Ai News Radar logo

Ai News Radar

CommunityPopular
LearnPrompt
ai-news-radar

Use when working on AI News Radar, 24 小时 AI 更新雷达, AI 更新雷达, 伯乐Skill, or Scout Skill: finding high-signal AI/tech sources, adding RSS/OPML/GitHub feeds, checking source health, updating the web UI, GitHub Actions, or GitHub Pages deployment.

Overview

PublisherLearnPrompt
Repositoryai-news-radar
Skill nameai-news-radar
Stars
1.7K
Forks
428
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LearnPrompt on GitHub. Read the source before you install it.

Installation

Install the Ai News Radar AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LearnPrompt/ai-news-radar.git /tmp/ai-news-radar
mkdir -p .claude/skills
cp -r /tmp/ai-news-radar/skills/ai-news-radar .claude/skills/ai-news-radar
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai News Radar in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai News Radar on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai News Radar is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AI News Radar

First Reads

When this skill triggers inside the repo, read these files first:

  • skills/ai-news-radar/README.md for the public-facing 伯乐Skill / Scout Skill positioning, source-intake prompt, and install-after-first-message guidance.
  • README.md for project usage and current commands.
  • docs/GPT_HANDOFF.md before release-readiness checks or handing the project to another agent.
  • docs/SOURCE_COVERAGE.md before changing source strategy.
  • docs/ROADMAP.md before changing Source Overlap Check, story merge, or version planning.
  • docs/V2_PRODUCT_BRIEF.md before changing product positioning or first-screen UX.
  • scripts/update_news.py before changing data generation.
  • assets/app.js, assets/styles.css, and index.html before changing the UI.
  • references/source-intake.md when the user provides a new site, GitHub repo, RSS feed, newsletter, X source, or asks whether a source can be ingested.
  • references/v2-method.md when the user asks for product optimization, source coverage strategy, Skill packaging, or "v2" direction.

V2 Workflow

Use this order for non-trivial product or source-strategy work:

  1. Context pass: read the current docs, source status, recent commits, and the smallest relevant code surface before proposing changes.
  2. Product diagnostic: identify the user, current workaround, signal-density problem, narrowest useful default, and what must stay in the advanced layer.
  3. Coverage pass: classify each requested source as official feed, OPML, public GitHub-generated feed, public archive, static page, X bridge, optional API adapter, or private inbox/bridge.
  4. Alternatives pass: when the choice is not obvious, present 2-3 approaches: minimal viable, durable architecture, and optional creative/packaged variant.
  5. Implementation pass: make small diffs, reuse existing fetcher/UI patterns, add tests for behavior changes, and run the fastest relevant validation.

For detailed prompts and decision criteria, read references/v2-method.md.

Product Direction

Maintain a two-layer product:

  • Default layer: a simple curated Signal view for ordinary AI enthusiasts.
  • Advanced layer: custom OPML, source health, GitHub Actions, AgentMail email intelligence, and maintainer controls.

Avoid adding many reader-facing choices. Prefer better defaults, source quality, and clearer status output.

The v2 packaging goal is a forkable public site plus a reusable agent Skill. The public-facing Skill name is 伯乐Skill in Chinese and Scout Skill in English. It should feel concrete and easy to use: a scout that helps choose high-signal sources worth tracking, instead of implying that the system knows everything or blindly adding every noisy feed. Ordinary users should be able to browse the hosted page. Maintainers should be able to add their own sources with OPML, public generated feeds, or secret-backed optional adapters without changing the public default.

Safety Rules

  • Never commit private feeds/follow.opml.
  • Never paste secrets, tokens, cookies, browser exports, or .env values into code or logs.
  • Keep the public repo runnable without API keys.
  • Prefer official RSS/Atom/OPML sources over fragile scraping.
  • Avoid account-bound social timelines as defaults.
  • Prefer reading public generated feeds over reimplementing another project's API or scraping pipeline.
  • Treat X API, email, WeChat, private newsletters, and cookies as optional advanced integrations. Store credentials only in environment variables or GitHub Secrets.
  • Treat AgentMail as a private advanced source, not a public default source. Never commit AGENTMAIL_API_KEY, AGENTMAIL_INBOX_ID, inbox addresses, full email bodies, raw emails, or private newsletter contents.
  • Keep AgentMail disabled unless EMAIL_DIGEST_ENABLED=1 is explicit. For QQ Agent Mail, use AGENTMAIL_PROVIDER=agently_cli after agently-cli auth login; default to agently-cli message +list. Only an explicitly authorized private run may set AGENTMAIL_RESOLVE_PUBLIC_URLS=1 to read allowed senders’ messages in memory for archive URLs; never persist bodies. Keep this disabled in the public workflow. The legacy API path may use AGENTMAIL_PROVIDER=api with AGENTMAIL_API_KEY and AGENTMAIL_INBOX_ID, but it must only call the list-messages endpoint; do not call /raw or read text/html bodies.
  • Do not publish data/email-digest.json to public Pages by default. Only allow publication when the maintainer explicitly sets EMAIL_DIGEST_PUBLISH=1 and understands the site/repo privacy implications.

Add Personal Sources

When the user has installed or forked the project but does not know how to start, ask them for a source list first. A good kickoff prompt is:

text
请使用伯乐Skill,先问我要信息源清单,然后帮我判断每个信源该用 RSS、OPML、公开 feed、静态页面、Jina 兜底、AgentMail 邮箱还是跳过。目标是部署一个不需要服务器、能用 GitHub Actions 自动更新的 AI 日报网站。不要把任何 API Key、cookies、token、真实 OPML、邮箱正文或私有邮件内容写入仓库。

Use OPML for private customization:

bash
cp feeds/follow.example.opml feeds/follow.opml
python scripts/update_news.py --output-dir data --window-hours 24 --rss-opml feeds/follow.opml

For GitHub Actions deployment, base64 encode feeds/follow.opml and save it as the repository secret FOLLOW_OPML_B64 to override the public demo OPML. If the secret is not configured, the workflow uses feeds/follow.example.opml as a small public RSS/OPML demo so the hosted page shows the OPML path working. Do not commit the private OPML file. For AgentMail, use EMAIL_DIGEST_ENABLED=1 plus either AGENTMAIL_PROVIDER=agently_cli for a locally authorized QQ Agent Mail CLI, or AGENTMAIL_PROVIDER=api with AGENTMAIL_API_KEY and AGENTMAIL_INBOX_ID in environment variables or GitHub Secrets. Keep EMAIL_DIGEST_INCLUDE_IN_RADAR and EMAIL_DIGEST_PUBLISH unset unless the maintainer explicitly wants a private Pages/repo to publish the metadata-only email digest.

Evaluate A New Source

When a user gives a source URL, first classify it:

  • RSS/Atom/OPML: add privately through feeds/follow.opml unless it should help every public visitor.
  • GitHub project with generated feeds: inspect README, workflows, output files, and raw JSON/RSS URLs; prefer consuming its public feed files.
  • Official changelog or static page: add a focused fetcher only if stable.
  • Newsletter: prefer public archive RSS or archive pages. Use AgentMail only for private newsletter/product-update inboxes; keep it disabled by default and do not expose full bodies, raw emails, inbox ids, or private mailbox addresses.
  • X/Twitter: prefer curated central feeds that already use official X API; direct X API should be optional and secret-backed.

For detailed intake checks and implementation patterns, read references/source-intake.md.

Add A Built-In Source

Only add a built-in source when it is useful to most public visitors.

  1. Run Source Overlap Check for candidate RSS/Atom sources before promoting them into the public default layer:

    bash
    python scripts/evaluate_source_overlap.py \
      --source-url https://example.com/feed.xml \
      --source-name "Example Source" \
      --site-id example_candidate \
      --baseline data/archive.json \
      --lookback-days 7 \
      --output reports/source-intake/example-overlap.json

    Treat the report as advisory: low duplication supports accept_default, high duplication supports skip_duplicate, and small samples or medium duplication should stay watchlist / OPML advanced first.

  2. Inspect existing fetchers in scripts/update_news.py.

  3. Add fetch_<source>(session, now) returning list[RawItem].

  4. Use existing helpers for URL normalization, date parsing, and sessions.

  5. Register the fetcher in the built-in task list.

  6. Update docs/SOURCE_COVERAGE.md when coverage changes.

  7. Add or update tests when behavior changes.

  8. Run a local source-only probe before the full end-to-end generation.

GitHub Project Feed Pattern

For repos like follow-builders, look for public files such as:

  • feed.json, feed-x.json, feed-blogs.json, latest.json
  • state*.json for dedupe behavior
  • .github/workflows/*.yml for schedules, secrets, and output commit paths
  • config/*.json for source lists

If the generated feed is public, stable, timestamped, and low-noise, add a built-in fetcher that reads the raw GitHub URL. Do not copy its private tokens or rebuild its crawler unless the user explicitly wants a self-hosted variant.

Validate

Run the fastest relevant checks:

bash
python -m py_compile scripts/update_news.py
python -m pytest -q
node --check assets/app.js
git diff --check
python "${CODEX_HOME:-$HOME/.codex}/skills/.system/skill-creator/scripts/quick_validate.py" skills/ai-news-radar

For AgentMail changes, also verify default-off safety:

bash
pytest -q tests/test_topic_filter.py -k agentmail

Confirm the checks cover: disabled AgentMail makes no network request, enabled but missing credentials makes no network request, the adapter only uses the list-messages endpoint, and email body/raw fields are not emitted.

When the Skill itself changes, validate the Skill package too:

bash
python "${CODEX_HOME:-$HOME/.codex}/skills/.system/skill-creator/scripts/quick_validate.py" skills/ai-news-radar

For an end-to-end local run:

bash
python scripts/update_news.py --output-dir data --window-hours 24 --rss-opml feeds/follow.opml
python -m http.server 8080

Open http://localhost:8080 and confirm the Signal view, all-source view, WaytoAGI block, search, site filter, and source counts still work.

After pushing source changes, trigger and watch the workflow:

bash
gh workflow run update-news.yml --repo LearnPrompt/ai-news-radar --ref master
gh run list --repo LearnPrompt/ai-news-radar --limit 5

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ai News Radar AI skill do?

Use when working on AI News Radar, 24 小时 AI 更新雷达, AI 更新雷达, 伯乐Skill, or Scout Skill: finding high-signal AI/tech sources, adding RSS/OPML/GitHub feeds, checking source health, updating the web UI, GitHub Actions, or GitHub Pages deployment.

Why use Ai News Radar on TypingMind?

Because you install it once and use it with any model. Ai News Radar is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai News Radar in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LearnPrompt/ai-news-radar/tree/master/skills/ai-news-radar. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ai News Radar?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai News Radar?

As many as you like. As long as a model supports skills, you can use Ai News Radar with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai News Radar AI skill free?

Yes. It is published on GitHub by LearnPrompt under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇