632 个（ruvnet 的仓库） · 按人气排序
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.
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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.
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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.
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Review a Homecore server operation plan before startup. Check authentication, restoration order, feature tests, speech provider configuration, and handling of pairing secrets. The skill reviews the plan without starting the server.
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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.
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Set up radar sensing for contactless heart rate, breathing rate, presence, and distance measurements. It covers supported hardware, firmware, and combining radar with WiFi CSI, sensing data derived from wireless signals, for shared readings and 3D views.
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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.
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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.
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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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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.
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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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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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Check code and agent outputs, track reliability scores, and roll back changes that fail a chosen threshold. Includes quality reports, live monitoring, and integration with automated build and deployment workflows.
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Automate checks, code formatting, agent coordination, and session records around Claude Code operations. Uses hooks, actions triggered before or after work, to preserve context and connect development tasks with shared memory.
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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.
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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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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.
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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.
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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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Set up RuView sensing nodes by building and flashing ESP32 firmware, configuring WiFi and the receiving address, and checking serial logs. Diagnose missing CSI streams, the WiFi signal measurements sent by sensing nodes.
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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.
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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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Connect agent tasks into a sequence where each step receives the previous output. Use custom prompts or predefined workflows to analyze code, plan refactoring, generate tests, optimize performance, or transform data through multiple stages.
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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.
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