ai-radar

Original：🇨🇳 Chinese

Translated

1 scriptsChecked / no sensitive code detected

AI Radar Skill — Zero-API, Zero-Key, Zero-Server Chinese AI News Query. Data comes from public static JSON files hosted on GitHub Pages by AI News Radar (automatically updated daily via GitHub Actions). You can retrieve data with curl, no authentication, no UA requirements, no rate limits, and you can fork the entire data pipeline to create your own version. This Skill should be triggered when users ask any Chinese AI news-related questions such as "What's happening in the AI circle today?", "AI news in the past 24 hours", "AI daily briefing", "Any recent large model releases?", "AI product updates", "What's new in Agent tools?", "What have OpenAI/Anthropic/Google released recently?", "Hot topics in the AI circle", "Check AI Radar", "Which AI news sources are worth following?", etc. Even if users only say "AI circle", "AI news", or "What's new today", as long as the context is in the AI / large model / Agent / developer tools domain, this Skill should be triggered. **Do NOT undertrigger** — if users ask for AI news and you don't invoke this Skill, you're treating outdated training data as today's news, which is harmful to users. Do NOT use this Skill for maintaining the AI News Radar repository itself (adding news sources, modifying crawling logic, deploying Pages — use Bole Skill / ai-news-radar for that); do NOT use it for non-AI general news queries; do NOT use it for private information sources that require login status.

16installs

Sourcelearnprompt/ai-news-radar

Added on2026-06-30

NPX Install

npx skill4agent add learnprompt/ai-news-radar ai-radar

SKILL.md Content (Chinese)

View Translation Comparison →

AI Radar Skill | AI Radar

You are helping users retrieve the latest 24-hour AI signals from AI News Radar's public data and organize them into a Chinese briefing.

The data is static JSON hosted on GitHub Pages: No API Key, no UA blacklist, no rate limits, just use curl. If the upstream page disappears, anyone can fork the repository to deploy an identical copy of the data on their own GitHub Pages — this is the fundamental difference between this Skill and news Skills that rely on centralized APIs.

General heuristic: When users ask about "current AI industry facts", never rely on training data to guess; always fetch the data first. Even if you "think" you know the answer, check it — the Radar data is much newer than your training cutoff date.

Data Sources

Default Base URL:

text

https://learnprompt.github.io/ai-news-radar/data

Fork users: If users have forked the repository to deploy their own Radar, replace the Base URL with

https://<username>.github.io/ai-news-radar/data

. Ask once when you first discover a user's own deployment, then remember it for future use.

File	Size	Content	When to Use
`latest-24h.json`	~2MB	AI-relevant entries from the past 24 hours (including AI labels, scores, bilingual titles, source tiers)	Default main entry
`source-status.json`	~8KB	Health status, crawl volume, and latency of each news source	When users ask "source health/which sources are valuable"
`stories-merged.json`	~1.4MB	Merged storylines from multiple sources (stratified by importance)	When users ask "today's major events/storylines", check freshness first
`daily-brief.json`	~45KB	Curated 20-entry daily briefing	When users explicitly ask for "daily briefing", check freshness first
`latest-24h-all.json`	~12MB	Full entries including non-AI content	Only pull when users explicitly say "all/including non-AI"
`archive.json`	~56MB	Full historical archive	Prohibited by default. If historical data is truly needed, inform users of the file size and obtain consent first

Freshness Check is Always the First Step

Before any response, check the

generated_at

field:

bash

curl -s "https://learnprompt.github.io/ai-news-radar/data/latest-24h.json" -o /tmp/radar-24h.json
python3 -c "import json;d=json.load(open('/tmp/radar-24h.json'));print(d['generated_at'],d['total_items'])"

If
```
latest-24h.json
```
hasn't been updated for over 36 hours: Answer as usual, but start by honestly informing users "Data stopped updating on [Month Day], the upstream GitHub Actions may be down", and suggest users (if they are maintainers) use Bole Skill to troubleshoot.
If
```
stories-merged.json
```
/
```
daily-brief.json
```
is more than 48 hours older than
```
latest-24h.json
```
: Do not use them to answer "today"-related questions; fall back to
```
latest-24h.json
```
and explain the reason for the fallback.
Never present expired data as fresh data. Honest labeling of data time is always part of the briefing.

Routing Table

User Query	Route
Default broad questions: "What's happening in the AI circle today?", "AI news in the past 24 hours", "What's new in AI recently"	`latest-24h.json` → Fetch top entries sorted by source authority + AI score
"Today's major events", "storylines", "What are the notable events to follow"	`stories-merged.json` (if freshness check passes) → Fetch top entries by `importance_score` ; otherwise fall back to main entry
Explicitly asks for "daily briefing"	`daily-brief.json` (if freshness check passes); otherwise fall back to main entry and explain
"Model releases", "AI products", "Agent tools", "papers", "robots"	`latest-24h.json` → Filter by `ai_label` (mapping below)
"What has OpenAI released recently", "Sora-related content"	`latest-24h.json` → Match keywords in `title` / `title_en` / `ai_signals`
"Which sources are healthy/valuable", "source status"	`source-status.json` + `site_stats` from main entry
"All updates/including non-AI content"	`latest-24h-all.json` (remind users it's ~12MB)
"AI news from last week/last month"	Honestly explain: The public data has a 24-hour rolling window; historical data requires `archive.json` (56MB), obtain consent before pulling

Chinese mapping for

ai_label

model_release

→ Model Release /

ai_product_update

→ Product Update /

developer_tool

→ Developer Tool /

agent_workflow

→ Agent Workflow /

research_paper

→ Research Paper /

industry_business

→ Industry Trends /

infra_compute

→ Computing & Infra /

robotics

→ Robotics /

ai_tech

→ Technological Progress /

curated_hotlist

→ Curated Hotlist /

ai_general

→ General AI.

Source tier

source_tier_rank

(smaller number means more authoritative): 0 → Official First-hand Source / 1 → AI Vertical Source / 2 → Builders/X Source / 3 → RSS/OPML / 5 → Hot Discussion Reference.

Workflow

Iron rule: Always download data to /tmp, filter with python3, never dump the entire JSON into context. The main entry is 2MB with nearly 1000 entries; directly reading it will overwhelm you.

Default Path: Top 24-hour Signals

bash

curl -s "https://learnprompt.github.io/ai-news-radar/data/latest-24h.json" -o /tmp/radar-24h.json
python3 - <<'EOF'
import json
d = json.load(open('/tmp/radar-24h.json'))
items = d['items_ai']
# Prioritize official first-hand sources, sort by AI relevance score descending within the same tier
top = sorted(items, key=lambda i: (i['source_tier_rank'], -i['ai_score']))[:30]
print(f"Data Time: {d['generated_at']} | 24h AI Entries: {d['total_items']} | Sources: {d['source_count']}")
for i in top:
    print(f"[{i['ai_label']}|{i['source_tier_label']}] {i['title']} — {i['source']} — {i['url']}")
EOF

Filter by Category ("Recent model releases")

bash

python3 - <<'EOF'
import json
d = json.load(open('/tmp/radar-24h.json'))
hits = [i for i in d['items_ai'] if i['ai_label'] == 'model_release']
hits.sort(key=lambda i: (i['source_tier_rank'], -i['ai_score']))
for i in hits[:20]:
    print(f"[{i['source_tier_label']}] {i['title']} — {i['source']} — {i['url']}")
EOF

Filter by Keyword ("What has OpenAI released recently")

bash

python3 - <<'EOF'
import json
KW = 'openai'  # lowercase
d = json.load(open('/tmp/radar-24h.json'))
def hit(i):
    blob = ' '.join([i.get('title',''), i.get('title_en') or '', ' '.join(i.get('ai_signals') or [])]).lower()
    return KW in blob
hits = sorted(filter(hit, d['items_ai']), key=lambda i: (i['source_tier_rank'], -i['ai_score']))
for i in hits[:20]:
    print(f"{i['title']} — {i['source']} — {i['published_at'][:10]} — {i['url']}")
EOF

Storylines (Pass Freshness Check First)

bash

curl -s "https://learnprompt.github.io/ai-news-radar/data/stories-merged.json" -o /tmp/radar-stories.json
python3 - <<'EOF'
import json, datetime
d = json.load(open('/tmp/radar-stories.json'))
gen = datetime.datetime.fromisoformat(d['generated_at'].replace('Z','+00:00'))
age_h = (datetime.datetime.now(datetime.timezone.utc) - gen).total_seconds()/3600
if age_h > 48:
    print(f"STALE:{age_h:.0f}h")  # Fall back to latest-24h.json when seeing STALE, do not force use
else:
    top = sorted(d['stories'], key=lambda s: -s['importance_score'])[:15]
    for s in top:
        print(f"[{s['importance_label']}|{s['source_count']} Sources] {s['title']} — {s['primary_url']}")
EOF

Source Health ("Which sources are valuable")

bash

curl -s "https://learnprompt.github.io/ai-news-radar/data/source-status.json" -o /tmp/radar-status.json
python3 - <<'EOF'
import json
d = json.load(open('/tmp/radar-status.json'))
print(f"Success: {d['successful_sites']} Failed: {d['failed_sites']} Zero Output: {d['zero_item_sites']}")
for s in d['sites']:
    flag = 'OK' if s['ok'] else 'FAIL'
    print(f"[{flag}] {s['site_name']}: {s['item_count']} Entries")
EOF

Output Format

Organize into a Chinese briefing with the following structure:

markdown

# AI Radar Briefing · [Date]

> Data Window: Past 24 Hours | Data Time: [Human-readable generated_at] | [N] AI-relevant Entries / [M] Sources

## Model Releases
- **[Title]** — [Source] ([Source Tier])
  [One-sentence description with original link]

## Products & Tools
- ...

## Notable Mentions
- [1-3 high-discussion entries from the hot discussion reference tier]

Briefing Rules:

Each entry must include the original
```
url
```
for users to dive deeper.
Entries from official first-hand sources take priority; hot discussion references are only added as "Notable Mentions" supplements, not mixed with main entries.
Use
```
title_zh
```
if available; use
```
title_bilingual
```
if the original title is in English.
Keep entry count concise: 10-20 entries by default, quality over quantity. Add more only if users request it.
Always label the data time at the end. If data is expired, state it at the beginning, do not hide it.

Failure Modes

Pages 404 / Network Failure: Retry once with the raw address:
```
https://raw.githubusercontent.com/LearnPrompt/ai-news-radar/master/data/latest-24h.json
```
. If it still fails, inform users honestly, do not fabricate news.
Expired Data: See "Freshness Check". Answer as usual + prominent label + suggest maintainers troubleshoot.
Empty Category (e.g., no papers on the day): Honestly state "No paper entries in the Radar over the past 24 hours", do not fill with entries from other categories.
User Query Outside 24-hour Window: Explain the window limit, offer the archive option (with size warning), do not pretend to have checked historical data.

Want to Change Sources? Upgrade Path

This Skill only reads data. If users say "I want to add/remove a source/build my own Radar":

Fork

https://github.com/LearnPrompt/ai-news-radar

;

Use the Bole Skill (
```
skills/ai-news-radar/
```
) in the repository to add and evaluate sources, deploy GitHub Pages;
Return to this Skill and point the Base URL to your own Pages.

You choose the sources, you own the data, and this Skill will continue to help you read it.

Security Boundaries

Only perform GET requests, only read public static files, never send any write requests.
Do not need or accept any API Key, token, or cookie.
Do not crawl pages that require login; suggest users use Bole Skill's private OPML/AgentMail path for private sources.
Keep original links when citing entries, do not rewrite source attribution.

ai-radar

NPX Install

Tags

SKILL.md Content (Chinese)

AI Radar Skill | AI Radar

Data Sources

Freshness Check is Always the First Step

Routing Table

Workflow

Default Path: Top 24-hour Signals

Filter by Category ("Recent model releases")

Filter by Keyword ("What has OpenAI released recently")

Storylines (Pass Freshness Check First)

Source Health ("Which sources are valuable")

Output Format

Failure Modes

Want to Change Sources? Upgrade Path

Security Boundaries