Agent Skills: 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”。
ID: majiayu000/claude-arsenal/github-trending
28026
Install this agent skill to your local
Skill Files
The files that make up github-trending.
Loading file tree…
More agent skills
- dyad:swarm-pr-reviewTeam-based PR review using Claude Code swarm. Spawns three specialized teammates (correctness expert, code health expert, UX wizard) who review the PR diff, discuss findings with each other, and reach consensus on real issues. Posts a summary with merge verdict and inline comments for HIGH/MEDIUM issues.
- lexiconDefine, review, and audit a project's domain vocabulary across code and documentation. Use when creating or editing a lexicon, glossary, or term list; auditing against a lexicon for drift, invented vocabulary, and deprecated terms; critiquing project vernacular; reconciling conceptual models; or planning naming refactors.
- rag-systemsRetrieval Augmented Generation systems with vector search, document processing, and hybrid retrieval.
- agent-creatorCreate and configure AiderDesk agent profiles by defining tool groups, approval rules, system prompts, subagent settings, subagent filtering, and provider/model selection. Use when setting up a new agent, creating a profile, or configuring agent tools, permissions, and subagent behavior.
- research-citation-check当用户明确要求"核查/优化综述 `{主题}_review.tex` 的正文引用"、"运行 research-citation-check"，或要求使用旧名 check-review-alignment skill 时使用。通过宿主 AI 的语义理解逐条核查引用是否与文献内容吻合，只在发现致命性引用错误时对"包含引用的句子"做最小化改写，并优先复用 `research-literature-review` 的渲染脚本输出 PDF/Word，过渡期允许 fallback 到旧名 systematic-literature-review。核心原则：不为了改而改，无法确定是否为致命性错误时保留原样并在报告中警告。⚠️ 不适用：用户只是想生成系统综述正文（应使用 research-literature-review）；用户只是想新增/核对 BibTeX 条目（应使用专门的 bib 管理流程）。
- residual-edge-analyzerSeparate a strategy return series into declared baseline exposure and residual edge with returns-based OLS attribution, HAC inference, rolling stability, alternate-baseline sensitivity, and regime breakdowns. Use when evaluating whether backtest, out-of-sample, or live returns contain independent alpha beyond market, equal-weight, momentum, sector, or user-supplied factor returns; when explaining whether a drawdown came from baseline exposure or strategy-specific behavior; or when a strategy needs an attribution quality gate after backtesting. Do not use for holdings-based Brinson attribution, feature-level Shapley explanations, or analysis from summary metrics without a dated return series.