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Meta Paper Write

OrganizationPopular
TokenRhythm
meta-paper-write

Create a new academic paper or LaTeX manuscript through a citation-aware drafting and validation DAG; explicit full/PDF requests include artifact compilation. Do not use for repairing an existing manuscript, generic research reports, slides, or plotting.

Overview

PublisherTokenRhythm
Repositoryopensquilla
Skill namemeta-paper-write
Stars
7K
Forks
566
Bundled files
Instructions only
LicenseApache-2.0
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 TokenRhythm on GitHub. Read the source before you install it.

Installation

Install the Meta Paper Write 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/TokenRhythm/opensquilla.git /tmp/opensquilla
mkdir -p .claude/skills
cp -r /tmp/opensquilla/src/opensquilla/skills/bundled/meta-paper-write .claude/skills/meta-paper-write
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Meta Paper Write 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 Meta Paper Write 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 Meta Paper Write 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.

meta-paper-write (Meta-Skill)

Draft a long LaTeX manuscript by orchestrating paper-specific skills and bounded LLM synthesis. The pipeline now leads with explicit experiment design + placeholder figures/tables + citation provenance audit so the deliverable can be reviewed for academic rigor, not just length.

DAG (in order):

  1. paper_collect — extracts topic, mode, language, target length, audience, and reference count from the same turn without pausing for a form. Missing facts are marked as assumptions so first-pass paper requests complete inline.
  2. paper_preferences — expand the collected facts into a planning contract.
  3. search_papers — sends a clean academic query to Crossref, Brave, and Tavily; backend-specific academic filtering stays with each engine.
  4. refbibpaper-refbib-stub now extracts eprint/doi from arXiv/DOI URLs and tags each entry with note = {source: <domain>} so downstream gates can classify provenance without re-fetching.
  5. source_pack — curates unique, relevant, verifiable references and emits a machine-readable usable count against the integer citation target.
  6. source_readiness_gate — deterministically blocks before experiment design or drafting when the curated primary references cannot meet the target, reporting a concrete found/required count.
  7. experiment_designdecides how many figures and tables the paper needs based on RQs, hypotheses, analysis dimensions, and the target page budget. Every figure/table is tied to an RQ or analysis dimension; no decorative artefacts.
  8. figure_placeholders — render LaTeX \fbox{\parbox{...}} placeholder figure environments for each entry in FIGURE_PLAN. Zero matplotlib dependency.
  9. table_placeholders — render LaTeX \begin{tabular} placeholder tables for each entry in TABLE_PLAN. Cells contain ---/<TBD>; no fabricated numbers.
  10. analysis_outline — bind every figure/table id to a Discussion subsection that names potential findings + threats to validity, and covers every ANALYSIS_DIMENSION.
  11. outline — paper outline that ties Method to experiment design and Results to the figure/table plan.
  12. citation_plan — assigns concrete cite keys from refbib to claims; cannot invent keys.
  13. writing_plan + section authors — the explicit FULL_MANUSCRIPT path converts the user's page target into section-level target_words and citation budgets before prose is written; section authors obey that plan.
  14. final_manuscript_package — the lower-latency compact path still writes a complete, target-sized MANUSCRIPT_TEX with the figure/table/analysis blocks inlined verbatim, plus REFERENCES_BIB containing only the entries actually cited.
  15. Sanitize, materialize, and preflight — persist the manuscript under the runtime-owned run directory, then compare language-aware content units with TARGET_PAGES. An undersized draft receives one bounded substantive expansion; a strict second gate must pass before compilation.
  16. citation_map — strict markdown audit table: Cite Key | Cited Times | Title | URL/DOI/arXiv | Source Quality with INVALID / UNUSED / WEAK detection. Inlined into the final deliverable AND queryable per-run via opensquilla skills meta runs show.
  17. Citation and publication gates — deterministically read the numeric citation_map SUMMARY; blocks when cited keys are below CITATION_TARGET, INVALID > 0, a cited source is WEAK, or the sanitized artifact violates publication rules. They never trust an LLM verdict or a fixed citation count.
  18. Compile probe and bounded page repair — run the real XeLaTeX/BibTeX cycle and count pages with pypdf. A measured shortfall gets one final substantive expansion and one recompile. The runtime permits only the fixed precompile and page-shortfall repair ids and rejects citation, document-boundary, external-input, forced-page, and spacing commands.
  19. Final gates / compile_pdf / publish / delivery — re-run sanitizer, length, and publication checks. An unchanged successful probe is reused by input fingerprint; a changed manuscript is recompiled once and must meet TARGET_PAGES before publish_artifact can run.

Removed from the previous version:

  • paper_mode (llm_classify) — superseded by paper_collect
  • experiment (skill_exec → paper-experiment-stub, fake CSV) — superseded by experiment_design (real plan, not data). The bundled paper-experiment-stub skill was deleted with this rewrite.
  • plot (skill_exec → paper-plot-stub, matplotlib line chart) — superseded by figure_placeholders (zero-dependency LaTeX). The bundled paper-plot-stub skill was deleted with this rewrite.

The default path is COMPACT_SKELETON and ends with a compiled PDF without section-by-section drafting. Explicit full/PDF/long-form requests use FULL_MANUSCRIPT. If the topic is missing, paper_clarify pauses and asks the user before generation continues. The compiler refuses to synthesize a degraded PDF when the manuscript contract is missing.

Frequently asked questions

What does the Meta Paper Write AI skill do?

Create a new academic paper or LaTeX manuscript through a citation-aware drafting and validation DAG; explicit full/PDF requests include artifact compilation. Do not use for repairing an existing manuscript, generic research reports, slides, or plotting.

Why use Meta Paper Write on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TokenRhythm/opensquilla/tree/main/src/opensquilla/skills/bundled/meta-paper-write. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Meta Paper Write?

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 Meta Paper Write?

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

Is the Meta Paper Write AI skill free?

Yes. It is published on GitHub by TokenRhythm under the Apache-2.0 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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