AI News Radar is an automated service that collects AI and technology updates, evaluates source quality, combines reports about the same event, and presents selected news in a web interface. It is for people who want a daily view of AI news and for developers who want to run a customized radar with their own sources. The catalogue add-ons help coding agents assess sources, maintain collection logic, and deploy the radar.
Borrowing it
Nothing to install: this file belongs to LearnPrompt/ai-news-radar. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/LearnPrompt/ai-news-radar/master/AGENTS.mdgit clone --depth 1 https://github.com/LearnPrompt/ai-news-radarWrote 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/instructions/learnprompt/ai-news-radar/agents-md)<a href="https://agentmods.dev/instructions/learnprompt/ai-news-radar/agents-md"><img src="https://agentmods.dev/badge/instructions/learnprompt/ai-news-radar/agents-md/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/instructions/learnprompt/ai-news-radar/agents-md"><img src="https://agentmods.dev/badge/instructions/learnprompt/ai-news-radar/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00375 | $0.00375 |
| Opus 5 | $0.00187 | $0.00187 |
| Sonnet 5 | $0.00075 | $0.00075 |
| Haiku 4.5 | $0.00038 | $0.00038 |
Grade A, and why
ai-news-radar AGENTS.md scanned grade A with 0 findings 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 10d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
AI News Radar Agent Notes
Scope
This repo powers the public AI News Radar static site and Scout Skill source workflow. Use it for high-signal AI/tech news aggregation, OPML-based custom feeds, GitHub Actions refresh jobs, and GitHub Pages publishing.
Working Rules
- Keep changes small and reviewable.
- Search the repo before changing source fetchers or output schemas.
- Do not commit private feeds, secrets, tokens, cookies, or
.envvalues. - Do not commit
feeds/follow.opml; usefeeds/follow.example.opmlas the public template. - Prefer stable public RSS/Atom/OPML sources before adding custom scrapers.
- Keep the reader-facing product simple: default to a curated AI-focused view, hide noisy or advanced source details behind existing filters/docs.
Source Strategy
Read docs/SOURCE_COVERAGE.md before adding or removing sources.
Default source priority:
- Official RSS/Atom feeds and OPML collections.
- Stable public JSON APIs or static pages with timestamps.
- Curated newsletters or changelogs with public feeds.
- Manual/custom adapters only when the source is high-signal and stable.
Avoid account-bound timelines, broad personal social feeds, login-gated pages, and fragile bridges unless the user explicitly accepts the maintenance cost.
Common Commands
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements-dev.txt
python -m py_compile scripts/update_news.py
python -m pytest -q
python scripts/update_news.py --output-dir data --window-hours 24 --rss-opml feeds/follow.opml
python -m http.server 8080
For agent workflows, read skills/ai-news-radar/SKILL.md.
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.
- 10d ago First seen · 45 lines · 375 tokens per session scan A 5ee757341e32
ai-news-radar AGENTS.md is an instructions file published in the GitHub repository LearnPrompt/ai-news-radar (1,708 stars, last pushed today), licensed MIT. It adds 375 tokens to every session, about $0.0019 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.