Paper Assembly

Community

lingzhi227

paper-assembly

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.

Overview

Publisher	lingzhi227
Repository	`agent-research-skills`
Skill name	`paper-assembly`
Stars	426
Forks	46
Bundled files	2
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by lingzhi227 on GitHub. Read the source before you install it.

Installation

Install the Paper Assembly AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/lingzhi227/agent-research-skills/tree/main/skills/paper-assembly
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/lingzhi227/agent-research-skills.git /tmp/agent-research-skills
mkdir -p .claude/skills
cp -r /tmp/agent-research-skills/skills/paper-assembly .claude/skills/paper-assembly

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Assembly in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Paper Assembly on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Paper Assembly is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
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)

python
# 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

json
{
  "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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Paper Assembly AI skill do?

Why use Paper Assembly on TypingMind?

Because you install it once and use it with any model. Paper Assembly is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Paper Assembly in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lingzhi227/agent-research-skills/tree/main/skills/paper-assembly. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Paper Assembly?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Paper Assembly?

As many as you like. As long as a model supports skills, you can use Paper Assembly with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Paper Assembly AI skill free?

It is published on GitHub by lingzhi227. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Paper Assembly

Input

References

Scripts

Check pipeline completeness

Workflow

Step 1: Assess Current State

Step 2: Execute Pipeline Phases

Step 3: State Propagation

Step 4: Quality Gates

Step 5: Final Assembly

Orchestration Patterns

Sequential Pipeline (AI-Scientist)

Multi-Agent State Broadcasting (AgentLaboratory)

Copilot Mode (AgentLaboratory)

Checkpoint Format

Rules

Related Skills

Bundled files

Frequently asked questions

Related AI Skills

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