Ruview Verify

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ruvnet

ruview-verify

Verify a RuView build — full Rust workspace tests, the deterministic Python pipeline proof (SHA-256 Trust Kill Switch), firmware hash manifest, and the ADR-028 witness bundle with one-command self-verification. Use after any significant change, before merging a PR, or to produce an attestation bundle for a recipient.

Overview

Publisher	ruvnet
Repository	`RuView`
Skill name	`ruview-verify`
Stars	94.3K
Forks	12.5K
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by ruvnet on GitHub. Read the source before you install it.

Installation

Install the Ruview Verify AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/ruvnet/RuView.git /tmp/RuView
mkdir -p .claude/skills
cp -r /tmp/RuView/plugins/ruview/skills/ruview-verify .claude/skills/ruview-verify

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ruview Verify in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ruview Verify on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ruview Verify is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

RuView Verification & Witness Bundle

The trust pipeline for RuView. Run this after meaningful changes and before merging.

1. Rust workspace tests

bash
cd v2
cargo test --workspace --no-default-features        # must be 1,400+ passed, 0 failed (~2 min)

Single-crate checks (no GPU): cargo check -p wifi-densepose-train --no-default-features, cargo test -p wifi-densepose-signal --no-default-features, etc.

2. Deterministic Python proof (Trust Kill Switch)

Feeds a reference CSI signal through the production pipeline and hashes the output. Any behavioural drift changes the hash.

bash
cd ..
python archive/v1/data/proof/verify.py              # must print VERDICT: PASS

If it fails on a hash mismatch after a legitimate numpy/scipy bump:

bash
python archive/v1/data/proof/verify.py --generate-hash
python archive/v1/data/proof/verify.py

Artifacts: archive/v1/data/proof/verify.py, expected_features.sha256, sample_csi_data.json (1,000 synthetic frames, seed=42).

3. Python test suite (v1)

bash
cd archive/v1 && python -m pytest tests/ -x -q

4. Generate the witness bundle (ADR-028)

bash
bash scripts/generate-witness-bundle.sh

Produces dist/witness-bundle-ADR028-<sha>.tar.gz containing:

WITNESS-LOG-028.md — 33-row attestation matrix, evidence per capability
ADR-028-esp32-capability-audit.md — full audit findings
proof/verify.py + expected_features.sha256 — the deterministic proof
test-results/rust-workspace-tests.log — full cargo test output
firmware-manifest/source-hashes.txt — SHA-256 of all 7 ESP32 firmware files
crate-manifest/versions.txt — all 15 crates + versions
VERIFY.sh — one-command self-verification for recipients

5. Self-verify the bundle

bash
cd dist/witness-bundle-ADR028-*/
bash VERIFY.sh                                       # must be 7/7 PASS

Pre-merge checklist (from CLAUDE.md)

Rust tests pass (1,400+, 0 fail)
Python proof passes (VERDICT: PASS)
README.md updated if scope changed (platform/crate/hardware tables, feature summaries)
CLAUDE.md updated if scope changed (crate table, ADR list, module tables, version)
CHANGELOG.md — entry under [Unreleased]
docs/user-guide.md updated if new data sources / CLI flags / setup steps
ADR index — bump ADR count in README docs table if a new ADR was added
Witness bundle regenerated if tests or proof hash changed
Docker Hub image rebuilt only if Dockerfile / deps / runtime behaviour changed
Crate publishing only if a published crate's public API changed (publish in dependency order — see CLAUDE.md)
.gitignore updated for new build artifacts/binaries
Security review for new modules touching hardware/network boundaries

Security scan

bash
npx @claude-flow/cli@latest security scan            # after security-related changes

Also see docs/security-audit-wasm-edge-vendor.md, docs/qe-reports/, ADR-080 (QE remediation plan), ADR-093 (dashboard gap analysis).

QEMU firmware CI (ADR-061)

11-job workflow ("Firmware QEMU Tests"). Local QEMU helpers: scripts/qemu-esp32s3-test.sh, qemu-mesh-test.sh, qemu-chaos-test.sh, qemu-snapshot-test.sh, install-qemu.sh. Notes: espressif/idf:v5.4 container needs source $IDF_PATH/export.sh before pip; QEMU needs esptool merge_bin --fill-flash-size 8MB; WARNs (no real WiFi) are treated as OK in CI.

Reference

docs/WITNESS-LOG-028.md, docs/adr/ADR-028-esp32-capability-audit.md
scripts/generate-witness-bundle.sh, archive/v1/data/proof/verify.py
CLAUDE.md → "Validation & Witness Verification" + "Pre-Merge Checklist"
CLAUDE.local.md → QEMU CI pipeline fixes

Frequently asked questions

What does the Ruview Verify AI skill do?

Why use Ruview Verify on TypingMind?

Because you install it once and use it with any model. Ruview Verify is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ruview Verify in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ruvnet/RuView/tree/main/plugins/ruview/skills/ruview-verify. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ruview Verify?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ruview Verify?

As many as you like. As long as a model supports skills, you can use Ruview Verify with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ruview Verify AI skill free?

Yes. It is published on GitHub by ruvnet under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

RuView Verification & Witness Bundle

1. Rust workspace tests

2. Deterministic Python proof (Trust Kill Switch)

3. Python test suite (v1)

4. Generate the witness bundle (ADR-028)

5. Self-verify the bundle

Pre-merge checklist (from CLAUDE.md)

Security scan

QEMU firmware CI (ADR-061)

Reference

Frequently asked questions

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