algorithm-design
Design algorithms with LaTeX pseudocode and UML diagrams. Generate algorithmic environments,…
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.
$ npx -y skills add lingzhi227/agent-research-skills --skill paper-assembly --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/paper-assemblyContext preview
The summary Claude sees to decide when to auto-load this skill.
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.
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.
python ~/.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.
1. Scan the paper directory for existing artifacts 2. Identify which phases are complete vs pending 3. Build a dependency graph of remaining work
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 | PDF | | 11. Review | self-review | PDF | Review scores |
After each phase completes: 1. Save output artifacts to the paper directory 2. Propagate results to downstream phases 3. Update the progress checkpoint file
Before proceeding to the next phase:
1. Merge all sections into main.tex 2. Verify all \includegraphics files exist 3. Verify all \cite keys exist in .bib 4. Compile to PDF 5. Run self-review for quality check
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"
}31 skills for Claude Code covering the full academic research paper lifecycle — from literature search to slide generation — plus GitHub repository analysis for research topics. Extracted from 17 GitHub repos studying LLM-agent-driven research automation.
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