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TenureAITenureAI/PhD-Zero/.agents/skills/paper-writing/SKILL.md

paper-writing

Skill52 starsChanged 8 months agoMITView on GitHub

Write CS/AI papers with progressive disclosure. Prefer invoking via research-workflow. TRIGGER when: user explicitly asks to draft/write/revise a paper, paper section (abstract, intro, related work, method, experiments, conclusion), rebuttal, or LaTeX content. DO NOT TRIGGER when: user mentions papers but needs research/investigation (use deep-research), experiment planning (use research-plan), or experiment execution (use experiment-execution).

36 other projects use a version of this.

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Most recently taken up by

Used with

Projects with it also use ml-training-recipe, replication and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • getcompanion-aiSkill

    ml-training-recipe

    Find implementable ML training recipes from papers, datasets, docs, and code. Use when the user wants to fine-tune, train, reproduce, or choose a practical ML method, dataset, hyperparameter setup, or benchmark recipe.

    Also in 3 projects that use this

  • getcompanion-aiSkill

    replication

    Plan a replication of a paper, claim, or benchmark, and execute only after an explicit environment choice. Use when the user asks to replicate results, reproduce an experiment, verify a claim empirically, or build a replication package.

    Also in 3 projects that use this

  • brainbytes-devSkill

    alpha-research

    Also in 3 projects that use this

  • JaimeParkerSkill

    paper-code-audit

    Perform three-way cross-validation of a research paper by comparing the original paper text (via Zotero), official code implementation (via Git), and personal Obsidian notes. Use this skill whenever the user wants to "audit a paper", "verify paper claims against code", "cross-check notes with implementation", "triangulate paper + code + notes", "find discrepancies between paper and code", or mentions wanting a deep, structured comparison of a paper's claims against its implementation and their own notes. Also trigger when the user asks to "analyze this paper thoroughly" with code available, or when they want to systematically reconcile what the paper says, what the code does, and what their notes claim.

    Also in 3 projects that use this

  • JimLiuSkill

    pdf-explore

    Use this skill when the user has attached a PDF, paper, report, or other document and the answer needs content from more than one place in it: summarize the methods or any other section, compare sections, find where a topic is discussed, read a value or label off a figure or chart, or find/list/extract every instance of something across the whole document (datasets, benchmarks, citations, figures, table rows, accession numbers — including appendices). Skip it only for a single lookup of 1–4 pages quoted in your very next response — `read_file(pages=[...])` attaches pages as images that are dropped from context after one turn, so multi-section answers end up re-reading the same ranges repeatedly. Parses the PDF once in the Python kernel: `pdf_pages` (pages as persistent text), `pdf_outline` (TOC), `pdf_scan` (rank pages by relevance), `pdf_map`/`pdf_extract` (per-page summary / structured fields via parallel haiku calls). For PDF creation/manipulation, use reportlab/pypdf directly.

    Also in 4 projects that use this

  • Yusong-EnceladusSkill

    research-review

    Get a deep critical review of research from GPT via Codex MCP. Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental results.

    Also in 6 projects that use this

  • +1 more from getcompanion-ai

The file

We split this skill into the rules it gives an agent

9 rules. 0 are common, 7 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 26 edited versions we read keep these.

  • 7

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Do not load all writing guidance at once.

    line 20
  2. Distinctive:

    Use this skill for:

    line 26
  3. Distinctive:

    Do not use this skill for:

    line 33
  4. Very specific:

    if related-work style mining is needed, load references/arxiv-source-workflow.md

    line 47
  5. Very specific:

    use scripts/fetch_arxiv_source.py to inspect exemplar paper sources

    line 48
  6. Distinctive:

    write only what is supported by actual evidence, experiments, and citations

    line 49
  7. Distinctive:

    Write from claims backward:

    line 78
  8. Distinctive:

    Prefer synthesis over surface summary.

    line 83
  9. Distinctive:

    Keep novelty claims proportional to the real delta.

    line 85

“Common” is measured: kept by at least 6 in 10 of the 26 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Paper Writing

Mission

Provide a section-aware paper-writing workflow that is:

  1. evidence-first
  2. progressively disclosed
  3. LaTeX- and structure-friendly
  4. optimized for CS and AI papers

Do not load all writing guidance at once. Start here, then open only the reference file needed for the current section.

Activation Gate

Activate this skill only when the user explicitly asks for paper-writing output.

Use this skill for:

  1. drafting a paper or a named paper section
  2. revising existing paper prose
  3. writing rebuttal text
  4. turning existing claims, evidence, and results into paper-ready prose

Do not use this skill for:

  1. topic scoping
  2. literature investigation without a writing deliverable
  3. feasibility analysis
  4. experiment design
  5. experiment execution
  6. deciding whether a project is paper-worthy

Default Workflow

  1. identify paper type and draft stage
  2. load references/workflow.md
  3. load only the section file needed right now
  4. if related-work style mining is needed, load references/arxiv-source-workflow.md
  5. use scripts/fetch_arxiv_source.py to inspect exemplar paper sources
  6. write only what is supported by actual evidence, experiments, and citations

Loading Map

Open these references on demand:

  1. references/source-index.md
    • external sources and what each one informs
  2. references/workflow.md
    • overall writing order, collaboration rules, and section contracts
  3. references/abstract.md
    • abstract structure and compression rules
  4. references/introduction.md
    • problem framing, gap, contributions, and section logic
  5. references/related-work.md
    • literature grouping, synthesis, comparison writing, and citation density
  6. references/arxiv-source-workflow.md
    • how to download and inspect arXiv LaTeX sources
  7. references/method.md
    • method narrative, notation, pipeline order, and algorithm exposition
  8. references/architecture-figures.md
    • architecture drawing heuristics and code-based tooling
  9. references/experiments.md
    • evaluation logic, ablations, robustness, and error analysis
  10. references/tables-and-layout.md
  • result tables, layout, appendix splitting, and figure-table coordination

Writing Rules

  1. Write from claims backward:
    • claim
    • evidence
    • section role
    • prose
  2. Prefer synthesis over surface summary.
  3. One paragraph should usually do one job.
  4. Keep novelty claims proportional to the real delta.
  5. For figures and tables, prefer code-generated assets that can be regenerated.
  6. For related work, inspect representative arXiv source when organization matters.

Scripts

  1. scripts/fetch_arxiv_source.py
    • download and unpack an arXiv source bundle
    • list candidate main .tex files and section fragments
    • useful for Related Work, Method, and layout mining

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