Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Inspect and analyze codebases using pygount for LOC counting, language breakdown, and code-vs-comment ratios. Use when asked to check lines of code, repo size, language composition, or codebase stats.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Analyze repositories for lines of code, language breakdown, file counts, and code-vs-comment ratios using pygount.
pip install --break-system-packages pygount 2>/dev/null || pip install pygount
Get a full language breakdown with file counts, code lines, and comment lines:
cd /path/to/repo
pygount --format=summary \
--folders-to-skip=".git,node_modules,venv,.venv,__pycache__,.cache,dist,build,.next,.tox,.eggs,*.egg-info" \
.
IMPORTANT: Always use --folders-to-skip to exclude dependency/build directories, otherwise pygount will crawl them and take a very long time or hang.
Adjust based on the project type:
# Python projects
--folders-to-skip=".git,venv,.venv,__pycache__,.cache,dist,build,.tox,.eggs,.mypy_cache"
# JavaScript/TypeScript projects
--folders-to-skip=".git,node_modules,dist,build,.next,.cache,.turbo,coverage"
# General catch-all
--folders-to-skip=".git,node_modules,venv,.venv,__pycache__,.cache,dist,build,.next,.tox,vendor,third_party"
# Only count Python files
pygount --suffix=py --format=summary .
# Only count Python and YAML
pygount --suffix=py,yaml,yml --format=summary .
# Default format shows per-file breakdown
pygount --folders-to-skip=".git,node_modules,venv" .
# Sort by code lines (pipe through sort)
pygount --folders-to-skip=".git,node_modules,venv" . | sort -t$'\t' -k1 -nr | head -20
# Summary table (default recommendation)
pygount --format=summary .
# JSON output for programmatic use
pygount --format=json .
# Pipe-friendly: Language, file count, code, docs, empty, string
pygount --format=summary . 2>/dev/null
The summary table columns:
Special pseudo-languages:
__empty__ — empty files__binary__ — binary files (images, compiled, etc.)__generated__ — auto-generated files (detected heuristically)__duplicate__ — files with identical content__unknown__ — unrecognized file types--folders-to-skip, pygount will crawl everything and may take minutes or hang on large dependency trees.wc -l directly.--suffix to target specific languages rather than scanning everything.还没有评价。欢迎分享你的使用感受。
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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