菜单
Orchestrate the full paper pipeline end-to-end. Manage state propagation between phases (literature → plan → code → experiments → figures → tables → writing → review), support checkpointing and resumption. Use for assembling a complete paper from components.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | paper-assembly |
| description | Orchestrate the full paper pipeline end-to-end. Manage state propagation between phases (literature → plan → code → experiments → figures → tables → writing → review), support checkpointing and resumption. Use for assembling a complete paper from components. |
| argument-hint | ["paper-directory"] |
Orchestrate the entire paper pipeline end-to-end with state management and checkpointing.
$0 — Paper project directory or paper plan~/.claude/skills/paper-assembly/references/orchestration-patterns.mdpython ~/.claude/skills/paper-assembly/scripts/assembly_checker.py --dir paper/ --output checkpoint.json
python ~/.claude/skills/paper-assembly/scripts/assembly_checker.py --dir paper/ --verbose
Scans paper directory, checks 9 pipeline phases, reports missing artifacts, suggests next steps.
Run phases in dependency order:
| Phase | Skill | Input | Output |
|---|---|---|---|
| 1. Literature | literature-search, literature-review | Topic | Knowledge base, BibTeX |
| 2. Planning | research-planning | Knowledge base | Paper structure, task list |
| 3. Code | experiment-code | Plan | Training/eval pipeline |
| 4. Experiments | experiment-design | Code | Results JSON/CSV |
| 5. Figures | figure-generation | Results | PNG figures |
| 6. Tables | table-generation | Results | LaTeX tables |
| 7. Writing | paper-writing-section | All above | main.tex sections |
| 8. Citations | citation-management | Draft | references.bib |
| 9. Formatting | latex-formatting | Draft | Formatted LaTeX |
| 10. Compilation | paper-compilation | All | |
| 11. Review | self-review | Review scores |
After each phase completes:
Before proceeding to the next phase:
generate_ideas → experiments → writeup → review
# Propagate results to all downstream agents
set_agent_attr("dataset_code", code)
set_agent_attr("results", results_json)
Human can intervene at any phase boundary for review/correction.
{
"project": "paper-name",
"phases_completed": ["literature", "planning", "code"],
"current_phase": "experiments",
"artifacts": {
"literature": "knowledge_base.json",
"plan": "research_plan.json",
"code": "experiments/",
"results": null
},
"last_updated": "2024-01-15T10:30:00Z"
}
Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity.
Explore and analyze GitHub repositories related to a research topic. Reads deep-research output, discovers repos from multiple sources, deeply analyzes code, and produces integration blueprints.
Manage BibTeX citations for LaTeX papers. Harvest missing citations from a draft using Semantic Scholar, validate cite keys against .bib files, deduplicate entries, and format bibliography. Use when working with references, BibTeX, or citations.
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Write ML experiment code with iterative improvement. Generate training/evaluation pipelines, debug errors, and optimize results through code reflection. Use when implementing experiments for a research paper.
Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.