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Investigate a Homecore capability by tracing relevant source files, tests, and accepted architecture decisions. Check documented status and limitations before proposing code, while keeping implementation presence separate from deployment compatibility or claims of Home Assistant parity.

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• Find source files and design decisions for a Homecore capability
• Check documented implementation status and limitations
• Review focused tests before proposing code changes

Integrate RuView through shell commands, an HTTP API, or WebAssembly builds, which run compiled code in browsers and supported edge devices. Covers survivor scan commands, result exports, API hosting, and browser or ESP32 deployment paths.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Script survivor scans and export results
• Expose RuView sensing through HTTP
• Build RuView for browser use

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

Train and evaluate models that estimate human poses from WiFi signals. Compare results against a model that always predicts the average pose, check for data leakage, and report measured improvements with clear evaluation conditions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a pose model using camera labels
• Evaluate pose estimation without a camera at inference
• Compare accuracy against an average-pose baseline

Plan and implement a unified AgentDB memory backend for agents. Migrate SQLite and Markdown data, add HNSW indexing for similarity search, and support shared memory and SONA learning patterns while preserving compatibility with existing systems.

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• Consolidating separate agent memory systems
• Migrating SQLite and Markdown data to AgentDB
• Finding stored information with similar meaning

Work with AI as a coding partner, choosing who writes code and who guides the work. Supports role switching, test generation, ongoing code review, debugging, and step-by-step explanations for learning.

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• Write code with ongoing AI review
• Direct AI implementation while reviewing its output
• Develop features by writing tests first

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.

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• Automating login flows
• Filling and submitting web forms
• Extracting product names, prices, and links

Organize software releases on GitHub, from version suggestions and release notes to testing, builds, and deployment. Coordinate multiple packages or repositories, roll changes out in stages, and configure rollback to restore an earlier release when problems occur.

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• Preparing version updates and release notes
• Coordinating releases across packages and repositories
• Deploying in stages with monitoring and rollback

Implement core TypeScript modules for claude-flow v3 using domain-driven design, which organizes code around distinct responsibilities. Build task and session domains, connect storage and services, and add unit and integration tests.

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• Build the core structure for claude-flow v3
• Implement task assignment and status rules
• Connect SQLite storage through dependency injection

Review GitHub pull requests, or proposed code changes, with specialized agents covering security, performance, architecture, and style. Generate actionable comments, apply quality thresholds, and coordinate automated review workflows.

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• Review authentication changes for security issues
• Assess performance impacts of database changes
• Check UI accessibility and coding conventions

Manage GitHub issues, synchronize project boards, and plan sprints, short development cycles. Coordinate multiple AI agents to break work into tasks, assign responsibilities, update progress, and produce reports on completion and blockers.

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• Sorting and prioritizing incoming GitHub issues
• Splitting large issues among AI agents
• Synchronizing task progress with project boards

Coordinate multiple AI agents for research, development, testing, and system analysis. It guides role assignment, communication structures, parallel and sequential tasks, shared memory, progress monitoring, and recovery, bringing separate contributions into reports or coordinated development workflows.

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• Gather research in parallel and compile a report
• Coordinate application design, implementation, and review
• Run unit, integration, security, and performance tests in parallel

Configure a RuView sensing deployment, including WiFi settings, detection thresholds, processing on devices, and coordinated nodes. It covers device provisioning, server options, firmware variants, and Cognitum Seed integration to adjust how the system operates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adjust WiFi channels and transmitter filters
• Tune presence, fall, and vital sensing settings
• Configure coordinated sensing across multiple nodes

Inspect RuView sensing hardware, including ESP32 nodes, mmWave radars, and LiDAR distance sensors. Find connected devices, read logs and live measurements, and use SSH to access supported read-only tools on another machine.

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• Finding sensing devices connected to a computer
• Checking CSI streams and serial logs
• Reading radar and LiDAR measurements

Review Homecore plugins by distinguishing built-in native code from external Wasm packages. Check signatures, publisher identity, paths, resource limits, and host permissions, then use the documented verification command to assess the plugin’s trust boundaries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review an external Wasm plugin before use
• Check built-in native plugin registration
• Inspect plugin resource limits and host permissions

Build agent memory with ReasoningBank and AgentDB. Record actions and outcomes, retrieve similar experiences, assess results, and consolidate memories into reusable patterns so agents can draw on past work when making decisions.

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• Build agent memory that reuses past experiences
• Record and assess sequences of agent actions
• Consolidate similar experiences into reusable patterns

Set up an ESP32-S3/C6 sensing node for RuView by choosing firmware, checking and flashing binaries, and configuring WiFi and capture settings. Use boot logs to confirm CSI, measurements of the WiFi channel, is being captured.

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• Choosing firmware for an ESP32 board
• Flashing firmware after checksum verification
• Configuring WiFi, channels, and MAC filters

Start using RuView by choosing a Docker demo, a source build, or a live ESP32 setup. The skill guides the next concrete step, distinguishing sample data playback from sensing an actual room with WiFi signals.

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• Trying the dashboard without sensing hardware
• Building and testing RuView from source
• Starting a live ESP32 room-sensing setup

Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.

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• Splitting large command files into focused modules
• Adding interactive setup prompts
• Recording command results through execution hooks

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.

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• Sharing stored patterns across multiple AgentDB instances
• Filtering semantic search by date or category
• Separating knowledge and conversation databases

Build custom Claude Code skills with clear instructions, reusable templates, and organized supporting files. The skill guides you through YAML metadata, meaning the skill’s name and description, and separates essential steps from detailed references.

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• Turning recurring workflows into custom skills
• Creating skill templates with supporting scripts
• Organizing long instructions into reference files

Run RuView applications for occupancy, breathing and heart rate, activity, pose, sleep, and room changes. Choose suitable examples and startup commands for a simulated demonstration or a working ESP32 setup that captures WiFi signal measurements.

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• Try presence and occupancy sensing
• Explore breathing, heart rate, and sleep monitoring
• Run activity and pose estimation examples

Train and evaluate RuView models using Wi-Fi measurements for pose estimation, retrieval, and adaptation to different environments. Follow laptop or GCloud GPU workflows, then package and publish trained models to Hugging Face.

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• Train pose estimation without cameras or labels
• Train pose estimation with paired camera and Wi-Fi data
• Adapt models to different rooms and environments

Get RuView running with a Docker demo using simulated WiFi signal data, source checks, or live sensing with supported hardware. Choose a starting path based on available equipment and open a working sensing dashboard.

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• Try the dashboard without sensing hardware
• Explore the API or develop the interface using simulated data
• Check the Rust build and Python verification