requesting-code-review
Dispatch code review subagents with focused context to catch issues before they compound.
github-trending
GitHub Trending 探索与分析。用于发现热门开源项目、技术趋势、开发者偏好,帮助理解技术社区的兴趣走向。使用场景包括“看看今天 GitHub 什么火了”、“Rust 最近热门项目”、“AI 领域趋势日报”、“这个方向有没有值得关注的开源项目”、“帮我做个 GitHub trending 分析”。
Full skill instructions
2026 版核心认知:GitHub Trending 已是严重被 hype 和 AI slop 污染的榜单(Karpathy 等反复确认)。本 skill 的唯一价值是「结构化数据 + 严格过滤 + 写出真实 angle」,而不是再输出一份 list。
职责边界(严格遵守):
/scout-to-article 或 /blog-write)/multi-ai-research)不适合:纯快速事实查询(直接让 Claude 回答即可)。
拿干净数据(优先用脚本)
# 在 skill 目录执行;如果 skill 已安装到 ~/.claude/skills,也可以换成对应安装路径
cd /path/to/spellbook/skills/github-trending
# 今日总榜 TOP 8
python3 scripts/fetch_trending.py --since daily --limit 8
# Rust 周榜
python3 scripts/fetch_trending.py --since weekly --language Rust --limit 6
# 开发者榜
python3 scripts/fetch_trending.py --developers --since daily --limit 10
脚本输出结构化 JSON,绝不直接 WebFetch 原始 HTML。
筛 TOP N + 过滤 hype
reference/extended.md 中的「Hype 红旗」快速排除必要时轻度 enrich
套模板 + 必写 Angle
输出 + 归档(可选)
首次使用执行:
python3 -m pip install -r requirements.txt
如果没有 requirements.txt,手动安装:
python3 -m pip install requests beautifulsoup4 lxml
脚本位置:scripts/fetch_trending.py(相对本 skill 目录)。
支持参数见脚本 --help。所有错误都会以清晰 JSON 返回,绝不静默失败。
日报结尾必须包含「Angle」段,否则视为不合格输出。
好 Angle 特征:
reference/extended.md 里的评估框架写对比表。/loop 技能每天定时跑脚本 + 分析,输出到固定目录。/multi-ai-research。scout-to-article。所有详细框架(数据源、评估维度、hype 识别、趋势分类、好/坏 Angle 示例、scraper 维护提示、2026 社区共识)已移至 reference/extended.md。
日常触发时不需要读它。只有你要定制模板、维护脚本、或做高精度分析时再加载。
更新记录:2026-05 完整重构(引入可靠 scraper 层 + 严格 pipeline + 拆分 reference + 移除所有硬编码日期/旧趋势列表)。数据永远以脚本实时输出为准。
Dispatch code review subagents with focused context to catch issues before they compound.
Evaluate code review feedback with technical rigor before implementing, avoiding performative agreement and blind implementation.
Isolated git worktrees with smart directory selection and safety verification.
Structured workflow for completing development branches with test verification and merge/PR options.
review
Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch.
Standardized git commits using Conventional Commits specification with intelligent diff analysis and message generation.
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, or when you need to organize work across multiple parallel streams.
Code review focused exclusively on over-engineering. Finds what to delete: reinvented standard library, unneeded dependencies, speculative abstractions, dead flexibility. One line per finding: location, what to cut, what replaces it. Complements correctness-focused review, this one only hunts complexity.
Generate Excalidraw diagrams from natural language descriptions in multiple formats.
Whole-repo audit for over-engineering. Like ponytail-review, but scans the entire codebase instead of a diff: a ranked list of what to delete, simplify, or replace with stdlib/native equivalents. One-shot report, does not apply fixes.
Systematic code review practices for constructive feedback, bug detection, and team knowledge sharing.
Opus.ai: Revolutionize Your Web Experience
Build a no-code AI app in minutes.
An IDE for code migration from legacy to modern frameworks through coding agents.
Automate CGI animation in live-action scenes
Branded artistic QR-code concepts
Launch a website in seconds with AI.
Streamline Your Coding Experience with AI Code Helper
Convert any screenshot or design to clean code.