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

• Scan the paper directory for existing artifacts

• Identify which phases are complete vs pending

• Build a dependency graph of remaining work

Step 2: Execute Pipeline Phases

Run phases in dependency order:

Phase

Skill

Input

Output

1. Literature

literature-search, literature-review

Topic

Knowledge base, BibTeX

1. Planning

research-planning

Knowledge base

Paper structure, task list

1. Code

experiment-code

Plan

Training/eval pipeline

1. Experiments

experiment-design

Code

Results JSON/CSV

1. Figures

figure-generation

Results

PNG figures

1. Tables

table-generation

Results

LaTeX tables

1. Writing

paper-writing-section

All above

main.tex sections

1. Citations

citation-management

Draft

references.bib

1. Formatting

latex-formatting

Draft

Formatted LaTeX

1. Compilation

paper-compilation

All

PDF

1. Review

self-review

PDF

Review scores

Step 3: State Propagation

After each phase completes:

• Save output artifacts to the paper directory

• Propagate results to downstream phases

• 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

• Merge all sections into main.tex

• Verify all \includegraphics files exist

• Verify all \cite keys exist in .bib

• Compile to PDF

• 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