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.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
日本語の概要は準備中です。原文の説明を表示しています。
Web browser automation with AI-optimized snapshots for claude-flow agents
日本語の概要は準備中です。原文の説明を表示しています。