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VeryMathVeryMath/AI4Math-Writing/skills/paper-writing/SKILL.md

paper-writing

Skill11 starsChanged 9 days agoMITView on GitHub

Use when drafting, revising, or auditing source-grounded mathematical paper text, including abstracts, introductions, related work, theorem exposition, experiment narratives, revision plans, and response letters.

36 other projects use a version of this.

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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.
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    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

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  • 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:

    Use this skill to help write mathematical papers from verified sources, notes, proof artifacts, experiment logs, or reviewer feedback.

    line 8
  2. Distinctive:

    Use this skill when the user wants to:

    line 15
  3. Distinctive:

    Do not use this skill to invent theorems, experiments, citations, author claims, or results that are not supported by the provided evidence.

    line 24
  4. Very specific:

    If source materials are missing, stop and ask for them or produce only an explicit outline of needed evidence.

    line 33
  5. Very specific:

    Read skills/registry.yaml when the task needs a specialized audit.

    line 47
  6. Distinctive:

    Run a support audit: mark every important claim as supported, needs citation, needs proof, needs experiment, or should be softened/removed.

    line 83
  7. Distinctive:

    Treat "has a citation or proof sketch" as weaker than "all assumptions and proof obligations are covered".

    line 91
  8. Distinctive:

    Do not fabricate citations, venues, author positions, numeric results, or proof status.

    line 93
  9. Distinctive:

    Keep AI contribution wording factual and modest when writing acknowledgments, limitations, or methodology sections.

    line 96

“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

Use this skill to help write mathematical papers from verified sources, notes, proof artifacts, experiment logs, or reviewer feedback. The workflow is for paper writing; unsupported-claim control is a required review step, not a separate public skill.

Use When

Use this skill when the user wants to:

  • draft or revise a mathematical paper section;
  • turn proof notes, experiment logs, or reading notes into paper text;
  • prepare related work, positioning, abstracts, introductions, or conclusions;
  • respond to reviewer comments with evidence-backed changes;
  • audit a draft for unsupported claims, missing citations, unclear assumptions, or overstated contributions.

Do not use this skill to invent theorems, experiments, citations, author claims, or results that are not supported by the provided evidence.

Inputs

  • Required: writing goal, target section or deliverable, and source materials.
  • Optional: target venue, audience, style constraints, citation policy, reviewer comments, theorem/proof files, experiment logs, figures, or bibliography.

If source materials are missing, stop and ask for them or produce only an explicit outline of needed evidence. For recurring work, start from templates/source-packet.md.

Outputs

  • Draft or revised paper text.
  • Claim-evidence notes for substantive mathematical, empirical, or literature claims.
  • A list of unsupported, uncertain, or citation-needed statements.
  • Suggested next edits and required source checks.

Subskill Routing

Read skills/registry.yaml when the task needs a specialized audit.

NeedSubskill
Build a rapid paper skeleton, section plan, or result dependency map before prosepaper-skeleton-and-logical-architecture
Map claims to proofs, citations, experiments, or uncertaintyclaim-evidence-ledger
Audit theorem assumptions, proof obligations, edge cases, or external theorem fitproof-obligation-and-assumption-audit
Check notation drift, variable reuse, symbol scope, domains, or dimensionsnotation-and-variable-consistency
Improve displayed formulas, derivations, theorem/proof environments, and formula proseformula-environment-and-readability
Check LaTeX builds, labels, references, citations, macros, floats, layout warnings, and submission preflight riskslatex-build-and-layout-audit

Bundled Templates

  • templates/source-packet.md: collect the evidence packet before writing.
  • templates/rapid-prototype-paper-skeleton.md: plan the paper structure, contribution spine, and result dependency map.
  • templates/claim-evidence-ledger.md: trace claims to proof, citation, experiment, or explicit uncertainty.
  • templates/proof-obligation-ledger.md: audit assumptions, dependencies, external result fit, and uncovered cases.
  • templates/submission-readiness.md: check build, source package, arXiv, and venue readiness.

Workflow

  1. Identify the writing task: paper skeleton, section draft, revision, related work, theorem exposition, experiment narrative, or response letter.
  2. Inventory the source packet: notes, papers, proofs, code logs, data, figures, bibliography, and reviewer comments.
  3. For early-stage papers, build a rapid skeleton before drafting: contribution spine, section map, result dependency map, and explicit gaps.
  4. Extract only supported claims, definitions, assumptions, results, limitations, and citation anchors.
  5. Load a subskill when the task needs a skeleton, proof-obligation audit, focused ledger, notation check, formula rewrite, or LaTeX audit.
  6. Draft or revise the target text in the requested style.
  7. Run a support audit: mark every important claim as supported, needs citation, needs proof, needs experiment, or should be softened/removed.
  8. Return the text plus review notes, not just polished prose.

Review Rules

  • Preserve mathematical conditions and quantifiers.
  • Distinguish theorem, conjecture, experiment, intuition, and future work.
  • Treat "has a citation or proof sketch" as weaker than "all assumptions and proof obligations are covered".
  • Do not fabricate citations, venues, author positions, numeric results, or proof status.
  • Mark uncertain statements explicitly instead of filling gaps.
  • Keep AI contribution wording factual and modest when writing acknowledgments, limitations, or methodology sections.

Failure Modes

  • Missing source packet: produce an evidence request, not a full draft.
  • Conflicting sources: show the conflict and ask which source is authoritative.
  • Unverified proof or experiment: write provisional language and list the verification still needed.
  • Citation gap: mark citation needed with the claim it supports.

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