hardware-evidence-capture

hardware-evidence-capture is a skill for Claude Code from Galbaz1/video-research-mcp. It costs 18 tokens per session (339 once invoked), scanned A, original, MIT.

A workflow for inspecting simulated hardware, reading its sensors or images, and recording the commands and observations that produced the results.

In plain words
What is it for?
Use it to discover a simulated device, inspect capabilities and health, read sensor or image data, and prepare or review controlled device writes.
Why use it?
It keeps synthetic device readings and proposed writes traceable, and prevents simulated observations from being presented as physical measurements. Safety limits and separate approval steps are retained.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the gr plugin — 27 skills, 17 commands, 7 agents shipped together

Good fit Use it to discover a simulated device, inspect capabilities and health, read sensor or image data, and prepare or review controlled device writes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/galbaz1/video-research-mcp/hardware-evidence-capture
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 Galbaz1/video-research-mcp --skill hardware-evidence-capture
Clone the repo
git clone --depth 1 https://github.com/Galbaz1/video-research-mcp

Made for: Claude Code.

Or install gr, the plugin that ships this one along with the rest of its 27 skills, 17 commands, 7 agents.

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 hardware-evidence-capture

README.md
[![agentmods](https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/hardware-evidence-capture/github.svg)](https://agentmods.dev/skills/galbaz1/video-research-mcp/hardware-evidence-capture)
Your own site
<a href="https://agentmods.dev/skills/galbaz1/video-research-mcp/hardware-evidence-capture"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/hardware-evidence-capture/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 hardware-evidence-capture

Your own site · 80×15
<a href="https://agentmods.dev/skills/galbaz1/video-research-mcp/hardware-evidence-capture"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/hardware-evidence-capture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 339 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 $0.00018 $0.00339
Opus 5.5 $0.00007 $0.00136
Sonnet 5.5 $0.00004 $0.00068
Haiku 4.5 $0.00002 $0.00034

Measured 1mo ago against content hash e101c92f51e9, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

hardware-evidence-capture 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.

skills/hardware-evidence-capture/SKILL.md · 34 lines

What it actually says

Hardware evidence capture

Use mhs_discover and mhs_meta_info to inspect the named device and current capabilities, directions and limits. All currently supported devices are simulated. Preserve that label in every result; never infer physical state, calibration, safety or camera coverage.

Read sensors or images with mhs_read and retain the returned original artifact path, byte count, SHA256, command/state identity, method and units. Generated simulator images are synthetic data and cannot become factual original evidence. Do not upload or publish observations without separate authority.

For a write, provide one explicit command ID and exact parameters. Hard limits cannot be overridden. confirm=true cannot approve a soft override or a confirmation-required operation. Return the full approval proposal for an operator's review; only the separately configured host file can authorize it. Do not write that file on the operator's behalf without an explicit instruction. An awaiting proposal has not attempted its action. A terminal command ID replays its receipt and never re-executes.

After a failed write, inspect mhs_health_check before any new write. Inspect the executed command, effective limits and resulting state in its receipt. Emergency stop latches the simulator off. Soft recovery needs its own exact host approval. Neither outcome proves physical stop or recovery effectiveness.

Physical adapters are unavailable pending named owner qualification. Stop on missing authority or an unknown result; preserve the error and receipt rather than inventing a device observation or automatically retrying.

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. 1mo ago First seen · 34 lines · 18 tokens per session scan A e101c92f51e9

Subscribe to this mod's changes

hardware-evidence-capture is a skill published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 6d ago), licensed MIT. It adds 18 tokens to every session and 339 once invoked, about $0.0001 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 2026-10-03.

Related

hardware-evidence-capture compared with skills from other repositories

hardware-evidence-capture next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
hardware-evidence-captureGalbaz1/video-research-mcpSkill236d agoMITA18Claude Code
meteocontrol-assistantnielsvbrecht/meteocontrol-mcpSkill04mo agoApache-2.0A40Claude Code, Codex
network-health-checksirkirby/unifi-mcpSkill1.0kyesterdayMITA49Claude Code
video-perceptionjordanrendric/claude-video-visionSkill1.5k23d agoMITA51Claude Code
eg-precommit-reviewvshvedov/elephant-goldfishSkill922d agoMITA24Codex
b123d-modelingpzfreo/build123d-mcpSkill14222d agoApache-2.0A0Claude Code, Codex
farming-expertpersonamanagmentlayer/pclSkill4411d agoApache-2.0A50Claude Code