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.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install chen-li-17/paper-card-analyzerExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
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.Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
# 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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