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Turn a feature request into a minimal implementation plan before coding. Identify which files need changes, explain why, define the smallest required interface, and flag broader effects or expanded permissions for the implementer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a feature before coding
• Identify files affected by a change
• Review scope and permission changes

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking pipeline output against a reference hash
• Preparing a release evidence bundle for independent verification
• Reviewing accuracy statements in reports and READMEs

Improve an AI agent's harness, the software that organizes its work, while keeping the model fixed. Test changes in a sandbox, measure their results, and retain variants that pass tests and demonstrate improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tune an agent harness without changing its model
• Compare changes to planning, context, or retry policies
• Try an offline dry run without executing tests

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reduce memory use in an AgentDB application
• Tune search speed while preserving accuracy
• Plan settings for millions of vectors

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Verify RuView changes with Rust and Python tests, output hashes that detect processing changes, and firmware hash records. Packages test logs and audit evidence into a witness bundle that recipients can check with a single command.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify a RuView build after significant changes
• Check tests and documentation before merging a PR
• Prepare a verification bundle for a recipient

Create and improve GitHub Actions workflows for automated testing and deployment. Coordinate multiple AI agents to review pull requests, investigate failed runs, scan for security issues, and manage releases across repositories.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create workflows for automated testing and deployment
• Investigate failed runs and suggest fixes
• Coordinate code reviews and security checks

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Implement agents that use recorded task outcomes to choose future approaches. ReasoningBank supports matching past patterns, comparing strategies, retaining experience in AgentDB, and tracking how strategy success and learning change over time.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose task strategies using past outcomes
• Compare approaches to code review or bug fixing
• Match current situations to learned patterns

Connect RuView sensing data to AI agents through tools for presence, breathing, and heart rate. Explore agent coordination and prototype storing agent state and decisions alongside sensing data in a shared RVF container.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Read live presence and vital-sign data from an agent
• Prototype agent responses to room-presence events
• Design coordination between sensing subagents

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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

Review Home Assistant migration behavior for input validation, configuration preservation, and output that does not overwrite existing files. Check storage schema versions, keep secrets out of diagnostics, and identify incomplete automation or integration behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing supported storage schema versions
• Checking preservation of unfamiliar configuration fields
• Reviewing writes that must not overwrite existing files

Coordinate changes across multiple GitHub repositories with teams of AI agents. Align package versions, synchronize documentation, standardize project structures, and organize integration tests and pull requests for updates that affect several projects.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aligning dependency versions across repositories
• Synchronizing shared documentation
• Standardizing repository structures and CI workflows

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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

Run the smallest relevant Homecore test profile against a specified repository checkout. Choose core, Wasmtime, HAP, or full tests to validate the selected software paths, while keeping the limits of passing results clear.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running Homecore core tests
• Checking the Wasmtime software paths
• Running the HAP test profile

Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare performance before and after v3 optimization
• Evaluate vector search speed and memory use
• Track performance regressions over time