volt-ota

volt-ota is a skill for Claude Code from tonone-ai/tonone. It costs 70 tokens per session (3,302 once invoked), scanned A, original, MIT.

An over-the-air (OTA) update system design for sending new firmware to devices remotely. It covers device storage layout, update steps, safe rollback, validation, fleet management, and recovery from failures.

In plain words
What is it for?
Use it to plan firmware updates for devices already deployed in the field, including how updates are checked, installed, reversed, and managed across a fleet.
Why use it?
It helps reduce the risk of leaving devices unusable when an update is interrupted or the new firmware fails.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the TodoWrite tool; mentions Claude Code.

Part of the engineering-team plugin — 80 skills shipped together

Good fit Use it to plan firmware updates for devices already deployed in the field, including how updates are checked, installed, reversed, and managed across a fleet.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tonone-ai/tonone/volt-ota
Install

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.

Any agent
npx skills add tonone-ai/tonone --skill volt-ota
Clone the repo
git clone --depth 1 https://github.com/tonone-ai/tonone

Made for: Claude Code.

Or install engineering-team, the plugin that ships this one along with the rest of its 80 skills.

Wrote 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.

agentmods badge for volt-ota

README.md
[![agentmods](https://agentmods.dev/badge/skills/tonone-ai/tonone/volt-ota/github.svg)](https://agentmods.dev/skills/tonone-ai/tonone/volt-ota)
Your own site
<a href="https://agentmods.dev/skills/tonone-ai/tonone/volt-ota"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/volt-ota/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.

agentmods 80×15 button for volt-ota

Your own site · 80×15
<a href="https://agentmods.dev/skills/tonone-ai/tonone/volt-ota"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/volt-ota.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,302 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00070 $0.03302
Opus 5.5 $0.00028 $0.01321
Sonnet 5.5 $0.00014 $0.00660
Haiku 4.5 $0.00007 $0.00330

Measured 17d ago against content hash e7789c143414, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

volt-ota 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 17d 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.

bundle/engineering-team/skills/volt-ota/SKILL.md · 313 lines

How it starts

The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OTA Update System Design

You are Volt — the embedded and IoT engineer on the Engineering Team.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

A bricked device in the field is a recall. OTA is not a feature — it is the mechanism that lets you fix every other mistake you will make after shipping. Design it to be safe before you design it to be fast.

This skill produces a complete OTA update system design. Given a device type, you output the design — partition layout, update flow, rollback conditions, validation checks, fleet management approach, and all failure modes with explicit recovery paths.


Phase 1: Device + Fleet Audit

Before designing the OTA system, establish what you're designing for. Decisions differ significantly based on these constraints.

Collect or infer from context:

Constraint Why it matters
MCU + flash size Determines whether A/B dual-partition or single-partition with delta updates is feasible
Connectivity WiFi vs BLE vs LoRa vs cellular — each has different bandwidth, reliability, and resumability characteristics
Power source Battery-powered devices need update windows; power loss mid-update is a primary failure scenario
Deployment scale 10 devices vs 10K devices changes fleet tooling requirements
Update frequency Monthly patches vs emergency hotfixes — changes how aggressively you push
Existing OTA mechanism ESP-IDF OTA, MCUboot, Mender, Golioth — determines partition layout constraints
Security requirement Consumer vs industrial vs medical — determines signing requirements

Read the full file on GitHub · 313 lines

Changes

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.

  1. 17d ago First seen · 313 lines · 70 tokens per session scan A e7789c143414

Subscribe to this mod's changes

volt-ota is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 70 tokens to every session and 3,302 once invoked, about $0.0003 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 2026-09-27.

Related

Other skills, from other repositories

iot-expert

Expert-level IoT systems, embedded devices, edge computing, and IoT protocols. Use when the user mentions embedded, edge computing, MQTT, sensors, or firmware, or when the task involves IoT Architecture, IoT Protocols, Embedded Systems, or Device Design.

personamanagmentlayer/pcl · 59 tokens

spark-environment-setup

Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.

wshobson/agents · 76 tokens

spark-training-gotchas

Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.

wshobson/agents · 63 tokens

llama-cpp

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

davila7/claude-code-templates · 76 tokens

technical-troubleshooting

Provide setup, troubleshooting, and maintenance guidance. Use when the user reports a device that won't power on, connectivity issues, setup questions, overheating, or maintenance concerns.

strands-agents/samples · 38 tokens

amazon-alexa

Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).

lingxling/awesome-skills-cn · 53 tokens