World Monitor is a real-time intelligence dashboard that combines curated news, AI-generated briefs, maps, geopolitical signals, infrastructure tracking, and financial data in one interface. It is used for monitoring global and regional events through web, desktop, and multilingual site variants. The catalogue entries are skills, an MCP server, and an instruction for working with the project.
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 koala73/worldmonitor --skill hazard-watchgit clone --depth 1 https://github.com/koala73/worldmonitorWrote 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/koala73/worldmonitor/hazard-watch)<a href="https://agentmods.dev/skills/koala73/worldmonitor/hazard-watch"><img src="https://agentmods.dev/badge/skills/koala73/worldmonitor/hazard-watch/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/koala73/worldmonitor/hazard-watch"><img src="https://agentmods.dev/badge/skills/koala73/worldmonitor/hazard-watch.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 | $0.00056 | $0.00353 |
| Opus 5.5 | $0.00022 | $0.00141 |
| Sonnet 5.5 | $0.00011 | $0.00071 |
| Haiku 4.5 | $0.00006 | $0.00035 |
Grade A, and why
hazard-watch 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 2d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 2d ago First seen · 28 lines · 56 tokens per session scan A 1bbace4bd059
hazard-watch is a skill published in the GitHub repository koala73/worldmonitor (90,096 stars, last pushed today), licensed AGPL-3.0. It adds 56 tokens to every session and 353 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-11-02.
Other skills, from other repositories
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ruview-applications
Run RuView sensing applications — presence/occupancy, breathing & heart rate, activity & fall detection, 17-keypoint pose estimation (WiFlow), sleep monitoring & apnea screening, environment mapping, Mass Casualty Assessment (MAT), and the 3D point-cloud fusion demo. Use when someone wants to actually do something…
train-pose
Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.
calibrate-room
Run the ADR-151 per-room calibration pipeline — baseline → enroll → extract → train → a bank of small specialists (presence/posture/breathing/heartbeat/restlessness/anomaly).
jupyter
This skill covers building PyWry widgets for Jupyter notebooks via MCP tools.