* feat: skills i18n 改造 — schemaVersion 1.1,零向后兼容
把 21 个 skills + 1 个 agent + manifest/categories 全量迁移到 schemaVersion 1.1
的 i18n 结构,配套 CI AI 翻译流水线(GitHub Models)与本地工具链。
## 关键变更
### 数据结构(破坏性,schemaVersion 1.0 → 1.1)
- SKILL.md: 顶层 name 改为 ASCII slug(== 目录名,符合 agentskills.io 规范);
中文显示名/short_desc/description 全部迁入 metadata.i18n.<locale>
- agents/<id>/agent.json: shortDesc/fullDesc/tags/persona.{role,traits} 迁入
i18n.<locale>;changelog[].changes 改为 { <locale>: string[] } 对象
- categories.json: 每个分类的 label/description 迁入 i18n.<locale>,顶层只剩
color/icon
- manifest.json: 加 supportedLocales / defaultLocale;顶层 description 迁入
i18n.<locale>
### Body 文件结构
- 根 SKILL.md = frontmatter + default_locale (en-US) body
- SKILL.<locale>.md = 各 locale 的 markdown body(首行 <!-- locale: xx --> 自校验)
### 工具链(scripts/i18n/)
- glossary.json: zh→en 术语表 + do_not_translate 白名单
- schema/skill-frontmatter.schema.json: i18n frontmatter JSON Schema
- validate-i18n.py: 8 条校验规则(name 合规 / locale 完整性 / hash 一致性等)
- translate.py: GitHub Models / Anthropic 双 backend,sha256 增量翻译
- migrate.py: 一次性迁移脚本(旧格式 → i18n 结构)
### CI(.github/workflows/)
- i18n-validate.yml: PR 触发跑 validate + translate --check
- i18n-translate.yml: PR 触发用 GitHub Models(默认 openai/gpt-5-mini)翻译缺失
locale,自动追加 commit;可切到 ANTHROPIC_API_KEY 走 Claude
### 文档
- docs/I18N.md: 作者贡献指南(schema 说明 / 提交流程 / 常见问题)
- README.md: 加多语言段落
## 验证
- uv run scripts/i18n/validate-i18n.py: OK,49 文件 0 错误
- uv run scripts/i18n/translate.py --check: 0 stale locale
- 21 skills 标题数 zh-CN == en-US 严格对齐(最大 66=66)
- skills-ref 规范校验:全部通过(顶层 name ASCII slug + description 单字段)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(i18n): 修复 PR #1 review 反馈的 6 项问题
- schema: translated_by 正则放宽为 ^(human|ai:[A-Za-z0-9._:/-]+)$,接受
'ai:github:openai/gpt-5-mini' 这类 backend:model 形式(CI 翻译输出格式)
- README + docs/I18N.md: 修正"CI 用 Claude API"误导描述,正确说明默认是
GitHub Models(openai/gpt-5-mini)+ GITHUB_TOKEN,可选切到 Anthropic
- skills/minimax-tts/SKILL.md & SKILL.zh-CN.md: 删除多余的 ``` 闭合,避免
Markdown 后续渲染错乱
- skills/docx/SKILL.md: 翻译时丢失的 • Unicode escape 示例已恢复,
与 zh-CN 版本对齐
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
12 KiB
web-access 技能
L0:一句话摘要
三层联网访问工具包——搜索公开页面、Jina 优化抓取、CDP 登录态浏览器访问。
L1:概述与使用场景
能力描述
web-access 是一个流程型技能(Procedural Skill),提供三层互补的联网访问能力:Layer 1(WebSearch + WebFetch)用于公开页面;Layer 2(Jina Reader)用于 JS 渲染的重页面,默认节省 Token;Layer 3(Chrome CDP)用于需要登录态的站点(小红书/B站/微博/飞书/Twitter)。
使用场景
- 用户需要搜索当前信息或研究特定主题
- 用户需要抓取公开网页内容或技术文档
- 用户需要访问登录态站点(小红书、B站、微博、飞书、Twitter 等)
- 用户需要对比产品、聚合新闻或调查 API/库版本
核心价值
- 三层递进:从轻量搜索到重度 JS 渲染到登录态访问,按需选择
- Token 优化:Jina Reader 默认减少 50-80% Token 消耗
- 登录态复用:通过 CDP 连接用户已登录的 Chrome,无需重复登录
L2:详细规范
Output Rule
When you complete a research task, you MUST cite all source URLs in your response. Distinguish between:
- Quoted facts: directly from a fetched page → cite the URL
- Inferences: your synthesis or analysis → mark as "(分析/推断)"
If any fetch fails, explicitly tell the user which URL failed and which fallback you used.
Prerequisites: Chrome CDP Setup (for login-gated sites)
Only required when accessing sites that need the user's login session (小红书/B站/微博/飞书/Twitter/知乎/公众号).
One-time setup
Launch a dedicated Chrome instance with remote debugging enabled:
macOS:
/Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome \
--remote-debugging-port=9222 \
--user-data-dir="$HOME/.desirecore/chrome-profile"
Linux:
google-chrome \
--remote-debugging-port=9222 \
--user-data-dir="$HOME/.desirecore/chrome-profile"
Windows (PowerShell):
& "C:\Program Files\Google\Chrome\Application\chrome.exe" `
--remote-debugging-port=9222 `
--user-data-dir="$env:USERPROFILE\.desirecore\chrome-profile"
After launch:
- Manually log in to the sites you need (小红书、B站、微博、飞书 …)
- Leave this Chrome window open in the background
- Verify the debug endpoint:
curl -s http://localhost:9222/json/versionshould return JSON
Verify CDP is ready
Before any CDP operation, always run:
curl -s http://localhost:9222/json/version | python3 -c "import sys,json; d=json.load(sys.stdin); print('CDP ready:', d.get('Browser'))"
If the command fails, tell the user: "请先启动 Chrome 并开启远程调试端口(见 web-access 技能的 Prerequisites 部分)。"
Tool Selection Decision Tree
User intent
│
├─ "Search for information about X" (no specific URL)
│ └─→ WebSearch → pick top 3-5 results → fetch each (see next branches)
│
├─ "Read this public page" (static HTML, docs, news)
│ └─→ WebFetch(url) directly
│
├─ "Read this heavy-JS page" (SPA, React/Vue sites, Medium, etc.)
│ └─→ Bash: curl -sL "https://r.jina.ai/<original-url>"
│ (Jina Reader = default for JS-rendered content, saves tokens)
│
├─ "Read this login-gated page" (小红书/B站/微博/飞书/Twitter/知乎/公众号)
│ └─→ 1. Verify CDP ready (curl http://localhost:9222/json/version)
│ 2. Bash: python3 script with playwright.connect_over_cdp()
│ 3. Extract content → feed to Jina Reader for clean Markdown
│ (or use BeautifulSoup directly on the raw HTML)
│
├─ "API documentation / GitHub / npm package info"
│ └─→ Prefer official API endpoints over scraping HTML:
│ - GitHub: gh api repos/owner/name
│ - npm: curl https://registry.npmjs.org/<pkg>
│ - PyPI: curl https://pypi.org/pypi/<pkg>/json
│
└─ "Real-time interactive task" (click, fill form, scroll, screenshot)
└─→ CDP + Playwright (see references/cdp-browser.md)
Three-layer strategy summary
| Layer | Use case | Primary tool | Token cost |
|---|---|---|---|
| L1 | Public, static | WebFetch |
Low |
| L2 | JS-heavy, long articles, token savings | Bash curl r.jina.ai |
Lowest (Markdown pre-cleaned) |
| L3 | Login-gated, interactive | Bash + Python Playwright CDP |
Medium (raw HTML, then clean via Jina or BS4) |
Default priority: L1 for simple public pages → L2 for anything heavy → L3 only when login is required.
Supported Sites Matrix
| Site | Recommended Layer | Notes |
|---|---|---|
| Wikipedia, MDN, official docs | L1 WebFetch | Static, clean HTML |
| GitHub README, issues, PRs | gh api (best) → L1 WebFetch |
Prefer API |
| Hacker News, Reddit | L1 WebFetch | Public content |
| Medium, Dev.to | L2 Jina Reader | JS-rendered, member gates |
| Twitter/X | L3 CDP (or L2 Jina with x.com) |
Login required for full thread |
| 小红书 (xiaohongshu.com) | L3 CDP | 强制登录 |
| B站 (bilibili.com) | L3 CDP | 视频描述/评论需登录 |
| 微博 (weibo.com) | L3 CDP | 长微博需登录 |
| 知乎 (zhihu.com) | L3 CDP | 长文+评论需登录 |
| 飞书文档 (feishu.cn) | L3 CDP | 必须登录 |
| 公众号 (mp.weixin.qq.com) | L2 Jina Reader | 通常公开,Jina 处理更干净 |
| L3 CDP | 登录墙 |
Tool Reference
Layer 1: WebSearch + WebFetch
WebSearch — discover URLs for an unknown topic:
WebSearch(query="latest typescript 5.5 features 2026", max_results=5)
Tips:
- Include the year for time-sensitive topics
- Use
allowed_domains/blocked_domainsto constrain
WebFetch — extract clean Markdown from a known URL:
WebFetch(url="https://example.com/article")
Tips:
- Results cached for 15 min
- Returns cleaned Markdown with title + URL + body
- If body < 200 chars or looks garbled → escalate to Layer 2 (Jina) or Layer 3 (CDP)
Layer 2: Jina Reader (default for heavy pages)
Jina Reader (r.jina.ai) is a free public proxy that renders pages server-side and returns clean Markdown. Use it as the default for any page where WebFetch produces garbled or truncated output, and as the preferred extractor for JS-heavy SPAs.
curl -sL "https://r.jina.ai/https://example.com/article"
Why Jina is the default token-saver:
- Strips nav/footer/ads automatically
- Handles JS-rendered SPAs
- Returns 50-80% fewer tokens than raw HTML
- No API key needed for basic use (~20 req/min)
See references/jina-reader.md for advanced endpoints and rate limits.
Layer 3: CDP Browser (login-gated access)
Use Python Playwright's connect_over_cdp() to attach to the user's running Chrome (which already has login cookies). No re-login needed.
Minimal template:
python3 << 'PY'
from playwright.sync_api import sync_playwright
TARGET_URL = "https://www.xiaohongshu.com/explore/..."
with sync_playwright() as p:
browser = p.chromium.connect_over_cdp("http://localhost:9222")
context = browser.contexts[0] # reuse user's default context (has cookies)
page = context.new_page()
page.goto(TARGET_URL, wait_until="domcontentloaded")
page.wait_for_timeout(2000) # let lazy content load
html = page.content()
page.close()
# Print first 500 chars to verify
print(html[:500])
PY
Extract text via BeautifulSoup (no Jina round-trip):
python3 << 'PY'
from playwright.sync_api import sync_playwright
from bs4 import BeautifulSoup
with sync_playwright() as p:
browser = p.chromium.connect_over_cdp("http://localhost:9222")
page = browser.contexts[0].new_page()
page.goto("https://www.bilibili.com/video/BV...", wait_until="networkidle")
html = page.content()
page.close()
soup = BeautifulSoup(html, "html.parser")
title = soup.select_one("h1.video-title")
desc = soup.select_one(".video-desc")
print("Title:", title.get_text(strip=True) if title else "N/A")
print("Desc:", desc.get_text(strip=True) if desc else "N/A")
PY
See references/cdp-browser.md for:
- Per-site selectors (小红书/B站/微博/知乎/飞书)
- Scrolling & lazy-load patterns
- Screenshot & form-fill recipes
- Troubleshooting connection issues
Common Workflows
Read references/workflows.md for detailed templates:
- 技术文档查询 (Tech docs lookup)
- 竞品对比研究 (Competitor research)
- 新闻聚合与时间线 (News aggregation)
- API/库版本调查 (Library version investigation)
Read references/cdp-browser.md for login-gated site recipes (小红书/B站/微博/知乎/飞书).
Read references/jina-reader.md for Jina Reader positioning, rate limits, and advanced endpoints.
Quick Workflow: Multi-Source Research
1. WebSearch(query) → 5 candidate URLs
2. Skim titles + snippets → pick 3 most relevant
3. Classify each URL by layer (L1 / L2 / L3)
4. Fetch all in parallel (single message, multiple tool calls)
5. If any fetch returns < 200 chars or garbled → retry via next layer
6. Synthesize: contradictions? consensus? outliers?
7. Report with inline [source](url) citations + a Sources list at the end
Anti-Patterns (Avoid)
- ❌ Using WebFetch on obviously heavy sites — Medium, Twitter, 小红书 will waste tokens or fail. Jump straight to L2/L3.
- ❌ Launching headless Chrome instead of CDP attach — loses user's login state, triggers anti-bot, slow cold start. Always use
connect_over_cdp()to attach to the user's existing session. - ❌ Fetching one URL at a time when you need 5 — batch in a single message.
- ❌ Trusting a single source — cross-check ≥ 2 sources for non-trivial claims.
- ❌ Fetching the search result page itself — WebSearch already returns snippets; fetch the actual articles.
- ❌ Ignoring the cache — WebFetch caches 15 min, reuse freely.
- ❌ Scraping when an API exists — GitHub, npm, PyPI, Wikipedia all have JSON APIs.
- ❌ Forgetting the year in time-sensitive queries — "best AI models" returns 2023 results; "best AI models 2026" returns current.
- ❌ Hardcoding login credentials in scripts — always rely on the user's pre-logged CDP session.
- ❌ Citing only after the fact — collect URLs as you fetch, not from memory afterwards.
Example Interaction
User: "帮我抓一下这条小红书笔记的内容:https://www.xiaohongshu.com/explore/abc123"
Agent workflow:
1. 识别 → 小红书是 L3 登录态站点
2. 检查 CDP:curl -s http://localhost:9222/json/version
├─ 失败 → 提示用户启动 Chrome 调试模式,终止
└─ 成功 → 继续
3. Bash: python3 connect_over_cdp 脚本 → page.goto(url) → page.content()
4. BeautifulSoup 提取 h1 title、.note-content、.comments
5. 返回给用户时:
- 引用原 URL
- 若内容很长,用 Jina 清洗一遍节省 token
6. 告知用户:「已通过你的登录态抓取,原链接:[xhs](url)」
Installation Note
CDP features require Python + Playwright installed:
pip3 install playwright beautifulsoup4
python3 -m playwright install chromium # only needed if user hasn't installed Chrome
If playwright is not installed when the user requests a login-gated site, run the install commands in Bash and explain you're setting up the browser automation dependency.