Agent skill

Ego Browser Search

by taxueseek in taxueseek/argo

Searches and fetches pages through a real logged-in Chromium session when ordinary API or HTML retrieval cannot get past login walls, scripts or anti-bot checks.

MITAuto-check passedProductivity & Automation

SKILL.md written in Chinese; this summary is our English description.

Install Ego Browser Search

skills CLI
$ npx skills add taxueseek/argo --skill ego-search -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install taxueseek/argo ego-search --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/taxueseek/argo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/sub-skills/ego-search .claude/skills/ego-search && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ego-search
GitHub stars
188
Token cost
~2.1k tokens
SKILL.md length
473 words
Files
12 (incl. scripts, references)
Skills in repo
5
Repo updated
First seen
Licence
MIT

At a glance

Searches and fetches pages through a real logged-in Chromium session when ordinary API or HTML retrieval cannot get past login walls, scripts or anti-bot checks.

  • Works in 4 steps: 同站多轮:--site zhihu.com(或 --task-space… → 单次取证:默认即可(跑完关空间) → quality.login_likely_ok=false → 在 ego… → …
  • Searching content that sits behind a login wall
  • SKILL.md covers 专业搜索模式(默认关闭), 定位与升级决策, 依赖与环境 and 快速开始(ego_search.py CLI), plus 3 more sections
  • Runs Python scripts from its folder; calls python3; reaches zhihu.com and cn.bing.com

What it does

This sub-skill of the argo search tool, documented in Chinese, drives a real browser runtime for content that plain HTTP retrieval misses: pages behind logins on sites such as Zhihu, Xiaohongshu, Weibo and X, JavaScript-rendered and single-page apps, pages behind anti-bot checks (best effort, heavy protection may still fail), interactive searches with paging or load-more, live search result pages, and same-origin API endpoints that can be called directly.

Professional search mode is off by default and is switched on with an enable command, which the agent may run only when you explicitly ask for it. Once on, search, fetch, act and api commands are available. Two runtimes are supported and complement each other: the ego lite browser and a bridge to your own browser extension, and either one being online is enough. Results print as JSON to stdout.

Every result carries provenance fields marking that a login state was used, that it belongs to the login partition, that it must not be written to the shared public search cache, and that it may still be merged with public results for evidence scoring. The skill advises trying ordinary retrieval first because it is faster and cheaper, and escalating only when results or page text are missing.

When your agent uses it

  • Searching content that sits behind a login wall
  • Extracting results from JavaScript-rendered or single-page sites
  • Paging through or expanding a dynamic search that one request cannot cover
  • Fetching data from a known same-origin API through the browser

Example prompts

  • “Turn on professional search mode, then search Zhihu for discussions about vector databases.”
  • “Fetch the full text of this login-walled article through my browser session.”
  • “Page through this search results site and give me everything as JSON.”
  • “Pull the data from the site's same-origin API endpoint instead of scraping the page.”

Requirements

  • macOS with the ego browser runtime and its ego-browser command, or the WebBridge browser extension
  • Professional search mode switched on by you
  • Python 3 to run ego_search.py

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. 同站多轮:--site zhihu.com(或 --task-space site:zhihu.com --keep-space)
  2. 单次取证:默认即可(跑完关空间)
  3. quality.login_likely_ok=false → 在 ego App 或用户 Chrome 人工登录后重试
  4. 不要把登录态 body 写入 public cache;汇总用 merge

What it can do on your machine

Read from SKILL.md and the folder at commit a6657b0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 7 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • zhihu.com
    • cn.bing.com

    Also links to:

    • lite.ego.app
    • kimi.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ego Browser Search loads about 2.1k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 473 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from taxueseek/argo at commit a6657b0, republished under its MIT licence (© taxueseek). 473 words, ~2,123 tokens.

Download SKILL.mdSave it as .claude/skills/ego-search/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
ego-search
description
ego-search 是 argo 的浏览器态搜索增强子技能,基于真实 Chromium 浏览器运行时,提供 登录态继承、JS 渲染、反爬(尽力而为,未验证可穿透重度防护)、动态交互与同源接口数据直取(api 模式)能力。专业搜索 模式默认关闭,需用户明确要求并执行开启指令后启用。当 argo 的 API/HTML 引擎覆盖不到 时启用:登录墙后的内容(知乎/小红书/微博/X/公众号)、JS 渲染与 SPA 页面、反爬与 Cloudflare 保护页(尽力而为,重度防护可能失败)、需要交互(翻页/展开/滚动加载)的动态搜索、SPA/XHR 接口数据直取、 以及需要真实登录态才能搜到的私有内容。输出对齐 argo 统一 JSON schema,可直接进入 evidence 评分与 RRF 融合。Triggers include 浏览器搜索、登录后才能搜到、JS 渲染页面、 SERP 补充、动态内容抓取、登录态抓取、API 数据直取、接口数据。
metadata.version
1.6.0
metadata.date
2026-08-07
metadata.upstream
ego lite(ego-browser)+ WebBridge 扩展桥(双运行时)
metadata.parent
argo
metadata.pro_mode
默认关闭,手动开启(enable/disable)
metadata.cache
登录态结果 cache_eligible=false,禁止写入 argo 公共缓存
metadata.architecture
dual_runtime_complete
metadata.security
URL 守卫 + 任务空间收尾 + 专业模式闸门 + 登录墙质量信号
triggers
专业搜索模式, 开启专业搜索模式, 浏览器搜索, 登录态, 登录墙, JS渲染, SERP, 动态内容, 反爬(尽力而为), 浏览器抓取, API数据直取, 接口数据

ego-search(argo 子技能)

ego-search 是 argo 的登录态专业搜索子技能(v1.4 完全态):

  • 双运行时保留:ego lite(ego-browser)与 WebBridge(用户浏览器扩展桥)互补,不互相替代
  • 任一可用即可:search / fetch / act / api 在至少一条运行时在线时可用
  • 与常规检索隔离:登录态结果 cache_eligible=false,禁止进公共 SearchCache
  • 汇总可融合:输出带 merge_with_public_ok=true,分析层可把 public + login 两路结果一起喂 evidence

专业搜索模式(默认关闭)

本子技能涉及真实浏览器登录态继承,出于安全与可靠性考虑默认关闭。开启后 search/fetch/act/api 与直接浏览器操作才可用;未开启时调用会被拒绝并提示开启命令。

bash
# 开启(必须由用户明确要求「开启专业搜索模式」后执行)
python3 sub-skills/ego-search/scripts/ego_search.py enable
# 关闭
python3 sub-skills/ego-search/scripts/ego_search.py disable
# 查看状态
python3 sub-skills/ego-search/scripts/ego_search.py status
  • 纪律:Agent 不得自行开启——只有用户明确表达开启意图时才运行 enable。
  • 未开启时,同样不得用 heredoc 直连浏览器运行时绕开闸门。
  • 状态持久化于 ~/.local/state/ego-search/pro-mode.json,开启后长期生效,直到手动关闭。

定位与升级决策

主系统 API/HTML 检索覆盖不足时,升级到 ego-search:

场景常规检索ego-search 补什么
登录墙内容(知乎/小红书/微博/X/公众号/会员站)拿不到正文任务空间继承登录态,搜并抓取
JS 渲染 / SPA / 懒加载页面可能空壳真实浏览器执行 JS 后提取
反爬 / Cloudflare 保护(尽力而为)HTTP 失败真实浏览器尝试,重度防护可能失败
需要交互的动态搜索(翻页/「加载更多」/表单筛选)单次请求不够act 或 ego-browser heredoc 多步
搜索引擎实时 SERP(Google/Bing/百度)可能被反爬真实 SERP 结构化提取
深嵌套 iframe 页面常挂快照专长
已知同源 API URLDOM 不全api + browserFetch
未知 XHR 尚未拿到 URL—不在 CLI 内;heredoc 自探或先拿 URL 再 api

决策顺序:

text
常规检索(快、省 token)
  → 结果不足 / 正文拿不到 / 需要登录态
    → ego-search search|fetch|act|api(pro-mode 开启)
      → 更复杂多步 → ego-browser nodejs heredoc
高后果结论 → 证据核验

依赖与环境

  • 官方项目:ego lite → https://lite.ego.app/(官网);WebBridge → https://www.kimi.com/zh-cn/help/kimi-webbridge/kimi-webbridge-introduction(Kimi WebBridge 官方帮助中心)。
  • 浏览器运行时(macOS 应用)与 运行时命令 ego-browser(~/.local/bin)。
  • 首次使用前确认:command -v ego-browser。未安装时按 references/install.md 完成安装。
  • 运行时依赖由安装包自管理,本子技能不重复携带安装脚本。
  • 登录态来自运行时 onboarding(可导入 Chrome 数据);不同站点登录态由用户在运行时中维护。

快速开始(ego_search.py CLI)

bash
# 首次使用:先开启专业搜索模式(需用户确认;开启后长期生效)
python3 sub-skills/ego-search/scripts/ego_search.py enable

# 探测运行时与专业模式(--fix 幂等启 WebBridge 桥)
python3 sub-skills/ego-search/scripts/ego_search.py status [--fix]

# 浏览器态搜索(真实 SERP,输出 argo JSON schema)
#   --runtime auto(默认: 有 ego 用 ego,否则 webbridge) | ego | webbridge
python3 sub-skills/ego-search/scripts/ego_search.py search "AI agent 浏览器自动化" --engine bing --n 8

# 强制登录态站点搜索(如知乎/小红书,需登录态)
python3 sub-skills/ego-search/scripts/ego_search.py search "site:zhihu.com AI 搜索" --engine bing

# 浏览器态正文提取(JS 渲染/反爬/登录墙页面)
python3 sub-skills/ego-search/scripts/ego_search.py fetch "https://example.com/article" --focus 关键词

# 同源 API 数据直取(登录态站点接口)
python3 sub-skills/ego-search/scripts/ego_search.py api "https://www.zhihu.com/api/v4/search_v3?t=general&q=AI代理&limit=5" --origin "https://www.zhihu.com"

# 指定任务空间名(同一目标的多轮操作复用同名空间)
python3 sub-skills/ego-search/scripts/ego_search.py search "竞品分析" --task-space "竞品调研"

输出约定:JSON 到 stdout(机器可读,可管道给 evidence.py / 解析喂 RRF),日志到 stderr。

登录态 provenance(强制)

所有 search / fetch / act / api 输出均带:

字段值含义
login_state_usedtrue使用了浏览器登录态
auth_partitionlogin认证分区标签(不写 cookie)
cache_eligiblefalse禁止写入 argo 公共 SearchCache / set_fetch
search_partitionlogin与常规 public 检索分区隔离
merge_with_public_oktrue允许在汇总分析时与 public 结果一并送入 evidence
runtimeego | webbridge实际使用的运行时

公共库路径 ~/.cache/unified-search/cache.db 在 set / set_engine / set_fetch 入口会硬拒绝 此类载荷。登录态结果默认不缓存 body。

与常规搜索的隔离与融合
阶段规则
检索public(argo)与 login(本技能)分开跑、分 cache、分 partition
缓存login 永不进 argo 公共缓存;两路互不 soft-hit
汇总Agent 可将两路 results / 正文一并交给 evidence 或报告生成;用 source / runtime / search_partition 区分权重与可信语境
安全与登录态长期可用
控制默认说明
专业模式闸门关需用户 enable 后才跑登录态检索
URL 守卫拦fetch/api/navigate 仅 http(s),默认禁本机/字面私有 IP;EGO_SEARCH_STRICT_SSRF=1 全量 DNS;ARGO_ALLOW_PRIVATE_URLS=1 放行
任务空间收尾关空间ego 路径默认 completeTaskSpace keep:false,防标签堆积
--keep-space / --site见右多轮保温;--site zhihu.com → site:zhihu.com 并默认 keep
登录墙质量信号开fetch/act 输出 quality.auth_wall_suspected / login_likely_ok
WebBridge session不自动 close_session避免误关用户标签
公共缓存拒写cache_eligible=false + SearchCache 硬守卫
分析融合merge与 public 结果隔离缓存、汇总时用 merge --public --login
降级auto任一安装即可;ego 优先,失败降 WebBridge

登录态稳定建议:

  1. 同站多轮:--site zhihu.com(或 --task-space site:zhihu.com --keep-space)
  2. 单次取证:默认即可(跑完关空间)
  3. quality.login_likely_ok=false → 在 ego App 或用户 Chrome 人工登录后重试
  4. 不要把登录态 body 写入 public cache;汇总用 merge
Show full SKILL.md (184 more words)Show less
search 模式

打开指定搜索引擎的真实结果页,js() 提取结构化 SERP,输出:

json
{
  "query": "AI agent 浏览器自动化",
  "engine": "ego_browser_bing",
  "source": "ego-browser",
  "url": "https://cn.bing.com/search?q=...",
  "results": [
    {"title": "...", "url": "https://...", "snippet": "..."}
  ],
  "count": 8,
  "fetch_method": "browser",
  "login_state_used": true,
  "auth_partition": "login",
  "cache_eligible": false
}
  • --engine:bing(默认,最稳)/ baidu(中文)/ google(需网络可达,可能弹验证)。
  • --n:结果条数(默认 8)。
  • 注意:SERP 本身是检索入口,不是正文证据——高后果结论必须 fetch 打开结果页取正文后再下判断(与 argo「SERP 链禁止当正文来源」纪律一致)。
fetch 模式

浏览器态正文提取。优先 article/main/[role=main] 等语义容器,回退 document.body.innerText, 输出 { url, title, content, word_count, fetch_method: "browser" },正文可管道给 argo 质量信号分析。

  • --focus 关键词:若传,只返回包含该关键词附近的段落(减少 token)。
api 模式

浏览器上下文同源 API 数据直取(v1.1.0)。 先打开目标站点页面(继承登录态),再用 browserFetch 从该页面上下文请求同源接口, 拿回结构化 JSON/文本。覆盖 DOM 提取拿不到的场景:

  • SPA/XHR 懒加载截断:页面只渲染前几屏,全量数据在接口里 → 直取接口拿全量。
  • 登录态私有数据:已登录站点的个人/搜索接口 → 页面 cookies 随请求携带。
  • 站点内 API 搜索:直接调站点搜索 API(如知乎 search_v3),比解析 SERP 更干净。
json
{
  "api_url": "https://www.zhihu.com/api/v4/search_v3?t=general&q=...",
  "page_url": "https://www.zhihu.com/",
  "page_title": "(1 条消息) 首页 - 知乎",
  "data": { "paging": {...}, "data": [...] },
  "data_type": "json",
  "fetch_method": "browser_api"
}
  • --origin:登录态上下文页面 URL。默认取 API URL 同源(scheme://host); 跨子域场景(如 cn.bing.com 请求 www.bing.com)须显式指定,否则 browserFetch 受 同源约束失败。
  • data_type:json / text / error(跨域、网络错误等归一化为 error,脚本不崩)。
  • 大响应截断:data 超 100KB 时截断并标记 truncated: true、data_type 加 _truncated 后缀。
  • 定位:api 模式输出是「原始数据」,供 Agent 分析判断,不是 SERP 证据, 高后果结论仍须走 argo_evidence 核验。
act 模式

把「搜索 → 点开结果 → 抓正文」等常见链式动作一次跑完。 支持 --engine bing|baidu|google(与 search 同表,默认 bing)。 输出含 query / results / detail 与登录态 provenance。 更复杂的多步交互(登录、表单、翻页、对比多个页面)用 ego-browser nodejs heredoc; ego_search.py 只覆盖高频确定路径。

与 argo 主系统配合

  1. 证据流水线:ego-search 的输出与 argo schema 对齐,results 可直接喂 python3 scripts/evidence.py "查询词" --stdin --json 做 Selection×Absorption 评分; 正文可复用 content_signals / content_security 检查。
  2. RRF 融合:source=ego-browser / engine=ego_browser_<engine> 可参与多源去重; envelope 会标 login_state_used=true。不得把该路结果写入公共 SearchCache。
  3. 分层查询纪律:事实类问题保持 argo 的 2–3 条子查询纪律(来源要求 / 对比 / 主体), ego-search 用于其中「登录态/动态内容」这一路,不替代 argo 引擎的全量召回。
  4. 防污染:内容安全引擎照常;缓存隔离靠 cache_eligible=false + SearchCache 硬守卫。
  5. 缓存策略:默认不缓存登录态 body;若未来做短缓存,必须独立库 ~/.cache/ego-search/,键含 auth_partition,禁止 soft-hit 公共 combo。

按需读取

复杂交互、heredoc 全 helper 速查、任务空间归属权纪律 → references/browser-runtime.md;安装细节 → references/install.md;升级到原版运行时 → references/original-ego-upgrade.md。

参考

  • 浏览器运行时细节:references/install.md(依赖安装)

© taxueseek, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts, references) in sub-skills/ego-search of taxueseek/argo.

  • SKILL.md
  • references/browser-runtime.md
  • references/install.md
  • references/original-ego-upgrade.md
  • scripts/ego_search.py
  • scripts/merge.py
  • scripts/quality.py
  • scripts/runtime.py
  • scripts/safety.py
  • scripts/serp_spec.py
  • scripts/webbridge_adapter.py
  • tests/test_query_normalize.py

Open the folder on GitHubat commit a6657b0

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Questions about Ego Browser Search

What does Ego Browser Search do?

Searches and fetches pages through a real logged-in Chromium session when ordinary API or HTML retrieval cannot get past login walls, scripts or anti-bot checks. This sub-skill of the argo search tool, documented in Chinese, drives a real browser runtime for content that plain HTTP retrieval misses: pages behind logins on sites such as Zhihu, Xiaohongshu, Weibo and X, JavaScript-rendered and single-page apps, pages behind anti-bot checks (best effort, heavy protection may still fail), interactive searches with paging or load-more, live search result pages, and same-origin API endpoints that can be called directly.

When should I use Ego Browser Search?

Ego Browser Search fits situations like: searching content that sits behind a login wall; extracting results from JavaScript-rendered or single-page sites; paging through or expanding a dynamic search that one request cannot cover; fetching data from a known same-origin API through the browser.

How do I install Ego Browser Search in Claude Code?

Run `npx skills add taxueseek/argo --skill ego-search -a claude-code`. Or copy the skill folder (sub-skills/ego-search in taxueseek/argo) into .claude/skills/ego-search in your project. Claude Code loads it when a task matches its description.

How do I install Ego Browser Search in Codex?

Run `npx skills add taxueseek/argo --skill ego-search -a codex`. Or copy the skill folder (sub-skills/ego-search in taxueseek/argo) into .agents/skills/ego-search in your project. Codex loads it when a task matches its description.

Can I use Ego Browser Search in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add taxueseek/argo --skill ego-search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ego-search, .gemini/skills/ego-search, .github/skills/ego-search and .opencode/skills/ego-search in your project.

What does Ego Browser Search need to run?

Going by SKILL.md and its folder, Ego Browser Search needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: macOS with the ego browser runtime and its ego-browser command, or the WebBridge browser extension; Professional search mode switched on by you; Python 3 to run ego_search.py.

Does Ego Browser Search access the network?

SKILL.md names 4 domains. In commands or code: zhihu.com and cn.bing.com; the agent is likely to contact these when it follows the instructions. As links in the text: lite.ego.app and kimi.com. This is read from the text; nothing was executed.

Is Ego Browser Search safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Ego Browser Search use?

Ego Browser Search is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ego Browser Search use?

About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.8k tokens, read only when the agent opens those files.

What are the alternatives to Ego Browser Search?

Skills that share tags, products or a category with Ego Browser Search: Bright Data MCP (brightdata/skills, 264 stars), Web Access via Browser CDP (eze-is/web-access, 9.1k stars), Reddit JSON Fetcher (ykdojo/claude-code-tips, 10k stars) and Scrapingbee CLI (ScrapingBee/scrapingbee-cli, 108 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ego Browser Search?

taxueseek (a GitHub user) maintains it in taxueseek/argo, which has 188 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 6, 2026.

Source: taxueseek/argo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.