bird-dogger 是做什么的？
Agent 技能，用于 bird-dogging（跨团队任务追踪）——通过可插拔的跟踪器（Jira、GitHub Issues 等）推动跨团队工作项完成，并利用本地 LLM 进行分类和生成可定制的状态报告。
用于跨团队追踪（bird-dogging）的 Agent 技能——通过可插拔的跟踪器（Jira、GitHub Issues 等）推动跨团队工作项完成，并具备本地 LLM 分类和可定制的状态报告。
此技能旨在帮助 AI 代理（如 Claude Code、Cursor 或 Codex）进行跨团队工作项的主动追踪与推进，确保任务不被遗漏并高效完成。它支持连接到多个常见的工单跟踪系统（如 Jira、GitHub Issues 等），通过可插拔的适配器统一管理。技能内置了本地 LLM 驱动的智能分类机制，能够自动分析工作项的重要性和优先级，辅助决策。同时，它提供可自定义的状态报告功能，让用户或代理能够灵活生成汇总信息，全面掌控项目进度。该设计适用于需要跨平台协调工作的开发团队，尤其在 AI 辅助编码的环境中，可显著减少手动协调成本，提升交付效率。
npx skills add colchuck-ai/bird-doggerInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Agent 技能，用于 bird-dogging（跨团队任务追踪）——通过可插拔的跟踪器（Jira、GitHub Issues 等）推动跨团队工作项完成，并利用本地 LLM 进行分类和生成可定制的状态报告。
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.