news-push

name	news-push
description	Generate a local news briefing from RSS / OPML subscriptions. Use when the user asks to 根据 RSS、订阅源或 OPML 生成新闻简报、新闻早报、每日摘要、本地 Markdown/HTML 报告，管理 RSS 订阅，或运行一个带本地工作台的新闻工作流； prefer this skill for feed-based briefings, not generic web news search.

IRON LAW: Only use a local HTTP workspace bound to 127.0.0.1 when browser interaction is needed. No external web deployment, no remote API backend, and no third-party runtime dependencies — all scripts use only Node.js built-in modules.

News Push

Natural Language Triggers

Use this skill when the user asks for a briefing built from their feeds, subscriptions, or OPML, for example:

• “根据我的 RSS 订阅生成今天的新闻简报”
• “把订阅源整理成一份本地 Markdown 新闻摘要”
• “生成一份按领域分类的新闻早报”
• “帮我把这个 RSS 加进 OPML，然后重新生成简报”
• “用本地订阅源输出一个 HTML 新闻报告”

Do not prefer this skill when the user is asking for generic latest news from the web with no RSS / subscription / OPML context.

Purpose

Turn RSS subscriptions into a lightweight local briefing with:

• 简报 — global brief + domain briefs (dynamically classified)
• 重要事实 — source-attributed facts
• 关键观点 — key opinions
• 原始订阅信息流 — raw feed articles

You (Claude) ARE the AI engine. You generate the briefing JSON using your own capabilities. The scripts only handle data fetching and rendering — no external AI API calls.

Architecture

Claude runs a stable CLI entrypoint:
    ↓
bin/news-push --workspace "$PWD"
    ↓
cli/main.mjs
    ↓
prepare phase:
  scripts/sync-feeds.mjs
  scripts/sync-extras.mjs
  scripts/apply-profile.mjs (optional)
  scripts/fetch-content.mjs
  scripts/preprocess-articles.mjs
    ↓
workspace/.news-push/data/articles-titles.txt
workspace/.news-push/ui-state.json  (waiting_for_ai)
    ↓
cli/main.mjs ensures local workspace server
  scripts/server.mjs  → http://127.0.0.1:<port>/
    ↓
Browser shows raw feed + “AI 正在进行总结和提炼”
    ↓
Claude reads titles + generates analysis JSON
    ↓
workspace/.news-push/data/analysis.json
    ↓
bin/news-push --workspace "$PWD"   (same command, second pass)
    ↓
finalize phase:
  scripts/gen-briefing.mjs
  scripts/render-md.mjs / scripts/render-html.mjs
  scripts/send-email.mjs (optional)
  scripts/manage-opml.mjs (feeds subcommands)
    ↓
workspace/.news-push/ui-state.json  (completed)
    ↓
Browser auto-refreshes into final HTML briefing

File Locations

• {baseDir}/feeds.opml — bundled default subscription template (read-only seed)
• {workspace}/.news-push/feeds.opml — user/workspace subscription source-of-truth
• {workspace}/.news-push/data/articles.json — fetched article cache
• {workspace}/.news-push/data/articles-titles.txt — one title per line for AI analysis
• {workspace}/.news-push/data/analysis.json — Claude-generated analysis JSON
• {workspace}/.news-push/output/latest.html — generated HTML report
• {workspace}/.news-push/output/latest.md — generated Markdown report
• {workspace}/.news-push/output/archive/ — timestamped snapshots
• {workspace}/.news-push/.env — email sending config (SMTP_HOST, SMTP_PORT, SMTP_USER, SMTP_PASS, EMAIL_TO)
• {workspace}/.news-push/ui-state.json — browser workspace state (waiting_for_ai, completed, failed)
• {workspace}/.news-push/server-state.json — local server metadata (pid, port, url)

Authorization-Friendly Runtime

To reduce repeated permission prompts, prefer the stable CLI entrypoint instead of calling many scripts one by one.

• Always execute bin/news-push from the skill directory
• Always pass --workspace "$PWD" so runtime files stay in the user's current project
• Prefer calling the same default news-push command twice around the AI analysis step, rather than issuing a long chain of shell commands
• Let news-push itself start or reuse the local workspace server; do not manually launch many helper commands unless the user explicitly asks
• Do not call scripts/server.mjs directly for normal usage; always let bin/news-push --workspace "$PWD" own the runtime root and server lifecycle
• Do not write runtime artifacts back into the installed skill directory unless the user explicitly asks for that behavior

Workflow

Default: /news-push (browser workspace + local files)

1. Run node {baseDir}/bin/news-push --workspace "$PWD"
2. Let the CLI finish prepare, then open or reuse the local browser workspace at http://127.0.0.1:<port>/
3. The browser page should now show the raw feed plus a status banner such as “AI 正在进行总结和提炼” If the command says the raw content is ready but the workspace is still recovering, run the same node {baseDir}/bin/news-push --workspace "$PWD" command again. Do not start AI analysis until the command prints a 工作台: URL for the current workspace.
4. Read {workspace}/.news-push/data/articles-titles.txt
5. If {workspace}/.news-push/data/focus.yaml exists and has a preference field, read it and apply the user's natural language preferences during analysis (see Focus Integration below)
6. Generate an analysis object using your own AI capabilities (see schema below)
7. Prefer writing that object through node {baseDir}/scripts/write-analysis.mjs --workspace "$PWD" --from-module {workspace}/.news-push/data/analysis-source.mjs
8. This helper must serialize with JSON.stringify and validate before writing {workspace}/.news-push/data/analysis.json; do not hand-write a raw JSON file if you can avoid it
9. Run the same command again: node {baseDir}/bin/news-push --workspace "$PWD"
10. The browser workspace will auto-refresh into the final HTML report; also mention the generated local file paths to the user

Important freshness rule:

• Once a run has completed, the next /news-push invocation must start a new prepare phase.
• Do not reuse a previous run's analysis.json just because it still exists on disk.
• The only time the same command should finalize is when the current workspace state is explicitly waiting for AI analysis from the current prepare phase.
• If a prepare phase already completed and the workspace is still waiting_for_ai, rerunning the same /news-push command should first resume/open the browser workspace instead of refetching the feeds.

Important stability rule:

• Prefer authoring analysis as a JS object module such as {workspace}/.news-push/data/analysis-source.mjs
• Then call scripts/write-analysis.mjs so the final file is always produced by JSON.stringify
• If validation fails, fix the source object/module and rerun the writer; do not manually patch the broken JSON text blindly

HTML output

Same flow, but use node {baseDir}/bin/news-push html --workspace "$PWD". The browser workspace still opens after prepare and refreshes after finalize; the final local artifact is {workspace}/.news-push/output/latest.html.

Email output (auto-send)

If .env is configured with SMTP_HOST, SMTP_USER, SMTP_PASS, and EMAIL_TO, the email sending step is appended automatically after rendering:

Run node {baseDir}/bin/news-push finalize --workspace "$PWD" --format html --email

If .env does not exist or lacks required variables, skip silently (no error, no prompt).

Both formats

Use node {baseDir}/bin/news-push both --workspace "$PWD" before and after the analysis step.

Local workspace server

• Manual start: node {baseDir}/bin/news-push serve --workspace "$PWD"
• Config page: node {baseDir}/bin/news-push serve --workspace "$PWD" --page /config
• Keep the server local-only on 127.0.0.1

Manage subscriptions

• List: node {baseDir}/bin/news-push feeds list --workspace "$PWD"
• Add: node {baseDir}/bin/news-push feeds add "Feed Name" "https://example.com/feed.xml" --workspace "$PWD"
• Remove: node {baseDir}/bin/news-push feeds remove "Feed Name" --workspace "$PWD"

Analysis JSON Schema (AI output)

The AI reads articles-titles.txt (one title per line) and generates an analysis JSON with only the analytical content. Metadata (raw_articles, coverage, timestamps) is assembled by the gen-briefing script.

{
  "global_brief": "1-3 sentence summary of the most important developments",
  "domain_briefs": {
    "<domain_key>": "domain summary (1-2 sentences)",
    "...": "2-5 domains, dynamically chosen based on actual content"
  },
  "highlight_facts": [
    {
      "title": "Concise fact title",
      "summary": "Fact with inline source attribution, e.g. 「OpenAI released o3 (OpenAI Blog, The Verge)」",
      "domain": "<domain_key>",
      "score": 9.0
    }
  ],
  "highlight_opinions": [
    {
      "title": "Opinion title",
      "summary": "Opinion summary with attribution",
      "domain": "<domain_key>",
      "score": 7.0
    }
  ],
  "title_translations": {
    "English Title Here": "这里是对应的中文翻译",
    "Another English Title": "另一个中文翻译"
  }
}

Domain Classification Rules

Domain keys are dynamic — you decide the domains based on actual article content. Use English keys, render scripts will map to Chinese labels.

Common domain keys (not exhaustive, add as needed): ai, finance, politics, tech, society, health, entertainment, sports, science, security.

Rules:

1. Choose 2-5 domains that best cover the current batch of articles. Skip domains with no significant content.
2. Use lowercase English keys (e.g. ai, not AI).
3. Every fact/opinion must have a domain matching one of the domain_briefs keys, or general.

Generation Rules

1. Distinguish facts from opinions. Facts = verifiable events. Opinions = analysis/commentary.
2. Translate English titles. Add a title_translations field mapping English article titles → Chinese. Skip titles already in Chinese. Keep translations concise (suitable for scanning, not literary translation). Example: "From 300KB to 69KB per Token: How LLM Architectures Solve the KV Cache Problem" → "从 300KB 到 69KB：LLM 架构如何解决 KV 缓存问题".

Focus Integration

If {workspace}/.news-push/data/focus.yaml exists and has a non-empty preference field, read it before generating analysis. The preference is a natural language string — interpret it holistically to decide which articles to prioritize, deprioritize, or highlight.

Apply these rules:

• Read the preference text and infer what domains, topics, and keywords the user cares about most.
• When selecting highlight items, weight articles that match the inferred priorities higher. Articles matching deprioritized topics should only appear if truly groundbreaking.
• When the user mentions specific companies, products, or events (e.g. "OpenAI", "Anthropic", "product launches"), treat those as boost keywords.
• When the user mentions topics they don't care about (e.g. "不太关心汽车"), treat those as ignore keywords.
• If focus.yaml does NOT exist or has an empty preference, proceed with neutral/default weighting — no bias.

Focus tagging: When an item in domain_briefs matches the user's inferred priorities from the preference text, prepend 【focus_on】 before the relevant text. For example: 【focus_on】Anthropic Claude 新模型曝光；普通新闻.... Use only the fixed tag 【focus_on】 — no other tag names. The render scripts apply underline styling to text following this exact tag and strip the tag itself from output.

2. Merge facts about the same event into one entry. Do not merge opinions.
3. Inline source attribution in summaries: 「fact（Source A, Source B）」 — you only see titles (no source names), so infer from content context (e.g. "IT之家", "Ars Technica", "TechCrunch").
4. Be conservative. If something cannot be confirmed, mark it as 「仍待确认」.
5. Max 6 items each for highlight_facts and highlight_opinions.
6. Score each item 1-10 based on importance and reliability.
7. Output JSON only, no Markdown wrapping.
8. No metadata — raw_articles, coverage, timestamps are assembled by the gen-briefing script, not by you.

Profiles

Select a profile to focus the briefing on specific domains:

node {baseDir}/bin/news-push prepare --workspace "$PWD" --profile ai
node {baseDir}/bin/news-push --workspace "$PWD" --profile ai

Available profiles in {baseDir}/profiles/:

Profile	Focus	Non-RSS Extras
general	综合早报 (全部源)	All extras enabled
tech	科技早报 (HN, GitHub, Dev.to, PH)	GitHub Trending, HF Papers, V2EX
ai	AI 深度 (arXiv, HF, OpenAI, DeepMind)	GitHub Trending, HF Papers
finance	财经早报 (华尔街见闻, 36Kr, 腾讯)	微博热搜, 华尔街见闻
social	吃瓜早报 (微博, V2EX, 知乎)	微博热搜, V2EX 热门

When a profile is active, the runtime workspace writes {workspace}/.news-push/data/articles-filtered.json. Subsequent pipeline steps use that file instead of articles.json.

Guardrails

• Confirm before deleting subscriptions
• Keep generated artifacts local and directly openable
• Facts stay conservative and source-attributed
• Keep the raw feed visible in time order
• Only bind the workspace server to 127.0.0.1
• Do not introduce any remote backend, hosted API, or external web deployment

Anti-Patterns

• Calling external AI APIs (you ARE the AI)
• Exposing the local workspace server beyond localhost
• Asking the user to type long flag-heavy commands
• Hiding the raw feed behind summaries only
• Writing output only to stdout without saving a durable file
• Introducing npm dependencies