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codebase-inspection
Analyze repositories for lines of code, language breakdown, file counts, and code-vs-comment ratios using `pygount`.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Analyze repositories for lines of code, language breakdown, file counts, and code-vs-comment ratios using `pygount`.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
引用三验 — 参考文献是否存在(L1) + 引用是否得当(L2) + 引用是否全面(L3)。三位一体验证管线,从DOI验真到语义审查到遗漏检测。
**触发条件**: 对一批论文(10-34 篇)批量处理 `step_quality_check.md` 中的 quality_score 并写入 `state.json`。
子skill | NotebookLM CLI全功能指南 — Q&A知识提取、内容生成(报告/视频/音频/信息图/幻灯片)、文献检索。响应paper-pipeline的P1阶段调用。
生产力工具 — Airtable、Google Workspace、Linear、Notion、Jupyter等。
Complete paper pipeline: retrieval, extraction, quality review, analysis, and publication.
双循环进化:内部反思(P0) + 外部吸收(P1)。Cross-project absorption methodology — multi-round cross-project comparison, active project tracking, self-expanding keyword discovery. 动灵驱动吸收(Entelechy-Driven Absorption v4.3).
| name | codebase-inspection |
| description | Analyze repositories for lines of code, language breakdown, file counts, and code-vs-comment ratios using `pygount`. |
| version | 1.0.0 |
| license | MIT |
| author | Synthos |
| metadata | {"synthos":{"signature":"task_desc: str, params: dict -> result: dict","atom_type":"skill","priority":"P2","related_skills":[]}} |
request: str, context: dict — 用户请求描述、上下文信息result: dict — 技能执行结果(结构因技能而异)对应原则:P2(机械原子暴露输入输出规范)
Analyze repositories for lines of code, language breakdown, file counts, and code-vs-comment ratios using pygount.
This skill is activated when the user asks you to:
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.Before considering the codebase inspection task complete:
--folders-to-skip used to exclude dependency/build directories--suffix filter