actionbook/deep-research

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=<encoded_query>"
actionbook browser text "#search"

# Or search via Bing
actionbook browser open "https://www.bing.com/search?q=<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/<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/<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/<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:

1. Overview / Executive Summary
2. Key Findings
3. Detailed Analysis
4. Supporting Data / Evidence
5. Implications / Significance
6. 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:

1. The JSON report is saved at <path>
2. 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

1. Breadth: Research from at least 3-5 diverse sources
2. Depth: Read full articles, not just snippets
3. Accuracy: Cross-reference facts across sources
4. Structure: Use appropriate json-ui components for each content type
5. Attribution: Always include source links in the report
6. 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

1. 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。
2. 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"。
3. 短句 > 长句：中文读者偏好短句。超过 40 字的句子应拆分。
4. 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）
5. 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。
6. 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。
7. 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：

1. 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。
2. 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。
3. 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"。
4. 可以省略冗余修饰：英文 "A generic serialization/deserialization framework" → 中文只需 "序列化框架"。
5. 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" |