Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use code-review-graph for local-first CLI/MCP code knowledge graphs, graph-backed code review, structural search, and repository-analysis integrations.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this repo skill when a task names code-review-graph, CRG, graph-backed code review, local MCP graph tools, blast-radius analysis, structural code search, risk-scored PR review, custom language parsing, or multi-repo code graph operations.
code-review-graph is a Python package and CLI/MCP server that builds a local SQLite code knowledge graph from Tree-sitter parses, updates it incrementally, and exposes token-efficient review/search workflows to coding agents.
install-and-setup.review-changes.graph-exploration.integrations-and-extensions.pip install code-review-graph
code-review-graph install
code-review-graph build
code-review-graph status
For an existing install, the bundled scripts/check_crg_install.py verifies import, package version, CLI discovery, and packaged docs without creating or updating a graph.
| Sub-skill | Read when |
|---|---|
| install-and-setup | Installing CRG, configuring MCP clients, building/updating the graph, checking status, running the MCP server, visualizing, watching, or uninstalling. |
| review-changes | Reviewing a diff or PR, computing blast radius, risk scoring, test gaps, token savings, affected flows, or rendering a PR review comment. |
| graph-exploration | Finding callers/callees/tests/imports, searching graph nodes, inspecting flows/communities/architecture, finding large functions, or previewing refactors. |
| integrations-and-extensions | Custom languages, embeddings/providers, wiki generation, multi-repo registry, daemon watch workflows, GitHub Action setup, or eval/benchmark reproduction. |
get_minimal_context_tool) when an MCP session is available.detail_level="minimal" unless the next step requires source snippets or full graph details.all unless the task needs them.Do not use this skill as a general-purpose LSP replacement, full source-code reading strategy, or benchmark runner unless the user explicitly asks for CRG workflows. This skill covers the Python CLI/MCP package and public integrations, not detailed development of the separate VS Code extension package.
This runtime skill is grounded by package metadata, public docs, installed-package inspection, existing repo-local CRG skills, and native test candidates spanning install, CLI, MCP, review, search, flows, communities, refactor, optional integrations, and CI workflow safety. Final verification artifacts live outside this runtime skill under the review/test artifact directory.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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