Deep Research

Analyze any topic, domain, or paper and generate a beautiful HTML report using Actionbook browser automation and json-ui rendering.

Usage

/deep-research:analyze <topic> /deep-research:analyze <topic> --lang zh /deep-research:analyze <topic> --output ./reports/my-report.json

Or simply tell Claude: "帮我深度研究 XXX 并生成报告" / "Research XXX and generate a report"

Parameters

Parameter Required Default Description

topic

Yes

The subject to research (any text)

--lang

No both

Language: en , zh , or both (bilingual)

--output

No ./output/<topic-slug>.json

Output path for JSON report

Topic Detection

Pattern Type Strategy

arxiv:XXXX.XXXXX

Paper arXiv Advanced Search (Step 2b) + ar5iv deep read

doi:10.XXX/...

Paper Resolve DOI, then arXiv Advanced Search for related work

Academic keywords (paper, research, model, algorithm) Academic topic arXiv Advanced Search (Step 2b) + Google for non-academic sources

URL Specific page Fetch and analyze the page

General text Topic research Google search + arXiv Advanced Search if relevant

Architecture

┌──────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────┐ │ Claude │────▶│ Actionbook │────▶│ Web Pages │────▶│ Extract │ │ Code │ │ Browser CLI │ │ (multiple) │ │ Content │ └──────────┘ └──────────────┘ └──────────────┘ └─────┬────┘ │ │ │ ┌──────────────┐ ┌──────────────┐ │ ├─────────▶│ Actionbook │ │ arXiv Adv. │ │ │ │ search/get │────▶│ Search Form │──────────▶│ │ │ (selectors) │ │ (40+ fields) │ │ │ └──────────────┘ └──────────────┘ │ │ │ │ Actionbook indexes arXiv form selectors, │ │ enabling field-specific, filtered academic │ │ searches that WebFetch/WebSearch CANNOT do. │ │ │ ┌──────────┐ ┌──────────────┐ ┌──────────────┐ │ │ Open in │◀────│ json-ui │◀────│ Write JSON │◀──────────┘ │ Browser │ │ render │ │ Report │ Synthesize └──────────┘ └──────────────┘ └──────────────┘

Why Actionbook, Not WebFetch/WebSearch?

Capability Actionbook WebFetch/WebSearch

Operate complex web forms (dropdowns, checkboxes, date pickers) Yes — uses indexed selectors No

arXiv: search by Author, Title, Abstract separately Yes — #terms-0-field select No — keyword only

arXiv: filter by subject (CS, Physics, Math, ...) Yes — category checkboxes No

arXiv: filter by date range or specific year Yes — date inputs No

Read pages with verified selectors (no guessing) Yes — actionbook get

No — raw HTML parse

Interact with any indexed site's UI Yes — click, type, select No — read-only

This is the core value of Actionbook for research: it turns web forms into structured, programmable interfaces for AI agents.

MUST USE Actionbook CLI

Always use actionbook browser commands for web browsing. Never use WebFetch or WebSearch.

actionbook browser open <url> # Navigate to page actionbook browser snapshot # Get accessibility tree actionbook browser text [selector] # Extract text content actionbook browser screenshot [path] # Capture visual actionbook browser click <selector> # Click element actionbook browser close # Close browser (ALWAYS do this at end)

Complete Workflow

Step 1: Plan Search Strategy

Based on the topic, generate 5-8 search queries from different angles:

• Core definition / overview

• Latest developments / news

• Technical details / implementation

• Comparisons / alternatives

• Expert opinions / analysis

• Use cases / applications

Search order — ALWAYS query Actionbook API first, then search:

Step Action Why

Step 2 (FIRST) Query Actionbook API Get verified selectors for arXiv Advanced Search form, ar5iv papers, and any other known sites BEFORE browsing. This is the foundation for all subsequent steps.

Step 3 (SECOND) arXiv Advanced Search Use Actionbook selectors from Step 2 to perform multi-field, filtered academic search. Even non-academic topics often have relevant papers.

Step 4 (THIRD) Google / Bing search Supplement with blogs, news, code, discussions, non-academic sources.

IMPORTANT: Always query Actionbook API first (Step 2) to get selectors, then use them in arXiv Advanced Search (Step 3). This is what makes Actionbook-powered research fundamentally different from WebFetch/WebSearch — the agent knows the exact selectors for every form field before it even opens the browser.

Step 2: Query Actionbook API for Selectors (ALWAYS DO THIS FIRST)

BEFORE browsing any URL, query Actionbook's indexed selectors. This gives you verified CSS/XPath selectors instead of guessing.

Search for indexed actions by domain

actionbook search "<keywords>" -d "<domain>"

Get detailed selectors for a specific page

actionbook get "<domain>:/<path>:<area>"

Pre-indexed sites useful for research:

Site area_id Key Selectors

arXiv Advanced Search arxiv.org:/search/advanced:default

40+ selectors: field select, term input, category checkboxes (CS/Physics/Math/...), date range filters, cross-list control — used in Step 3

ar5iv paper ar5iv.labs.arxiv.org:/html/{paper_id}:default

h1.ltx_title_document (title), div.ltx_authors (authors), div.ltx_abstract (abstract), section.ltx_section (sections)

Google Scholar scholar.google.com:/:default

#gs_hdr_tsi (search input), #gs_hdr_tsb (search button)

arXiv homepage arxiv.org:/:default

Global search across 2.4M+ articles

For any URL you plan to visit, run actionbook search "<keywords>" -d "<domain>" to check if it's indexed. Use indexed selectors when available; fall back to actionbook browser snapshot for unindexed sites.

Example: Get arXiv Advanced Search selectors before searching:

Query Actionbook for arXiv form selectors

actionbook get "arxiv.org:/search/advanced:default"

Returns 40+ selectors: #terms-0-field, #terms-0-term, #classification-computer_science, etc.

Step 3: arXiv Advanced Search (Using Actionbook Selectors)

Key differentiator: WebFetch/WebSearch can only do simple keyword searches. Actionbook has indexed the entire arXiv Advanced Search form with 40+ verified selectors (queried in Step 2), enabling multi-field, multi-criteria academic searches — just like a human researcher would use the form.

Using the selectors obtained from Step 2, the Agent can:

Capability Actionbook Selector WebFetch/WebSearch

Search by specific field (Title, Author, Abstract) #terms-0-field select → choose field Not possible

Add multiple search terms with boolean logic button "Add another term +"

Not possible

Filter by subject (CS, Physics, Math, etc.) #classification-computer_science checkbox Not possible

Filter by date range #date-filter_by-3 radio + #date-from_date / #date-to_date

Not possible

Filter by specific year #date-filter_by-2 radio + #date-year input Not possible

Include/exclude cross-listed papers #classification-include_cross_list-0/1 radio Not possible

Control results display #size select, #abstracts-0/1 radio Not possible

Example: Search for recent CS papers by a specific author:

Open arXiv Advanced Search

actionbook browser open "https://arxiv.org/search/advanced"

1. Set search field to "Author" and type author name

actionbook browser click "#terms-0-field" actionbook browser click "option[value='author']" actionbook browser type "#terms-0-term" "Yann LeCun"

2. Filter to Computer Science only

actionbook browser click "#classification-computer_science"

3. Restrict to past 12 months

actionbook browser click "#date-filter_by-1"

4. Show abstracts in results

actionbook browser click "#abstracts-0"

5. Submit search

actionbook browser click "button:has-text('Search'):nth(2)"

6. Extract results

actionbook browser text "#main-container"

Example: Search by title keywords in a date range:

actionbook browser open "https://arxiv.org/search/advanced"

Search in "Title" field

actionbook browser click "#terms-0-field" actionbook browser click "option[value='title']" actionbook browser type "#terms-0-term" "large language model agent"

Date range: 2025-01 to 2026-02

actionbook browser click "#date-filter_by-3" actionbook browser type "#date-from_date" "2025-01-01" actionbook browser type "#date-to_date" "2026-02-09"

Submit and extract

actionbook browser click "button:has-text('Search'):nth(2)" actionbook browser text "#main-container"

Step 4: Supplement with Google / Bing Search

After arXiv, use Google/Bing to find non-academic sources (blogs, news, docs, code, discussions):

Search via Google

actionbook browser open "https://www.google.com/search?q=&#x3C;encoded_query>" actionbook browser text "#search"

Or search via Bing

actionbook browser open "https://www.bing.com/search?q=&#x3C;encoded_query>" actionbook browser text "#b_results"

Parse the search results to extract URLs and snippets. Collect the top 5-10 most relevant URLs. For each discovered URL, query Actionbook API (Step 2 pattern) to check if the site is indexed before visiting.

Step 5: Deep Read Sources

For each relevant URL, first query Actionbook API (same as Step 2) to check if the site is indexed, then use verified selectors:

actionbook browser open "<url>" actionbook browser text # Full page text (fallback) actionbook browser text "<selector>" # Use Actionbook selector if indexed

For arXiv papers, try sources in this order (newer papers often fail on ar5iv):

1. Try ar5iv first (best structured selectors from Actionbook)

actionbook browser open "https://ar5iv.org/html/&#x3C;arxiv_id>" actionbook browser text "h1.ltx_title_document" # Title actionbook browser text "div.ltx_authors" # Authors actionbook browser text "div.ltx_abstract" # Abstract

NOTE: section.ltx_section often fails on newer papers — use "article" as fallback

2. If ar5iv content is truncated (<5KB), fall back to arxiv abstract + other sources

actionbook browser open "https://arxiv.org/abs/&#x3C;arxiv_id>" actionbook browser text "main"

3. Supplement with HuggingFace model cards and GitHub READMEs for full details

actionbook browser open "https://huggingface.co/papers/&#x3C;arxiv_id>" actionbook browser text "main"

Key lesson: Don't rely solely on ar5iv. Always cross-reference 3-4 sources for completeness.

For Google Scholar (indexed by Actionbook):

actionbook browser open "https://scholar.google.com"

Type into search: use selector #gs_hdr_tsi

actionbook browser click "#gs_hdr_tsi"

... type query, click #gs_hdr_tsb to search

For unindexed sites, use snapshot to discover page structure:

actionbook browser open "<url>" actionbook browser snapshot # Get accessibility tree to find selectors actionbook browser text "<discovered_selector>"

Step 6: Synthesize Findings

Organize collected information into a coherent report:

• Overview / Executive Summary

• Key Findings

• Detailed Analysis

• Supporting Data / Evidence

• Implications / Significance

• Sources

Step 7: Generate json-ui JSON Report

Write a JSON file following the @actionbookdev/json-ui schema. Use the Write tool.

Output path: ./output/<topic-slug>.json (or user-specified --output path)

Step 8: Render HTML

CRITICAL: You MUST try ALL fallback methods before giving up. Do NOT stop at the first failure.

IMPORTANT: Always use ABSOLUTE paths for JSON_FILE and HTML_FILE. Relative paths break when git rev-parse returns an absolute repo root.

Try each method one by one until one succeeds:

Method 1: npx (recommended — works anywhere if npm is available)

npx @actionbookdev/json-ui render /absolute/path/to/report.json -o /absolute/path/to/report.html

Method 2: Global install (if user ran: npm install -g @actionbookdev/json-ui)

json-ui render /absolute/path/to/report.json -o /absolute/path/to/report.html

Method 3: Monorepo local path (fallback if inside actionbook project)

node "$(git rev-parse --show-toplevel)/packages/json-ui/dist/cli.js" render /absolute/path/to/report.json -o /absolute/path/to/report.html

NEVER give up silently. If all methods fail, tell the user:

• The JSON report is saved at <path>

• To install the renderer, run: npm install -g @actionbookdev/json-ui

Step 9: Open in Browser

macOS

open <report.html>

Linux

xdg-open <report.html>

Step 10: Close Browser

Always close the browser when done:

actionbook browser close

json-ui Report Template

IMPORTANT: Always include BrandHeader and BrandFooter.

{ "type": "Report", "props": { "theme": "auto" }, "children": [ { "type": "BrandHeader", "props": { "badge": { "en": "Deep Research Report", "zh": "深度研究报告" }, "poweredBy": "Actionbook" } }, { "type": "Section", "props": { "title": { "en": "Overview", "zh": "概述" }, "icon": "paper" }, "children": [ { "type": "Prose", "props": { "content": { "en": "English overview...", "zh": "中文概述..." } } } ] }, { "type": "Section", "props": { "title": { "en": "Key Findings", "zh": "核心发现" }, "icon": "star" }, "children": [ { "type": "ContributionList", "props": { "items": [ { "badge": { "en": "Finding", "zh": "发现" }, "title": { "en": "...", "zh": "..." }, "description": { "en": "...", "zh": "..." } } ] } } ] }, { "type": "Section", "props": { "title": { "en": "Detailed Analysis", "zh": "详细分析" }, "icon": "bulb" }, "children": [ { "type": "Prose", "props": { "content": { "en": "...", "zh": "..." } } } ] }, { "type": "Section", "props": { "title": { "en": "Key Metrics", "zh": "关键指标" }, "icon": "chart" }, "children": [ { "type": "MetricsGrid", "props": { "metrics": [], "cols": 3 } } ] }, { "type": "Section", "props": { "title": { "en": "Sources", "zh": "信息来源" }, "icon": "link" }, "children": [ { "type": "LinkGroup", "props": { "links": [] } } ] }, { "type": "BrandFooter", "props": { "timestamp": "YYYY-MM-DDTHH:MM:SSZ", "attribution": "Powered by Actionbook", "disclaimer": { "en": "This report was generated by AI using web sources. Verify critical information independently.", "zh": "本报告由 AI 基于网络来源生成，请独立验证关键信息。" } } } ] }

Paper Report Template (for arXiv papers)

When analyzing academic papers, use a richer template with:

• PaperHeader (title, arxivId, date, categories)

• AuthorList (authors with affiliations)

• Abstract (with keyword highlights)

• ContributionList (key contributions)

• MethodOverview (step-by-step method)

• ResultsTable (experimental results)

• Formula (key equations, LaTeX)

• Figure (paper figures from ar5iv)

Available json-ui Components

Component Use For Key Props

BrandHeader

Report header badge , poweredBy

PaperHeader

Paper metadata title , arxivId , date , categories

AuthorList

Authors authors: [{name, affiliation}] , maxVisible

Section

Major section title , icon (paper/star/bulb/chart/code/link/info/warning)

Prose

Rich text content (supports bold, italic, code , lists)

Abstract

Abstract text text , highlights: ["keyword"]

ContributionList

Numbered findings items: [{badge, title, description}]

MethodOverview

Step-by-step steps: [{step, title, description}]

MetricsGrid

Key stats metrics: [{label, value, trend, suffix}] , cols

ResultsTable

Data table columns , rows , highlights: [{row, col}]

Table

Generic table columns: [{key, label}] , rows , striped , compact

Callout

Info/tip/warning type (info/tip/warning/important/note), title , content

Highlight

Blockquote type (quote/important/warning/code), text , source

KeyPoint

Key finding card icon , title , description , variant

CodeBlock

Code snippet code , language , title , showLineNumbers

Formula

LaTeX equation latex , block , label

Figure

Image(s) images: [{src, alt, width}] , label , caption

Image

Single image src , alt , caption , width

DefinitionList

Term/definition items: [{term, definition}]

LinkGroup

Source links links: [{href, label, icon}]

Grid

Grid layout cols , children

Card

Card container padding (sm/md/lg), shadow

TagList

Tags tags: [{label, color, href}]

BrandFooter

Footer timestamp , attribution , disclaimer

json-ui Known Pitfalls

Pitfall Symptom Fix

MetricsGrid.suffix as i18n object text.replace is not a function

suffix must be a plain string, not { "en": ..., "zh": ... }

MetricsGrid.value as number Render error value must be a string (e.g., "58.5" not 58.5 )

Missing BrandHeader /BrandFooter

Report looks broken Always include both

Table row values as i18n object [object Object] in cells Row cell values must be plain strings. Column label supports i18n, but row data does not. Use "Runtimes / 运行时" instead of { "en": "Runtimes", "zh": "运行时" }

Very long Prose content Truncated render Split into multiple Prose blocks or use subsections

i18n Support

All text fields support bilingual output unless noted above:

{ "en": "English text", "zh": "中文文本" }

For --lang en , use plain strings. For --lang zh , use plain Chinese strings. For --lang both (default), use i18n objects.

Exceptions:

• MetricsGrid props value and suffix must always be plain strings.

• Table row cell values must be plain strings (column label supports i18n, but row data does not). For bilingual, use "English / 中文" format.

Academic Paper Support

arXiv Papers

ar5iv.org HTML (preferred for reading, but often incomplete for papers < 3 months old):

Element Selector (Actionbook-verified) Reliability Fallback

Title h1.ltx_title_document

High div.ltx_abstract includes title context

Authors div.ltx_authors

High —

Abstract div.ltx_abstract

High —

Full article article

Medium Use when section selectors fail

Sections section.ltx_section

Low on new papers article for all content

Section title h2.ltx_title_section

Low on new papers Parse from article text

Figures figure.ltx_figure

Medium —

Tables table.ltx_tabular

Medium —

Bibliography .ltx_bibliography

Medium —

Note: For papers submitted within the last ~3 months, ar5iv often renders incomplete content. Always check actionbook browser text 2>&1 | wc -c — if < 5KB, the page didn't fully render. Fall back to other sources.

arXiv API (for metadata via actionbook browser):

actionbook browser open "http://export.arxiv.org/api/query?id_list={arxiv_id}" actionbook browser text

Recommended Source Priority for Papers

Based on testing, use this priority order for maximum coverage:

Priority Source What you get Reliability

1 arxiv.org/abs/<id>

Abstract, metadata, submission history Very high

2 huggingface.co/papers/<id>

Abstract, community comments, related models/datasets Very high

3 GitHub repo (from search results) README with method details, model zoo, code High

4 HuggingFace model card Training recipe, benchmark results, quick start High

5 ar5iv.org/html/<id>

Full paper HTML with structured selectors Medium (fails on new papers)

6 Google Scholar / Semantic Scholar Citations, related work Medium

Key insight: Don't rely on a single source. The combination of arxiv abstract + HuggingFace + GitHub typically gives 90%+ of what you need, even when ar5iv fails.

Other Academic Sources

Use actionbook browser to visit and extract content from:

• Google Scholar (scholar.google.com ) — Actionbook indexed, use #gs_hdr_tsi for search

• Semantic Scholar (semanticscholar.org )

• Papers With Code (paperswithcode.com )

• Conference proceedings sites

Error Handling

Error Action

Browser fails to open Run actionbook browser status , retry

Page load timeout (30s) Skip source, try next. Common on papers.cool, slow academic sites

ar5iv content truncated (<5KB) Paper too new for ar5iv. Fall back to arxiv abstract + HuggingFace + GitHub

section.ltx_section not found ar5iv rendering incomplete. Use actionbook browser text "article" or "main" instead

Actionbook selector not found Use actionbook browser snapshot to discover actual page structure

actionbook search returns no results Site not indexed. Use actionbook browser snapshot to find selectors manually

json-ui render crash (text.replace ) Check MetricsGrid suffix /value — must be plain strings, not i18n objects

npx @actionbookdev/json-ui fails Run npm install -g @actionbookdev/json-ui and retry with json-ui render . If still fails, try monorepo local path

No search results Broaden search terms, try different angles

Render failed Save JSON, tell user path, and suggest: npm install -g @actionbookdev/json-ui

IMPORTANT: Always run actionbook browser close before finishing, even on errors.

Quality Guidelines

• Breadth: Research from at least 3-5 diverse sources

• Depth: Read full articles, not just snippets

• Accuracy: Cross-reference facts across sources

• Structure: Use appropriate json-ui components for each content type

• Attribution: Always include source links in the report

• Freshness: Prefer recent sources when relevance is equal

Chinese Content Quality Guidelines (中文内容质量规范)

CRITICAL: Chinese text must be written as native Chinese, NOT translated from English.

The zh field is not a translation — it is an independent Chinese version of the content. Write it as if authoring a Chinese tech article from scratch.

Common Problems to Avoid

Problem Bad Example Good Example

被动语态过多 "Wasm 已被广泛采用" "Wasm 已经大规模落地"

英语语序直译 "广泛但常常不可见地被采用" "已经深入渗透到各类产品中，只是用户浑然不觉"

生硬术语拼接 "语言无关的异步通信" "不绑定特定编程语言的异步通信机制"

直译英文短语 "关键缺失部分" "最后一块拼图"

"地道"当形容词 "地道绑定" "符合各语言习惯的绑定"

逗号长句（一逗到底） "A，B，C，D，E。" 拆成 2-3 个短句

引用原文硬翻 "「许多用户并未意识到它正在被使用」" 用中文重新表述引用的核心意思，必要时保留原文人名

Writing Rules

• 先写中文，再对照英文查漏：不要从英文逐句翻译。先理解内容本质，用中文重新组织表达。

• 主动语态优先：中文天然偏好主动句。"X 被 Y 采用" → "Y 采纳了 X" 或 "Y 已在用 X"。

• 短句 > 长句：中文读者偏好短句。超过 40 字的句子应拆分。

• 术语处理原则：

• 有公认中文译名的术语用中文：服务器 → 服务器、容器 → 容器、沙箱 → 沙箱

• 无公认译名的术语保留英文：Component Model、WASI、SIMD、Cold Start

• 首次出现时中英对照：「组件模型（Component Model）」，之后可只用中文

• 不要硬造中文译名（如"非干扰分析"，应写"Noninterference 分析"或解释性表述）

• 数据和事实保持一致：中英文的数字、日期、引用来源必须完全一致。

• 语气自然：可以用"说白了"、"换句话说"、"简单来说"等口语化连接词，避免全篇书面腔。

• 标点规范：用中文标点（，。；：「」）而非英文标点。引号优先用「」。

Table & Short Text Rules (表格与短文本规则)

Table 行中的 "English / 中文" 格式是中文质量的重灾区。短文本更容易暴露机翻痕迹。

规则：中文部分必须是独立的中文表述，不是英文的逐词翻译。

Bad Why Bad Good

"应用级错误处理" 太生硬，像翻译腔 "应用层统一报错"

"高性能 Web 框架" 可以，但太平淡 "主打性能的 Web 框架"

"序列化框架" 可以接受 "序列化框架" ✓

"嵌入式异步执行器" 太绕 "嵌入式异步运行器"

"凸块分配器" 硬造译名，没人这么说 "Bump 分配器（arena 风格）"

"安全内存擦除" 太书面 "安全清零内存"

"多生产者多消费者通道" 太长 "MPMC 通道"

"类 React UI 框架" OK "类 React 的 UI 框架" ✓

表格中文短文本的核心原则：

• 保留英文术语：Bump allocator、MPMC、ECS 这些没有公认中文译名的概念，直接保留英文。别硬翻。

• 口语化 > 书面化：短文本读者扫一眼就过，要像说话一样自然。"主打性能" 比 "性能优化的" 好。

• 别逐词翻译：先理解英文描述的意思，然后用中文重新说一遍。"Full-featured datetime" → "功能齐全的时间日期库"，不是 "全功能日期时间"。

• 可以省略冗余修饰：英文 "A generic serialization/deserialization framework" → 中文只需 "序列化框架"。

• 数量控制在 10 字以内：表格中文描述尽量控制在 10 个汉字以内，简短精准。

Bilingual Tone Reference

English Tone Chinese Equivalent Example

"X has reached a critical milestone" 不要直译"关键里程碑" "X 迎来了重要转折点" 或 "X 进入成熟期"

"This is the key missing piece" 不要直译"关键缺失部分" "这是最后一块拼图" 或 "补上了最关键的短板"

"excels at cold starts" 不要直译"在冷启动方面表现卓越" "冷启动速度远超同类方案"

"security scrutiny intensifies" 不要直译"安全审查加强" "安全领域的关注度持续升温"

"emerging as a standard" 不要直译"正成为标准" "逐渐确立了标准地位" 或 "大有一统江湖之势"

"widespread but invisible adoption" 不要直译"广泛但不可见的采用" "已经悄然渗透到各类产品中"