Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aAAaqwq/AGI-Super-Team --skill ai-radargit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aaaaqwq/agi-super-team/ai-radar)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/ai-radar"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/ai-radar/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/ai-radar"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/ai-radar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00300 | $0.03089 |
| Opus 5 | $0.00150 | $0.01545 |
| Sonnet 5 | $0.00060 | $0.00618 |
| Haiku 4.5 | $0.00030 | $0.00309 |
Grade A, and why
ai-radar scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
雷达Skill（AI Radar）——零API、零Key、零服务器的中文AI资讯查询。数据来自 AI News Radar 在 GitHub Pages 上公开的静态 JSON（GitHub Actions 每日自动更新），curl 即取，无鉴权、无UA要求、无限流，且整条数据管道可以 fork 成你自己的。 How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
雷达Skill | AI Radar
你在帮用户从 AI News Radar 的公开数据里取出最近 24 小时的 AI 信号，整理成中文简报。
数据是静态 JSON，躺在 GitHub Pages 上：没有 API Key，没有 UA 黑名单，没有限流，curl 就行。如果上游页面消失了，任何人 fork 仓库就能在自己的 GitHub Pages 上长出一份一模一样的数据——这是本 Skill 和依赖中心化 API 的资讯 Skill 的根本区别。
通用启发：用户问的是"现在的 AI 行业事实"，不要凭训练数据脑补，永远先拉数据。即使你"觉得"知道答案，也要查——雷达数据比你的训练截止日新得多。
数据源
默认 Base URL：
https://learnprompt.github.io/ai-news-radar/data
fork 用户：如果用户 fork 了仓库部署自己的雷达，把 Base URL 换成 https://<用户名>.github.io/ai-news-radar/data。第一次发现用户有自己的部署时问一次，之后记住。
| 文件 | 大小 | 内容 | 什么时候用 |
|---|---|---|---|
latest-24h.json |
~2MB | 24小时AI强相关条目（含AI标签、分数、双语标题、信源分层） | 默认主入口 |
source-status.json |
~8KB | 每个信源的健康状态、抓取量、耗时 | 用户问"信源健康/哪些源有料" |
stories-merged.json |
~1.4MB | 多源合并后的故事线（importance分层） | 用户问"今天的大事/故事线"，先查新鲜度 |
daily-brief.json |
~45KB | 精选20条日报成品 | 用户明确说"日报"，先查新鲜度 |
latest-24h-all.json |
~12MB | 含非AI的全量条目 | 仅用户明确说"全部/包括非AI"才拉 |
archive.json |
~56MB | 全部历史存档 | 默认禁止。确需历史数据时先告知体积并征得同意 |
第一步永远是新鲜度检查
任何回答之前，先看 generated_at：
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'])"
latest-24h.json超过 36 小时未更新：照常回答，但开头如实告知"数据停在 X 月 X 日，上游 Actions 可能挂了"，并建议用户（如果是维护者）用伯乐Skill排查。stories-merged.json/daily-brief.json比latest-24h.json旧超过 48 小时：不要用它们回答"今天"类问题，降级到latest-24h.json，并说明降级原因。- 绝不把过期数据当新鲜数据报给用户。诚实标注数据时间永远是简报的一部分。
路由表
| 用户在说 | 走哪 |
|---|---|
| 默认宽问题："今天AI圈有什么"、"过去24小时AI新闻"、"最近AI有啥" | latest-24h.json → 按信源权威度+AI分数排序取头部 |
| "今天的大事"、"故事线"、"有什么值得关注的事件" | stories-merged.json（新鲜度通过时）按 importance_score 取头部；否则降级主入口 |
| 明确说"日报" | daily-brief.json（新鲜度通过时）；否则降级主入口并说明 |
| "模型发布"、"AI产品"、"Agent工具"、"论文"、"机器人" | latest-24h.json 按 ai_label 过滤（映射见下） |
| "OpenAI最近发了什么"、"Sora相关" | latest-24h.json 按关键词在 title/title_en/ai_signals 里匹配 |
| "哪些信源健康/有料"、"源状态" | source-status.json + 主入口的 site_stats |
| "全部动态/包括非AI的" | latest-24h-all.json（提醒~12MB） |
| "上周/上个月的AI新闻" | 如实说明：公开数据滚动窗口为24小时，历史需 archive.json（56MB），先征得同意再拉 |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 199 lines · 300 tokens per session scan A 386bb5d95c22
ai-radar is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed today), licensed MIT. It adds 300 tokens to every session and 3,089 once invoked, about $0.0015 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
guidance
Add, edit, or audit guidance docs. Default writes guidance for Claude (.claude/guidance/, Markdown, moflo universal rules). -h writes for human readers (docs/, lighter ruleset). --html emits HTML with a minimal default stylesheet instead of Markdown. -a audits the .claude/guidance/ directory.
deslop
The optimization pass, defined - delete before you add, one smell class per pass, behaviour pinned by a test that ran BEFORE the edit. Lints a SKILL.md and prose by the same instinct. Use for the per-story optimization pass or when code has grown noisy without growing capable.
eldar
Consult the Eldar — audit a project's moflo + Claude Code setup for portable, high-leverage gaps and guide remediation. Default mode is read-only audit with severity-ranked findings; --fix presents an interactive triage menu and walks the user through each chosen fix (healer, missing CLAUDE.md, sparse guidance…
root-cause
Find the mechanism behind a failure instead of patching its symptom - reproduce first, one variable per experiment with the prediction written before the run, exit by naming the mechanism and pinning it with a failing test. Use for a bug, an unexplained red test, or a failure that will not reproduce.
memory-worktree
Verify, customize, or opt out of moflo's AUTOMATIC durable-learning sharing across git worktrees / Conductor workspaces on one machine. As of the worktree-auto-sharing change this is on by default — learnings converge across a repo's worktrees with no setup. Use when the user asks "is memory shared across my…
code-tour
Maintain docs/code-tour.md — the annotated guided reading of Aigon's core logic. Use when you have changed code the tour quotes, added a subsystem a new reader would need, or the user says "update the code tour", "the tour is stale", "add X to the code tour", or asks to review/refresh the code examples doc.