stock-data-skill
OpenClaw Skill for stock data analysis
Analyze `paper-parse` outputs and generate a research-oriented paper card directly in natural language. Use this skill after paper parsing when you need a structured summary of contributions, method, experiments, limitations, reproducibility notes, and future work without running any extra script.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install chen-li-17/paper-card-analyzer打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Generate a research-oriented paper-card from paper-parse results using direct natural-language analysis.
Read artifacts produced by paper-parse:
*_content.md (full parsed paper content in markdown)*_parsed.json (metadata and figures)Produce the paper card in English by default, with balanced depth, and always save outputs in the same folder as the selected *_content.md and *_parsed.json.
Always save:
paper-card.md
paper-card.json
paper-card-feedback.md (feedback log and revision history)The generated card uses this fixed section order:
*_content.md and one *_parsed.json in the same folder.*_parsed.json:
title, paper_name, num_pages, figures.*_content.md and extract evidence by section:
paper-card.md and paper-card.json.paper-card-feedback.md with: round number, user request, key edits.这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# Paper Card Analyzer Generate a research-oriented `paper-card` from `paper-parse` results using direct natural-language analysis. ## Input Expectations Read artifacts produced by `paper-parse`: - `*_content.md` (full parsed paper content in markdown) - `*_parsed.json` (metadata and figures) ## Output Produce the paper card in English by default, with balanced depth, and always save outputs in the same folder as the selected `*_content.md` and `*_parsed.json`. Always save: - `paper-card.md` - `paper-card.json` - `paper-card-feedback.md` (feedback log and revision history) The generated card uses this fixed section order: 1. Paper Snapshot 2. Research Problem and Motivation 3. Core Contributions 4. Method Overview 5. Experimental Setup 6. Main Results and Evidence 7. Ablation and Analysis Findings 8. Limitations and Threats to Validity 9. Reproducibility Notes 10. Open Questions and Future Work ## Workflow 1. Identify the target pair of files: - Preferred: one `*_content.md` and one `*_parsed.json` in the same folder. - If multiple candidates exist, ask user to pick one pair. 2. Read parsed metadata from `*_parsed.json`: - `title`, `paper_name`, `num_pages`, `figures`. 3. Read `*_content.md` and extract evidence by section: - abstract/introduction/method/experiments/results/ablation/limitations/conclusion. 4. Write a research-oriented card: - Prioritize scientific novelty, methodological logic, evidence strength, validity threats, and reproducibility. 5. Save first draft to the same folder: - `paper-card.md` and `paper-card.json`. 6. Request human feedback and revise: - Ask what to correct, expand, or make stricter. - Update card and save again (overwrite current files). - Append each round to `paper-card-feedback.md` with: round number, user request, key edits. 7. Repeat revision rounds until the user explicitly confirms satisfaction. 8. Keep uncertainty explicit: - If a section is missing, say "Not clearly stated in parsed...
OpenClaw Skill for stock data analysis
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