Verify RuView pipeline output against a reference hash, package evidence for others to check, and review accuracy statements. The skill distinguishes measured, claimed, and synthetic results and requires real device logs for hardware validation.
中文简介正在准备中。当前显示英语简介。
Prove a RuView result is real — run the deterministic SHA-256 proof and the witness bundle (ADR-028), and lint any claim for MEASURED-vs-CLAIMED honesty.
安装前可以查看交给 AI 代理的指令内容。
The "prove everything" skill. Nothing ships as validated without this.
ruview_verify runs archive/v1/data/proof/verify.py: it feeds a reference signal
through the production pipeline and hashes the output against
expected_features.sha256. Must print VERDICT: PASS. If numpy/scipy changed the
hash, regenerate with verify.py --generate-hash then re-verify.
For a release-grade attestation:
bash scripts/generate-witness-bundle.sh
cd dist/witness-bundle-ADR028-*/ && bash VERIFY.sh # must be 7/7 PASS
Contains the Rust test log, the proof + expected hash, firmware SHA-256 manifest, and crate versions — a recipient can re-verify with one command.
Run ruview_claim_check {text} on any report, README section, PR body, or model card
before quoting accuracy. It flags:
A firmware fix is not "hardware-validated" without a captured boot log on real
silicon (e.g. the v0.8.1-esp32 rev-v0.2 validation: running headless so CSI captures (#1000) + CSI filter upgraded to MGMT+DATA + a no-false-detect mmwave
probe). Do not merge or release on a build-passes signal alone.
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Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
中文简介正在准备中。当前显示英语简介。
Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.
中文简介正在准备中。当前显示英语简介。
Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.
中文简介正在准备中。当前显示英语简介。
Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.
中文简介正在准备中。当前显示英语简介。
Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.
中文简介正在准备中。当前显示英语简介。
Automate browser tasks with agent-browser, using snapshots that label page elements for the agent. Handle login flows, fill and submit forms, extract page information, and capture screenshots through a repeatable navigation and interaction workflow.
中文简介正在准备中。当前显示英语简介。