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 kornia/bubbaloop --skill openclawgit clone --depth 1 https://github.com/kornia/bubbaloopWrote 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/kornia/bubbaloop/openclaw)<a href="https://agentmods.dev/skills/kornia/bubbaloop/openclaw"><img src="https://agentmods.dev/badge/skills/kornia/bubbaloop/openclaw/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/kornia/bubbaloop/openclaw"><img src="https://agentmods.dev/badge/skills/kornia/bubbaloop/openclaw.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 | $0.00040 | $0.00652 |
| Opus 5.5 | $0.00016 | $0.00261 |
| Sonnet 5.5 | $0.00008 | $0.00130 |
| Haiku 4.5 | $0.00004 | $0.00065 |
Grade A, and why
bubbaloop-physical-ai 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 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.
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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bubbaloop Physical AI Skill
You have access to a physical sensor network managed by bubbaloop. Use the bubbaloop MCP tools to interact with hardware.
Quick Reference
Discover what's available
list_nodes— See all registered sensor/actuator nodes and their statusget_node_detail(name)— Get detailed info about a specific nodeget_system_status— Overview of the entire system
Read sensor data
query_zenoh(key_expression)— Query any Zenoh topic for current data- Use
bubbaloop/**/outputto find all sensor outputs - Use
bubbaloop/**/health/*to check node health - Use
bubbaloop/**/manifestto discover node capabilities
- Use
Control nodes
start_node(name)— Start a stopped nodestop_node(name)— Stop a running noderestart_node(name)— Restart a nodeinstall_node(name)— Install a node as a system servicebuild_node(name)— Build a node from source
Monitor
get_node_health(name)— Check if a node is healthyget_node_logs(name)— View recent logsget_stream_info(name)— Get Zenoh connection params for streaming data
Configure
get_node_config(name)— Read node configurationsend_command(name, command, params)— Send commands to nodes
Common Patterns
"What sensors are available?"
Call list_nodes to see all nodes. Each node's status shows if it's Running, Stopped, or Failed.
"What's the temperature?"
- Call
list_nodesto find temperature-related nodes - Call
query_zenoh("bubbaloop/**/output")to read current values - Parse the protobuf or JSON response
"Start monitoring cameras"
start_node("rtsp-camera")to ensure the camera node is runningget_stream_info("rtsp-camera")to get the Zenoh topic for live frames
"Something seems wrong"
get_system_statusfor overviewget_node_health(name)for specific node healthget_node_logs(name)for recent logs
Architecture
bubbaloop uses a dual-plane model:
- MCP (Control Plane): You interact here — lifecycle, config, commands
- Zenoh (Data Plane): Real-time sensor data flows here — pub/sub, queryables
What ships with it
2 files 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.
- 1mo ago First seen · 70 lines · 40 tokens per session scan A 41c4ff5b6547
bubbaloop-physical-ai is a skill published in the GitHub repository kornia/bubbaloop (29 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 652 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 2026-08-30.
Other skills, from other repositories
vss-generate-video-calibration
Use this skill when running or tuning AutoMagicCalib on local MP4s, RTSP, or the bundled sample dataset, or when deploying vss-auto-calibration. Do not use for non-AMC calibration or runtime analytics.
vss-manage-alerts
Use this skill when operating VSS alert workflows — real-time monitoring, Alert-Bridge subscriptions, verification verdicts, on-demand verification, always-on operation, Slack notifications, incident queries, or camera onboarding. Not for non-alert analytics.
habitat-gs-control
Interactively pilot a robot in habitat-gs 3D Gaussian Splatting indoor scenes via MCP tools — load a scene, move, observe with RGB/depth, run autonomous nav loops, and export video. Use for hands-on control of a live sim session, NOT for training or evaluating a navigation policy (use the habitat-gs-train skill for…
voyager-launch
Complete one-shot Voyager SDK launcher for Axelera AI Metis pipelines and demos. Use when the user asks to build, create, launch, run, verify, package, open a browser result viewer, or show results for a complete AI vision pipeline from a natural-language request (including when they want to debug or evaluate a…
voyager-new-app
Create or modify a standalone Python application using the Voyager SDK InferenceStream/createinferencestream API for Axelera AI Metis hardware. Use when the user explicitly asks for app code, monitoring, analytics, REST API, custom UI, multi-stream logic, alerts, or business logic around an existing/validated…
voyager-bench
Benchmark model performance on Axelera AI Metis hardware with the Voyager SDK. Use when the user wants to measure FPS, throughput, latency, or compare model variants. Use to quantify a working pipeline. Prefer voyager-debug when performance is a suspected fault or regression.