Paper writing

Paper Writing

Overview

This skill implements the exact Hierarchical Manuscript Drafting and Iterative Refinement process described in section 4.4 of the NeuroClaw architecture and the accompanying flowchart.

It acts as the Manuscript Composer within an end-to-end multi-agent framework:

• Reads the latest versions of IDEA.md, METHOD.md, and EXPERIMENT.md from the current workspace (continuously updated by the related skills research-idea, method-design, and experiment-controller).
• If any of the three files are missing or empty, it immediately prompts the user to provide the research idea, experimental methods, and results before proceeding.
• First asks the user whether the final output should be plain text (.md) or LaTeX (.tex).
• When LaTeX format is selected, it asks the user to provide a custom template file (e.g., template.tex). If none is supplied, it automatically uses the default IEEE template (conference or journal version with appropriate \documentclass, packages, and structure).
• When processing EXPERIMENT.md, if sufficient quantitative results are present (metrics, comparisons, ablation studies, numerical values, etc.), the skill automatically generates and inserts well-formatted tables (Markdown tables for plain-text mode; proper LaTeX tabular or table environment for LaTeX mode) into the Results and Discussion sections.
• Then executes the hierarchical flow step by step (never one-shot generation), saving the output of every single step as a separate Markdown file in the workspace root (e.g., 01_section_content.md, 02_ethics_review.md, ..., 10_final_draft.md). This enforces incremental execution and allows OpenClaw to resume or inspect progress at any stage.

The process preserves narrative coherence, reuses semantic anchors from previous sections, automatically generates figures from experimental logs, ensures ethical dataset citation, improves scientific storytelling, verifies cross-references, and applies self-healing compilation for LaTeX.

Research use only — the output is a high-quality draft ready for final human polishing and journal submission.

Quick Reference (Hierarchical Flow)

Step	Description	Output File
1. Section Content Generation	Build each section from IDEA.md + METHOD.md + EXPERIMENT.md (auto-generate tables from results if data is sufficient)	01_section_content.md
2. Ethics Review	Verify dataset origin, license, and ethical approval	02_ethics_review.md
3. Iterative Optimization	Refine content based on previous sections	03_iterative_optimization.md
4. Narrative Storytelling	Convert technical content into coherent scientific story	04_narrative_storytelling.md
5. De-AI Polishing	Remove AI artifacts and improve natural academic tone	05_de_ai_polishing.md
6. Logic & Continuity Review	Ensure logical flow and cross-section consistency	06_logic_continuity_review.md
7. Length Control	Adjust section lengths to target journal limits	07_length_control.md
8. Reference Review & Repair	Verify and fix all citations and cross-references	08_reference_review.md
9. LaTeX Error Repair	(LaTeX only) Automatically correct compilation errors	09_latex_error_repair.md
10. Final Draft Generation	Produce and save the complete manuscript	10_final_draft.md or 10_final_draft.tex

Installation

# Place files in: skills/paper-writing/

Scripts

The scripts/ directory contains reusable Python modules derived from nature-skills:

File	Purpose
scripts/nature_figure_style.py	Publication style rcParams, color palettes (PALETTE, PALETTE_NMI_PASTEL, etc.), and helper functions (apply_publication_style, is_dark, add_panel_label, finalize_figure)
scripts/figure_templates.py	Complete figure templates: grouped bar with legend panel, alpha-graduated ablation bar, multi-panel trend, dual-colormap heatmap
scripts/section_phrasebank.py	Section-specific move orders, phrase families, evidence strength verbs, and transition words for all manuscript sections
scripts/data_statement_templates.py	12 Data Availability statement templates, anti-patterns to flag, and FAIR audit questions

Integrated Knowledge from nature-skills

The paper-writing skill incorporates academic writing standards from the nature-skills project (MIT License, Copyright 2026 Yuan Yizhe). The following rules and strategies are applied during the hierarchical drafting process:

Writing Strategy (from nature-polishing)

Domain	Rule
Hourglass structure	Introduction narrows from broad context to specific gap; Discussion widens from specific findings back to broader implications
Writing order	Write in this order: Results → Methods → Introduction → Discussion → Abstract → Title — not in reading order
Sentence length	Every sentence ≤ 30 words; sentences > 20 words should be checked for multiple propositions; the last sentence of a paragraph is often the weakest
Paragraph control	One controlling idea per paragraph; use thematic linking between paragraphs
Section tense	Results = past-tense reporting verbs + quantitative detail; Discussion = hedging interpretation verbs + mechanism
Hedging calibration	Match claim strength to evidence: demonstrate → suggest → may reflect
Overclaim detection	Flag absolutes ("prove", "conclusively", "unprecedented", "best", "first"), unwarranted causation, and scope expansion
Citation integrity	Cite only sources personally verified; four attribution types: support, borrow, contrast, reuse
British English	signalling, colour, analyse, programme, modelling, behaviour

Section Responsibilities

Each section must answer specific questions before the draft moves on. Full move orders and phrase families are in scripts/section_phrasebank.py:

Section	Must answer
Introduction	What is the problem? Why does it matter? What has been tried? What is the gap? What is our approach?
Results	What did we find? How does it compare? What is the magnitude and direction of effects?
Discussion	What do the results mean? How do they relate to prior work? What are the limitations? What is next?
Conclusion	What is the single take-away? Why does it matter for the field?
Abstract	Problem → Gap → Approach → Key result → Impact (≤ 250 words)

Figure Standards (from nature-figure)

When generating or describing figures for the manuscript, use the helpers in scripts/nature_figure_style.py and templates in scripts/figure_templates.py:

• Multi-panel figures follow a three-level information hierarchy: overview → deviation → relationship; no two panels may answer the same scientific question.
• Primary output is always .svg; .png at 300 dpi is a secondary raster preview.
• Figure legends ≤ 300 words; titles ≤ 75 characters.
• Four figure archetypes: quantitative grid, schematic-led composite, image plate + quant, asymmetric mixed-modality.
• Anti-redundancy: every panel must encode distinct information; no visual duplication across panels.

Data Availability (from nature-data)

When writing the Data Availability statement, use the templates in scripts/data_statement_templates.py:

• Map every result-supporting dataset to a durable access route (public repo → discipline-specific repo → generalist repo → supplementary → request-based).
• State restriction reason, controller, review route, and access conditions for restricted data.
• Cite public datasets with DataCite-style metadata: creator, title, repository, year, identifier.
• Flag weak patterns such as "Data are available upon request" without further detail.

Important Notes & Limitations

• The skill never generates the full paper in a single step.
• Every intermediate step must be saved as a separate numbered Markdown file (01_.md through 10_.md) to guarantee step-by-step execution in OpenClaw.
• If LaTeX format is chosen, the skill will ask the user to provide a custom template file. If none is provided, the default IEEE template is used automatically.
• If EXPERIMENT.md contains sufficient quantitative data, tables are automatically generated and inserted into Results/Discussion sections (Markdown tables or LaTeX tabular environment).
• If any source file (IDEA.md, METHOD.md, EXPERIMENT.md) is missing, the skill stops and prompts the user to provide the missing content.
• Final output is always saved as paper_draft.md (plain text) or paper_draft.tex (LaTeX) in the workspace root.
• All intermediate files remain in the workspace for inspection, editing, or resumption.

When to Call This Skill

• After completing research-idea, method-design, and experiment-controller phases
• When the user wants a polished manuscript draft from the three core MD files
• Before final submission to a journal or arXiv

Complementary / Related Skills

• research-idea → continuously updates IDEA.md
• method-design → continuously updates METHOD.md
• experiment-controller → continuously updates EXPERIMENT.md
• dependency-planner → installs LaTeX dependencies when LaTeX output is selected
• claw-shell → used internally for LaTeX compilation and file operations
• overleaf-skill → optional sync of final draft to Overleaf

External Skill Libraries (nature-skills)

• nature-polishing → academic prose polishing, sentence/paragraph rules, hedging calibration, overclaim detection
• nature-figure → publication-ready figure workflow, chart archetypes, design theory
• nature-data → Data Availability statements, repository strategy, FAIR metadata
• nature-paper2ppt → paper-to-PPTX conversion for journal club presentations

Reference

Hierarchical Manuscript Drafting and Iterative Refinement (section 4.4 of NeuroClaw architecture).
Flowchart: multi-agent section generation → ethics review → iterative optimization → narrative writing → de-AI polishing → logic/continuity/length/reference checks → LaTeX repair → final draft.

Integrated writing standards from nature-skills by Yuan Yizhe (MIT License):

• Writing strategy, sentence rules, section responsibilities, and phrase bank (nature-polishing)
• Figure archetypes, design theory, and chart standards (nature-figure)
• Data Availability statement patterns and FAIR checklist (nature-data)

Created At: 2026-03-23 00:00 HKT Last Updated At: 2026-03-26 00:27 HKT
Author: chengwang96