World2Agent is an open protocol that gives AI agents structured, real-time information from sensors watching the real world. It is for connecting agents to interchangeable data sources through a shared schema so they can perceive events and decide what to do. The catalogue skill helps operate World2Agent with an agent runtime.
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 machinepulse-ai/world2agent --skill build-w2a-sensorgit clone --depth 1 https://github.com/machinepulse-ai/world2agentWrote 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/machinepulse-ai/world2agent/build-w2a-sensor)<a href="https://agentmods.dev/skills/machinepulse-ai/world2agent/build-w2a-sensor"><img src="https://agentmods.dev/badge/skills/machinepulse-ai/world2agent/build-w2a-sensor/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/machinepulse-ai/world2agent/build-w2a-sensor"><img src="https://agentmods.dev/badge/skills/machinepulse-ai/world2agent/build-w2a-sensor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 3 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium MCP Rug Pull · line 142 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 479 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 502 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium Rogue Agent · line 333 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Rogue Agent · line 375 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00069 | $0.06470 |
| Opus 5.5 | $0.00028 | $0.02588 |
| Sonnet 5.5 | $0.00014 | $0.01294 |
| Haiku 4.5 | $0.00007 | $0.00647 |
Grade C, and why
build-w2a-sensor scanned grade C 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 1mo 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
"clean": "rm -rf dist *.tsbuildinfo", How it starts
The opening of the file, as written. The whole thing — 511 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build a World2Agent Sensor
You are guiding a developer through designing and building a W2A sensor — a package that subscribes to an external source and emits W2ASignal per the World2Agent protocol.
Protocol reference: https://github.com/machinepulse-ai/world2agent — the schema under schema/0.1/ is authoritative for every field shape. If anything in this skill contradicts the schema, the schema wins.
Your first job is discovery, not code. Sensors that skip discovery ship either too much config (tuning knobs no one touches) or too little signal quality (vague summaries, wrong event types, missing install recipe). Both are painful to fix later.
A sensor package has two deliverables:
- The runtime (
src/) — connects to the source, transforms incoming events intoW2ASignal, callsctx.emit(). SETUP.md— the install recipe. When the consumer agent runs the host's sensor-add flow (e.g./world2agent:sensor-add <package>in Claude Code), it reads this file, runs the Q&A defined inside, writes the sensor's entry into~/.world2agent/config.json, and writes a per-user handler skill into the agent runtime's skills directory. The handler is generated by the install flow, not shipped in the package.
Do not scaffold until Phase 1 and Phase 2 are settled.
Phase 1 — Interrogate the source
Ask these conversationally (batch in one message, don't serialize). Adapt to what the user already said.
1. What does the sensor connect to?
Name the source platform and give the user-facing "why this matters". This drives source_type, package naming, and what domain namespace the event types use.
2. How do events reach the sensor?
Pick the highest available option — do not default to polling if a push channel exists.
| Mechanism | Use when | Notes |
|---|---|---|
| Official push (webhook / SSE / WebSocket) | The source offers it | Preferred: seconds latency, no wasted calls, no cursor drift |
| Official API polling | No push offered | Persist a cursor via ctx.store |
| Schedule-triggered (cron) | Source is inherently periodic (digests, daily summaries) | Time itself is the trigger |
| File-system watch / scraping | No official API at all | Last resort — brittle and hard to test |
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.
- 1mo ago First seen · 511 lines · 69 tokens per session scan C 57e0e458ab5b
build-w2a-sensor is a skill published in the GitHub repository machinepulse-ai/world2agent (1,132 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 69 tokens to every session and 6,470 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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