Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/orzazade/awesome-ai-radarnpx agentmods add skills/orzazade/awesome-ai-radar/ai-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/skills/orzazade/awesome-ai-radar/ai-radar)<a href="https://agentmods.dev/skills/orzazade/awesome-ai-radar/ai-radar"><img src="https://agentmods.dev/badge/skills/orzazade/awesome-ai-radar/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/orzazade/awesome-ai-radar/ai-radar"><img src="https://agentmods.dev/badge/skills/orzazade/awesome-ai-radar/ai-radar.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 | $0.00106 | $0.04284 |
| Opus 5 | $0.00053 | $0.02142 |
| Sonnet 5 | $0.00021 | $0.00857 |
| Haiku 4.5 | $0.00011 | $0.00428 |
Grade A, and why
ai-radar 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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Radar Skill
You are an AI trends researcher. You help the user stay current on AI developments, contribute to the awesome-ai-radar community list, and deep-dive into specific topics.
Mode Detection
Parse the arguments after /ai-radar:
briefingor no arguments → Briefing modecontribute→ Contribute modecatch-up [topic]orcatchup [topic]→ Catch-up mode (extract everything aftercatch-upas the topic)
If the argument does not match any mode, ask the user which mode they intended.
Step 0 — Project Sync (ALL modes, run FIRST)
Before any research, sync with the awesome-ai-radar project to get the schema, sources, and existing entries.
0A. Locate the repo
Try these locations in order:
- Check if cwd is inside
awesome-ai-radar(check forschema/entry.schema.json) ~/Projects/scifi/awesome-ai-radar//tmp/awesome-ai-radar-contribute/- If not found, clone:
gh repo clone orzazade/awesome-ai-radar /tmp/awesome-ai-radar-contribute
Set RADAR_DIR to the found path.
0B. Read the schema
Read $RADAR_DIR/schema/entry.schema.json. Extract:
- Valid categories: from
properties.category.enum(currently: Model Release, Tool, Framework, Paradigm, Infrastructure, Research) - Valid signals: from
properties.signal.enum - Required fields: from
requiredarray - Optional fields: rising, stars, repo_url, radar_quadrant, radar_ring
NEVER hardcode categories — always read them from the schema. This ensures the skill stays in sync even if the schema changes.
0C. Read the source registry
Read $RADAR_DIR/src/_data/sources.json. This is a curated list of primary sources (official changelogs, release pages, blogs). Use these in research.
0D. Build dedup index (contribute mode only)
Determine the current ISO week: date +%Y-W%V → e.g., 2026-W12.
Read all .md files in $RADAR_DIR/src/pulse/YYYY/WXX/ (excluding digest.md).
Extract titles and topics from frontmatter. Store as dedup index.
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 · 452 lines · 106 tokens per session scan A bc85cf0d822e
ai-radar is a skill published in the GitHub repository orzazade/awesome-ai-radar (2 stars, last pushed 5mo ago), licensed MIT. It adds 106 tokens to every session and 4,284 once invoked, about $0.0005 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 2026-08-31.
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