searxng-search

name	searxng-search
description	Aggregate web search across 20+ engines (Google, Google Scholar, Semantic Scholar, arXiv, GitHub, Reddit, etc.). Returns titles, URLs, and snippets. Use for: fact lookups, background reading, finding academic papers or citations, locating repos/packages/APIs, checking recent news, and gathering community discussions or comparisons. Trigger keywords: "search," "look up," "find," "查," "搜," "找," "有没有," and similar. Supports category, language, and single-engine selection when the default Google engine is not enough. Do not use for local file search (use grep), repo-level search (use gh CLI), or in-repo doc search.
metadata	{"argument-hint":"searxng-search \"query\" [-n count] [-e engine] [-l lang] [-c category] [-t timeout]"}
allowed-tools	Bash

searxng-search

A web search skill that queries a SearxNG backend. It defaults to Google for clear, broad web results. Part of a research workflow: find → read → synthesize. Pair with browser-fetch to read the pages this skill finds.

Input

The primary input is a natural language query. Optional flags adjust scope:

Flag	Meaning	Example
query	What to search for (required)	"knowledge graph multi-hop reasoning"
-n	Limit number of results	-n 5
-e	Use one specific engine instead of Google	-e google_scholar
-l	Language preference	-l zh-CN
-c	Category	-c news
-t	Request timeout in seconds	-t 60
-u	Output URLs only	-u
-j	Output JSON (structured, for scripting)	-j

How to invoke

The script is bundled with this skill. Always invoke it via the script path so it works regardless of whether searxng-search is on the user's PATH:

uv run --script ${SKILL_DIR}/scripts/searxng-search "query"
uv run --script ${SKILL_DIR}/scripts/searxng-search "query" -n 5
uv run --script ${SKILL_DIR}/scripts/searxng-search "query" -e google_scholar
uv run --script ${SKILL_DIR}/scripts/searxng-search "query" -c news -l zh-CN

Do not invoke the script directly (e.g. ${SKILL_DIR}/scripts/searxng-search ...) — the file may not have execute permission after deployment. Always use uv run --script.

The only external dependency is uv (auto-installs inline Python deps on first run) and a running SearxNG backend.

Environment

SEARXNG_URL — SearxNG endpoint (default: http://localhost:8082)

WEB_KIT_API_KEY — required; sent as the X-API-Key header to authenticate to the gateway.

Default behavior

If you do not specify any flags:

• Engine: Google
• Results: top 10
• Output: concise human-readable list with title, URL, snippet, and engine tag
• Errors: JSON to stdout with an error_type field (exit code 1)

Available engines

Engines are grouped by what they index. Choose at most one non-default engine per search. To compare sources, run separate searches.

The CLI exposes shell-friendly aliases such as google_scholar and semantic_scholar; the script maps them to SearXNG's configured engine names such as google scholar and semantic scholar.

Group	Engines
General	google, bing, duckduckgo
Academic	google_scholar, semantic_scholar, openalex, dblp, pubmed, arxiv
Code and packages	github, github_code, gitlab, huggingface, npm, pypi, crates, lib_rs, pkg_go_dev, sourcehut, microsoft_learn, nvd
Community	hackernews, reddit, wikidata
Books and library	annas_archive, zlibrary

Per-engine usage notes and examples are in references/engines.md.

Typical workflow

1. Start with the default search. The default already indexes a wide range of sources (paper aggregators, repos, blogs, docs, forums). Often enough to understand the landscape of a topic.

2. Observe what kind of results appear. If the top results already point to the right kind of sources (papers, repos, news, forums), you may already have what you need.

3. If results are not from the expected domain, retry with one domain-specific engine. For example:

   • Academic metadata: -e google_scholar, then a separate search with -e semantic_scholar if needed
   • Code and packages: -e github, then a separate search with -e npm or -e pypi if needed
   • Community experience: -e reddit, then a separate search with -e hackernews if needed
   • News and recent events: -c news

4. If results are from the right domain but not specific enough, narrow further — e.g. site:arxiv.org in the query, or point at a specific source with -e.

How to interpret results

• The default output shows which engine each result came from.
• If results cluster around a particular domain (papers, repos, docs), that may indicate which single domain-specific engine could be useful next.
• If results are already rich and directly relevant, additional searches may not add much value.
• A site: filter is useful when you already know the kind of source you want, without switching the underlying engine set.

Notes on engine choice

• For academic tasks, dedicated academic engines often provide cleaner metadata (authors, years, venues, citations). Google already indexes most major paper sources, so the default is a reasonable first step.
• For code and package tasks, dedicated engines point directly to repos and package registries, which the default may surface less reliably.
• Some engines (notably arxiv) can be slower or more prone to timeout. If you do not specifically need arXiv as a source, the default or another academic engine is often sufficient.
• -e accepts exactly one engine. Do not pass comma-separated engine lists; mixed-engine results are harder to interpret and compare.
• -e changes which search path is used; it does not just filter the default results. Prefer default Google first, then one explicit engine per retry.
• The default human-readable output is intentionally concise. For structured results, -j is available (hidden from default help because it produces longer output).

Error handling

All errors are JSON to stdout (exit code 1):

{"error_type": "timeout", "query": "...", "hint": "..."}

error_type	What it means	What is available to you
timeout	The request took too long	Try a longer timeout with -t 60
no_results	No results came back from the requested engines	Refine the query or try different engines
http_error	The backend returned an HTTP error	Usually transient
backend_unreachable	The SearxNG backend could not be reached	Check SEARXNG_URL

If the command succeeds but an engine failed, the output includes which engine did not respond — useful for deciding whether to retry with a different engine.

Final output

Depending on how you invoke the skill, you may get:

• Default human-readable list (recommended for most tasks):

  [1] Title
      https://...
      Snippet text (max 200 chars)
      [google]
  

• URL-only list (useful for feeding into browser-fetch):

  https://...
  https://...
  

• JSON (mainly for scripting or structured parsing; errors are always JSON)