paper-assembly

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]

Paper Assembly

Orchestrate the entire paper pipeline end-to-end with state management and checkpointing.

Input

• $0 — Paper project directory or paper plan

References

• Orchestration patterns and state management: ~/.claude/skills/paper-assembly/references/orchestration-patterns.md

Scripts

Check pipeline completeness

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.

Workflow

Step 1: Assess Current State

1. Scan the paper directory for existing artifacts
2. Identify which phases are complete vs pending
3. Build a dependency graph of remaining work

Step 2: Execute Pipeline Phases

Run phases in dependency order:

Step 3: State Propagation

After each phase completes:

1. Save output artifacts to the paper directory
2. Propagate results to downstream phases
3. Update the progress checkpoint file

Step 4: Quality Gates

Before proceeding to the next phase:

• Verify all required outputs exist
• Check for consistency (e.g., all cited keys in .bib)
• Validate figures/tables match experimental results

Step 5: Final Assembly

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

Orchestration Patterns

Sequential Pipeline (AI-Scientist)

generate_ideas → experiments → writeup → review

Multi-Agent State Broadcasting (AgentLaboratory)

# Propagate results to all downstream agents
set_agent_attr("dataset_code", code)
set_agent_attr("results", results_json)

Copilot Mode (AgentLaboratory)

Human can intervene at any phase boundary for review/correction.

Checkpoint Format

{
  "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"
}

Rules

• Never skip phases — each depends on previous outputs
• Save checkpoints after every phase completion
• Human review is recommended at phase boundaries
• All numbers in the paper must trace to actual experiment logs
• Re-run downstream phases if upstream changes

Related Skills

• Upstream: all other skills (this is the orchestrator)
• Downstream: paper-compilation, self-review
• See also: research-planning