'uv' 검색 결과

Generate or evolve a repository-specific CLI, MCP and skills harness using MetaHarness, with bounded evaluation and explicit release authority.

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Detect texture-atlas regions and map each source part to its own UV rectangle or projected UV surface. Use when a texture pack must fit a Blender model 1:1, when textures look off/stretched, when alpha decals or lightmaps are provided, or before exporting a textured GLB for a brand mascot/logo.

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UV unwrap, atlas-map, project textures, use alpha decals, bake maps/lightmaps, and prepare texture-driven Blender assets for glTF/GLB export. Use whenever the user provides texture packs, texture atlases, decals, UV layouts, lightmaps, wants a texture to fit a mesh 1:1, or reports stretched/off textures. Pairs with reference-to-3d for template-accurate reconstruction and blender-materials for PBR values.

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Alluvial gold recovery using panning, sluicing, and classification. Covers site reading (geological indicators, stream dynamics, pay streak location), panning technique, sluice box operation, and responsible extraction practices. Use when prospecting in an area with known or suspected alluvial gold deposits, sampling a stream to test for gold presence, maximizing recovery during recreational panning, or assessing a site's gold potential before investing more effort.

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Publish and verify a released GEODE version across GitHub Release and PyPI/uv. Use for uv/uvx distribution, stable promotions, release repair runs, and public installation-channel audits.

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Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.

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Test-driven development in Python using uv as the package manager. Covers the red-green-refactor cycle, vertical slicing, and uv project setup.

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This skill should be used when working with Python projects that use uv for package and project management. Use this skill for running Python scripts and CLI tools with `uv run`, managing dependencies, creating projects, handling virtual environments, and executing commands within isolated project environments. Essential for projects with pyproject.toml files.

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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.

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

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

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

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.

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

Improve the MCP server that connects claude-flow v3 to tools. It guides connection reuse, faster tool lookup, request distribution, and communication changes, with monitoring to evaluate response times, errors, memory use, and cache performance.

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• Analyze slow MCP startup and tool responses
• Reuse connections and clean up idle ones
• Improve tool lookup and distribute requests

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

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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• Turn code analysis into an improvement plan
• Prioritize and refactor existing code
• Find test gaps and generate unit tests

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

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

Review and strengthen security in claude-flow v3 by addressing dependency risks, password hashing, credential generation, input validation, file paths, and command execution. It also guides threat modeling and tests for security-critical code.

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• Reviewing security boundaries and potential threats
• Updating dependencies and checking audit results
• Revising password hashing and credential generation

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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• Automatically format and validate edited code
• Share progress across multiple agents
• Save and restore development sessions

Coordinate 15 agents implementing claude-flow v3 across security, core systems, integration, testing, performance, and release work. Provides role assignments, task dependencies, GitHub tracking examples, and a four-phase schedule spanning 14 weeks.

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• Assign claude-flow v3 work across 15 agents
• Run tasks in parallel while respecting dependencies
• Track milestones and progress through GitHub issues

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

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

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

Coordinate multiple AI agents with agentic-flow to run tasks in parallel or through ordered stages. Configure shared memory, team structure, workload distribution, retries, and task reassignment, then monitor how the group performs.

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• Run API, interface, and test work in parallel
• Build a design-to-review workflow with ordered stages
• Share specifications and state across agents