search1api

name	search1api
description	Live web search, page retrieval, news, sitemap discovery, trending topics, and turning a URL into a reusable Agent Skill — all through Search1API. Use this skill whenever the user wants to search the web, look something up, research a topic, read or summarize a URL, check current news, explore a site's links, see trending topics, check API balance, or teach an agent a URL so it can be used later. Trigger on phrases like "search for", "look up", "find out about", "what's happening with", "any news on", "what does this link say", "read this page", "summarize this URL", "trending on GitHub", "learn this URL", "learn this site", "turn these docs into a skill", "记住这个链接", "学会这个文档", or when the user shares a bare URL. Prefer the bundled Search1API MCP tools when available and fall back to the search1api CLI (`s1`).
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Search1API

Live web research through the bundled Search1API MCP server or the s1 command-line tool.

Choose the available transport

1. Prefer the connected Search1API MCP tools. They may be namespaced by the host, but their final tool names are search, fetch, news, crawl, sitemap, and trending.
2. If the MCP tools are unavailable, check whether s1 is installed and use the matching CLI command.
3. If neither transport is available, ask the user to connect the bundled MCP server or install the CLI:

curl -fsSL https://cli.search1api.com/install.sh | bash

Or via npm:

npm install -g search1api-cli

Do not install software or switch transports when a working Search1API tool is already available. To update an existing CLI install, the user can run s1 update. Only suggest this when a command fails in a version-related way.

The remote MCP server handles authentication through the host's connection flow. If the host reports that authentication is required, ask the user to connect or re-authenticate Search1API.

For CLI authentication, prefer browser login:

s1 login

Manual fallback:

s1 config set-key <your-api-key>

You can also set the environment variable SEARCH1API_KEY.

If a CLI command fails with "command not found" or an auth error, remind the user to install s1 and run s1 login before retrying.

When to use

User intent	MCP tool	CLI fallback
Shares a URL / link → read and summarize	crawl	s1 crawl <url>
Wants to search the web	search	s1 search "<query>"
Wants the full page behind a search result	fetch with its result id	s1 crawl <url>
Wants news	news	s1 news "<query>"
Wants to explore a site's links	sitemap	s1 sitemap <url>
Wants trending topics	trending	s1 trending <service>
Wants to check remaining credits	Not exposed	s1 balance
Wants an agent to keep a URL for later use	Not exposed	s1 learn <url>

Read it once, or learn it for good?

These look similar and are not:

• "What does this page say?", "summarize this link", "read these docs and answer X" → crawl. One page, answer now, nothing written to disk.
• "Learn this URL", "make a skill out of these docs", "记住这个文档" → learn. Produces an installable skill directory the user's agent can load later.
• Scope follows the request. A single link is one page; a documentation set ("learn these docs", "学一下 XX 文档") is --site pointed at its root — that is the normal case, not an escalation. When it is genuinely unclear, run --discover and show the user what a full run would cover.

Dynamic tuning

Adapt parameters to user intent — don't just use defaults:

• Quick lookup ("search for X", "what is X") → -n 5, no crawl
• Deep research ("research X thoroughly", "comprehensive analysis") → -n 15, then crawl top 3–5 results with separate s1 crawl calls
• User specifies a number ("find 10 articles") → match it with -n
• Recency signals ("latest", "recent", "this week") → -t day or -t month
• Domain-specific ("search on Reddit", "find GitHub repos") → -s reddit, -s github, etc.
• Site-scoped ("only from arxiv.org") → --include arxiv.org
• Chinese queries → use google, bing, wechat, or bilibili according to intent

For MCP calls, map the CLI flags above to their schema equivalents: max_results, search_service, crawl_results, include_sites, exclude_sites, and time_range.

Commands

search

s1 search "<query>" [options]

Option	Description	Default
-n, --max-results <N>	Number of results (1–50)	10
-s, --service <engine>	Search engine	google
-c, --crawl <N>	Crawl N results for full content	0
--include <sites...>	Only include these sites
--exclude <sites...>	Exclude these sites
-t, --time <range>	day, month, year
--json	Raw JSON output

Search engines: google, bing, duckduckgo, yahoo, x, reddit, github, youtube, arxiv, wechat, bilibili, imdb, wikipedia

news

s1 news "<query>" [options]

Same options as search. News services: google, bing, duckduckgo, yahoo, hackernews. Default service: bing.

When user asks for breaking/latest news, always add -t day.

crawl

s1 crawl <url>

Extracts clean content from a URL. Use this whenever the user shares a link.

sitemap

s1 sitemap <url>

Returns all discovered links on a URL/domain.

trending

s1 trending <service> [-n <N>]

Services: github, hackernews.

learn

s1 learn <url> --name <name> [--site] [--exclude <paths...>] [--out <dir>]
s1 learn <url> --site --discover          # list what would be learned; crawls nothing
s1 learn --from <dir> --install <global|project> [--name <new-name>]
s1 learn --refresh <dir>                  # relearn using the source and scope it recorded
s1 learn --validate <dir>                 # static checks on a learned directory

Turns a URL into an Agent Skill directory:

<name>/
  SKILL.md                 # routing layer: when to use, what is in references/
  references/*.md          # page content with title/url frontmatter
  references/sources.json  # source URLs, hashes, and any pages that failed

Option	Description	Default
--name <name>	Required. You choose it — see naming below
--site	Learn everything under the URL's path, not just that page	off
--discover	With --site: print the section breakdown and stop	off
--exclude <paths...>	Path prefixes to leave out, e.g. --exclude /docs/cloud
--max-pages <N>	Safety valve, not a knob to tune	500
--out <dir>	Where to write it	staging dir under the cache
--from <dir>	Install an already-learned directory (no crawl)
--refresh <dir>	Relearn using the source, mode and --exclude it recorded
--validate <dir>	Report problems in a learned directory, exit 1 on errors
--install <scope>	global (~/.agents/skills/) or project (./.agents/skills/)	not installed
--json	Raw JSON output

--site learns what lives under the URL you point at: the docs root gets the whole set, /docs/api gets just that section.

One learn produces one skill, and one bundle per site is the default. Do not split a documentation set you are meeting for the first time. Before crawling you know only the URLs, and grouping pages by their slugs is a guess that goes wrong on exactly the pages that matter: on umami's docs, links, pixels and tags read like reporting features and are all instrumentation. What a page actually covers is knowable only after it has been crawled — at which point you already hold one complete bundle, and its routing table does the routing that separate skills would have done.

Point --site at a single section instead of the root only when that section is plainly its own job and you can already tell — a REST API reference under /docs/api, an SDK under /sdk. When in doubt, take the whole set.

--discover crawls nothing; it only reads what the site publishes, so use it to size a job before running it.

Name it after you have seen it

--discover gives you paths, not titles, and titles are what tell you whether a bundle is one job or four. When a site is unfamiliar, learn it under a working name, read the titles in references/sources.json, then rename on install:

s1 learn --from <staged dir> --name <real-name> --install global

That rewrites the folder, the frontmatter and sources.json together, and warns you if the body still mentions the old name.

Naming is yours to choose

s1 learn will not invent a name — it has only the URL, which is the one thing that should not decide the name.

The hard rules are just the shape: lowercase kebab-case, 64 characters or fewer, starting with a letter, and the folder basename must equal the frontmatter name. --validate checks those.

Everything else is judgement, and the user's judgement wins. Reuse the prefix of related skills they already have (ls ~/.agents/skills). Think about what else might live alongside it: umami-docs is a good name precisely when a separate skill about analytics practice could exist for the same product, and hono-build-api is a good name when nothing else will. Ask the user if two readings are both defensible — they know what their skill library looks like.

Writing the SKILL.md

The generated SKILL.md is a deterministic skeleton — no model writes it. Replace the placeholder description with real trigger wording, and turn the page list into a task-to-page routing table so the reader knows which file answers which question. Until that description is rewritten the skill will not be selected by an agent, and --validate fails on it.

Read the pages you need. Deciding what a section contains, what to exclude, what to call the skill, and what to put in the routing table all require knowing what is actually in the files — the URLs alone will mislead you. What to avoid is dumping the corpus into the conversation: read the pages that inform a decision, not all of them.

State no fact you have not read. The moment you write a concrete claim — a header name, a base URL, a flag, a limit — open that reference file first and cite it (Source: references/<file>.md). Writing API details from memory into a skill whose whole purpose is to be sourced is the one failure this command cannot survive.

Relearning the same URL rewrites references/, reports what changed, and keeps a SKILL.md you have edited while refreshing its reference table in place.

balance

s1 balance

Shows remaining API credits.

Workflows

Deep research

1. Use search (or s1 search "<topic>" -n 15) to get broad results
2. Use fetch with result IDs (or s1 crawl <url>) for the top 3–5 relevant pages
3. Synthesize all gathered content into a coherent answer with source citations

Learning a URL into a skill

Nothing lands on disk until the user confirms, and nothing is installed until it is worth installing. Work in this order — scope decides coverage, coverage decides the name and the description, and all of that has to be settled before anything is installed.

Plan

1. Settle the scope. "Read this link" is one page. "Learn these docs", "学一下 XX 文档" is the set: --site, pointed at the docs root. Point at a single section instead only when the user asked for that section, or when that section is plainly its own job.
2. s1 learn <url> --site --discover --json — crawls nothing, returns the section breakdown and every URL it would learn.
3. Decide what to leave out. Most doc sets carry a section that is dead weight for half their readers: hosted versus self-hosted, one platform's deploy guide out of twenty, contributor and governance pages, changelogs. Ask the user which flavour applies. A section is not automatically homogeneous. Before excluding one, look inside: s1 learn <url-of-that-section> --site --discover. umami's /docs/guides is 33 pages of which 21 are hosting guides and 11 are core how-tos for tracking outbound links, form submissions, SPAs and server-side events — excluding it wholesale silently drops those. When a section is mixed, either keep it or exclude the individual pages.
4. ls ~/.agents/skills before naming. If something for this product already exists, compare the job, not the name: the same job means refresh that skill instead (step 10), a different job means the two names must say which is which. Then choose the name (see naming above) and report the plan to the user: name, sections, page count, and what you are excluding.

Learn and author

5. s1 learn <url> --site --name <name> [--exclude <paths...>] --json. It writes to a staging directory and installs nothing.
6. Author SKILL.md in the staging directory, before installing:
   • a description that names the topics and trigger phrasing, and states what was excluded, so the skill declares its own gaps;
   • a routing table by task, not by directory, so the reader does not have to understand the site's layout;
   • facts only from files you have read, cited.
7. s1 learn --validate <staging dir> and fix what it reports.

Install

8. Tell the user what it captured and where it is, then ask: globally (~/.agents/skills/), in this project (./.agents/skills/), or leave it staged for now.
9. Only after they say yes: s1 learn --from <staging dir> --install <global|project> — no further requests. Add --name <name> here to settle on a different name; it rewrites the folder, the frontmatter and sources.json together. Installing also links the skill into the agent directories that already exist beside the store, and the output names them — read that line, because a skill in the store that no agent is linked to is invisible. Run s1 learn --validate <installed dir> afterwards too — if you renamed on install, the installed copy is the only place the folder name and the frontmatter can be checked against each other.

Maintain

10. Later, s1 learn --refresh <dir> relearns from the source, mode and --exclude it recorded — no arguments to remember. Pass --exclude to change the scope, and it is recorded for next time. A refresh rewrites references/, keeps a SKILL.md you have edited while refreshing its reference table in place, and reports what was added, changed or removed. If the scope changed, update the description to match — a description that still claims a section was excluded after you added it back is worse than no description.
11. Pages that failed after their retry are listed in the output and recorded in references/sources.json. Name them to the user rather than letting the skill look complete, and note that --refresh will try them again.

URL summarization

1. s1 crawl <url> → get the page content
2. Summarize or answer questions based on the content

Trending deep dive

1. s1 trending github -n 10 → discover hot topics
2. s1 search "<interesting topic>" -t day → search for details
3. s1 crawl <url> → read full article if needed

Output handling

• CLI commands produce human-readable output by default; add --json for programmatic processing.
• After retrieving results, summarize and synthesize the information instead of dumping raw output.
• Preserve source URLs and cite the relevant pages in the final answer.
• Distinguish facts found in sources from your own inference.

References

• Usage examples — read for additional patterns