paper-assembly

SKILL.md

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

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

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