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* feat(daily-paper): add daily paper cookbook workflow with schema and tests - Introduce daily paper schema types (DailyBriefOutput, PaperInfo, PaperNoteOutput, etc.) - Create daily paper cookbook module with analyze, collect, digest, rank, and select steps - Add cookbook entry point and integrate into main steps module - Replace job config export with daily brief output in schema exports - Add comprehensive unit tests covering pipeline, filtering, and output generation - Update dependencies including openai-codex and pypdf packages - Configure standalone daily paper cron job with proper scheduling and routing * test(daily_paper): update tests to use Claude Code wrapper exclusively - Add test to verify web search is disallowed by default in Claude Code - Update imports to include DailyBriefOutput, PaperNoteOutput, and PaperSelection schemas - Change test name from standalone_config_has_backend_split to reflect Claude Code only usage - Remove default agent wrapper and configure all steps to use Claude Code wrapper - Rename select_wrapper to cc_wrapper for clarity and consistency - Remove duplicate Claude Code wrapper initialization - Update test assertions to verify output schema usage matches expected sequence - Remove unused as_llm component from standalone configuration test * refactor(agent-wrapper): simplify skill resolution logic across all wrappers - Replace duplicate skill resolution code with centralized _resolve_project_skills method - Add project_path property with configurable relative path resolution - Introduce proper validation for skill names and directory existence - Change Codex wrapper to use project_path instead of workspace_path for skills - Add SKILL.md requirement validation for project skills - Remove redundant skill processing logic from individual wrappers * feat(daily_paper): add daily paper workflow with PDF analysis and brief generation - Implement shared state management and file helpers for daily-paper steps - Add PDF download and text extraction capabilities with arXiv integration - Create paper collection step with Hugging Face weekly/monthly rankings - Build ranking system using reciprocal-rank fusion with memory keyword scoring - Add Claude Code integration for paper analysis and detailed note generation - Implement digest step to create final five-minute brief from detailed notes - Add configuration for standalone daily cookbook application with cron scheduling - Create typed schema for paper information, selection, and output formats - Add atomic file writing with temporary file safety mechanisms - Implement exclusion logic for previously recommended papers and daily filters * feat(daily_paper): add DingTalk notification integration and enhance logging - Integrate DingTalk markdown send step to notify groups about daily paper briefs - Add comprehensive logging throughout daily paper workflow including start/finish events - Update daily paper analysis prompt to include code repository context requirement - Configure DingTalk notification in daily_cookbook.yaml with app credentials - Add dingtalk-stream dependency for proactive message API integration - Enhance daily paper README with DingTalk notification section and updated flow chart - Implement detailed logging for each step including paper processing and agent calls - Add test coverage for DingTalk markdown sending functionality and configuration - Update pre-commit config to exclude skills directory from checks - Add .claude/skills to gitignore for local development environment * refactor(dingtalk): move dingtalk_stream import to local scope and improve code safety - Moved global dingtalk_stream import to local scope in send.py to avoid eager loading - Added dynamic import with error handling for optional dependency cases - Updated test suite to verify lazy loading behavior works correctly - Fixed markdown title generation by using safe variable naming in wait.py - Enhanced test coverage for arxiv PDF download caching functionality - Updated application context initialization with proper resource directory configuration - Modified paper metadata to include source PDF path reference in output files * refactor(daily_paper): remove manifest system and store selection metadata in digest files - Remove JSON manifest creation and storage functionality - Store selection data directly in digest file frontmatter instead of separate manifest files - Add load_saved_selection method to rebuild selection from digest and paper-note metadata - Update README documentation to reflect new cookbook workflow architecture - Modify test cases to verify selection metadata in digest files instead of manifest JSON - Remove unused json import from multiple daily paper modules - Integrate PaperSelection schema for proper data validation in stored metadata * docs(daily_paper): add bilingual cookbook guides
18 lines
1 KiB
YAML
18 lines
1 KiB
YAML
select_user: |
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你是 AI 研究论文编辑。请从以下候选中选择恰好 {top_k} 篇最值得深入阅读的论文,并另外给出至多 3 个候补 ID。
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选择应兼顾研究价值、技术新颖性、潜在影响和可读性。融合分与榜单排名是重要依据,但不是唯一依据。
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与大模型 Agent 长期记忆、记忆检索、记忆整合、持续学习、个性化和上下文管理直接相关的高质量论文应优先考虑。
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只能选择输入候选集合中的 arXiv ID,不得编造论文或事实。输出简洁、可核验的选择理由。
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要求:
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1. selected 的 rank 必须从 1 到 {top_k} 连续排列。
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2. arxiv_id 必须逐字复制候选数据中的值,不能重复。
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3. reason 说明具体选择依据,不要只复述标题。
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4. memory_relevance 评价论文与大模型/Agent 长期记忆的相关程度。
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5. selection_reasoning 是面向读者的简短决策摘要,不要输出隐含的逐步思维过程。
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上一次校验反馈:{retry_feedback}
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# 候选论文
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{candidates}
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