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Paper Orchestration

CommunityPopular
Norman-bury
paper-orchestration

Use when a research-writing task spans multiple sections, medium-sized revisions, full-paper drafting, or repeated quality failures

Overview

PublisherNorman-bury
Repositoryresearch-writing-skill
Skill namepaper-orchestration
Stars
3.2K
Forks
214
Bundled files
Instructions only
LicenseMIT
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Norman-bury on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. 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.
2

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/Norman-bury/research-writing-skill.git /tmp/research-writing-skill
mkdir -p .claude/skills
cp -r /tmp/research-writing-skill/skills/paper-orchestration .claude/skills/paper-orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Orchestration 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 Orchestration 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 Orchestration 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 Orchestration

This skill controls the workflow around the writing skills. It prevents "one prompt writes the paper" behavior by forcing stage detection, task packets, review gates, and a capability-use audit.

Hard Gate

For any medium task or full-paper task, do not write manuscript text until these exist or are created:

  • plan/project-overview.md
  • plan/outline.md
  • plan/progress.md
  • a task packet for the current section or deliverable
  • for full-paper drafting or redrafting, plan/chapter-architecture.md

Medium task means any request that affects more than one paragraph, one subsection, one chapter, one figure/table set, or any claim tied to references or experiments.

Full-Paper Redraft Gate

For a full-paper draft, the controller must first lock the chapter architecture. Do not inherit an old chapter split if the user, target venue, or paper type implies a different structure.

For computer science / engineering SCI papers, use this body structure unless the user or target template explicitly requires otherwise:

  1. Introduction, with related work integrated into the motivation and research-gap argument.
  2. Methodology.
  3. Dataset and Experimental Setting, either standalone or integrated into Methodology/Results when short.
  4. Experimental Results and Analysis.
  5. Discussion.

Abstract, Conclusion, and References may be generated as supporting files, but they must not hide weak body chapters or inflate the chapter count. A standalone Related Work chapter is only allowed when the target outline explicitly demands it.

Create plan/chapter-architecture.md before drafting. It must list the exact required chapter files, target role, minimum body length, owner agent, and whether the chapter may use planning placeholders.

markdown
## Required chapter files
- chapters/01_Introduction.md | min_chars=4500 | agent=required | placeholders=no
- chapters/02_Methodology.md | min_chars=4500 | agent=required | placeholders=no

If this file exists, the quality gate must treat extra or missing chapter files as failures.

Multi-Agent Chapter Gate

For any full-paper draft or redraft, the controller must not write all chapters itself. It must:

  • create one task packet per major chapter;
  • dispatch a separate fresh agent for each major chapter;
  • give each agent only the relevant task packet, source excerpts, evidence map, experiment protocol, and hard rejection checks;
  • assign disjoint write ownership so that two agents never edit the same chapter file;
  • record the agent, prompt summary, inputs, output path, and review status in plan/chapter-agent-provenance.md;
  • perform a controller review after agent output before claiming progress.

If subagents are not available, stop and ask the user whether to continue in degraded single-agent mode. Do not silently fall back to one-pass generation.

Stage Detection

Classify the current work before acting:

StageTriggerRequired next skill
S0 Scopetopic, target, structure unclearbrainstorming-research
S1 Evidencereferences, Introduction, Related Workevidence-driven-writing + literature-review
S2 Methodmodel, algorithm, system, method sectionwriting-chapters with method flow gate
S3 Experimentssetup, results, tables, chartsexperiment-results-planning + figures-python
S4 Draftingsection textwriting-chapters
S5 Reviewquality, consistency, submission riskpeer-review + verification

Record the stage in plan/progress.md.

Task Packet

Before dispatching a section task, create a persistent packet under plan/task-packets/. Conversation-only packets are not acceptable for medium or full-paper work because they cannot be audited later.

markdown
## Task Packet
- Scope:
- Files to read:
- Files allowed to edit:
- Required skills:
- Evidence/data inputs:
- Required artifacts:
- Rejection checks:
- Validation commands:

Subagents must receive the task packet, not the entire vague project. If subagents are available, split independent work into literature mapping, method architecture, experiment planning, figure generation, and review. Do not let multiple agents edit the same file.

For chapter-writing packets, add:

  • Target chapter file and exclusive owner.
  • Required argument chain, in paragraph roles rather than bullet content.
  • Minimum prose length.
  • Required sources and data artifacts.
  • Prohibited structure, especially bullet-like lists, process notes, and old chapter scaffolding.
  • Required handoff format: status, file path changed, unresolved evidence/data gaps, and self-review.

Review Gates

Every medium task needs two reviews before completion:

  1. Spec compliance review: did the output match the task packet and required artifacts?
  2. Quality review: are claims supported, section logic coherent, and manuscript pollution absent?

If either review fails, fix before moving to the next task.

Capability-Use Audit

At the end of every medium task, write a short audit in plan/progress.md:

markdown
### Capability-use audit
- Required skills:
- Skills actually used:
- Inputs consumed:
- Inputs not used and why:
- Artifacts produced:
- Verification run:
- Remaining risk:

The task is not complete without this audit.

Common Failures

  • Treating user instructions as manuscript content. Convert instructions into structure or edits; never paste them into the body.
  • Claiming a section is "done" when it only has headings and placeholders.
  • Running style checks while skipping evidence, data, and claim checks.
  • Producing tables instead of prose to hide weak argumentation.
  • Keeping an old chapter split after the user rejects it. Rebuild the chapter architecture first.
  • Letting the controller write a full manuscript sequentially instead of using one independent agent per major chapter.
  • Accepting short, enumerative prose because it passes keyword checks. Length, paragraph flow, evidence use, and agent provenance must all be reviewed.

Frequently asked questions

What does the Paper Orchestration AI skill do?

Use when a research-writing task spans multiple sections, medium-sized revisions, full-paper drafting, or repeated quality failures

Why use Paper Orchestration on TypingMind?

Because you install it once and use it with any model. Paper Orchestration 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 Orchestration in TypingMind?

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

Which AI models can use Paper Orchestration?

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 Orchestration?

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

Is the Paper Orchestration AI skill free?

Yes. It is published on GitHub by Norman-bury under the MIT license. 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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