brave search","search with brave","brave web search","brave local search","local search","find businesses near","find restaurants near","near me","free search"] |
| metadata | {"short-description":"Web + local search via Brave API"} |
Web and local search using the Brave Search API. Returns raw results (not LLM-summarized).
BRAVE_API_KEY or BRAVE_SEARCH_API_KEY in environment or .envpip install typer# Web search (JSON by default)
python .agents/skills/brave-search/brave_search.py web "site:openai.com gpt-4o"
# Local search
python .agents/skills/brave-search/brave_search.py local "coffee near Cambridge MA" --no-json
python .agents/skills/brave-search/brave_search.py web "query" [--count N] [--offset N] [--json/--no-json]
python .agents/skills/brave-search/brave_search.py local "query" [--count N] [--json/--no-json]
from brave_search import web_search, local_search
results = web_search("site:openai.com gpt-4o", count=5)
local = local_search("pizza near Boston", count=5)
If MCP tools are available, prefer:
mcp__brave-search__brave_web_search for general web queriesmcp__brave-search__brave_local_search for places/nearby queriespython .agents/skills/brave-search/brave_search.py web "ArangoDB ArangoSearch BM25"
python .agents/skills/brave-search/brave_search.py local "restaurants near Pike Place Market" --no-json
--no-json for quick human-readable outputFile-based inter-agent messaging. Check inbox, send bugs/requests to other projects, acknowledge resolved issues. Use for cross-project agent communication.
Search arXiv for academic papers. Use when user says "find papers on", "search arxiv", "look up research", "academic papers about", "what papers exist on", "use your arxiv tool", or asks about scientific/ML research topics.
Step back and critically reassess project state. Use when asked to "assess", "step back", "fresh eyes", "check alignment", "sanity check", "health check", or "evaluate what's working". Offer to run after major changes (don't auto-run).
Submit code review requests to multiple AI providers (GitHub Copilot, Anthropic Claude, OpenAI Codex, Google Gemini) and get patches back. Use when user says "code review", "review this code", "get a patch for", or needs AI-generated unified diffs for code fixes.
Fetch up-to-date library documentation from Context7 API. Use when user asks "how do I use this library", "show me docs for", "library documentation", "API reference for", or needs current documentation for any code library.
Distill PDF, URL, or text into Q&A pairs stored in memory. Use --context for domain-focused extraction.