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Paper Review Pipeline

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cnfjlhj
paper-review-pipeline

Use when a mostly complete ML conference paper needs self-review, pre-submission QA, camera-ready checking, section-by-section critique, citation-risk inspection, or rebuttal/review-response drafting. Skip this for initial drafting and use `paperreview` only when the user explicitly wants external submission.

Overview

Publishercnfjlhj
Repositoryai-collab-playbook
Skill namepaper-review-pipeline
Stars
455
Forks
44
Bundled files
14
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.

  • 14 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cnfjlhj on GitHub. Read the source before you install it.

Installation

Install the Paper Review Pipeline 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.
    https://github.com/cnfjlhj/ai-collab-playbook/tree/main/skills/full/paper-review-pipeline
  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/cnfjlhj/ai-collab-playbook.git /tmp/ai-collab-playbook
mkdir -p .claude/skills
cp -r /tmp/ai-collab-playbook/skills/full/paper-review-pipeline .claude/skills/paper-review-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Review Pipeline 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 Review Pipeline 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 Review Pipeline 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 Review Pipeline (ML Top Conferences)

Run a two-view paper review for ML conference submissions:

  1. Section-by-section review (Abstract → Intro → Method → Experiments → …) with concrete edits.
  2. Prioritized issue list with P0/P1/P2 severity, grouped by category, including recommended fixes and verification notes.

This skill also supports rebuttal / review response: parse reviewer comments, classify, choose a strategy, and draft a professional point-by-point response.

Parity Guarantee (No-Omission)

This skill is a consolidation layer. It must not omit any distinctive workflow, constraints, or output formats from the legacy skills it replaces.

Use:

  • references/parity-matrix.md as the feature-parity contract and regression scenarios.
  • references/modules/ for full imported workflows and checklists.

Execution Modes

This skill supports two modes:

  • Default: targeted — run only the most relevant tracks based on the user request and inputs, but always produce the Final Synthesis.
  • Optional: full-parallel — run all tracks as independent outputs (acceptable redundancy), then produce the Final Synthesis.

Trigger full-parallel when the user says: “全量”, “并行”, “run all tracks”, “run every skill”, “逐个 skill 测试”, “full pipeline”.

Protocol + required report template:

  • references/full-parallel-protocol.md
  • references/report-template.md

Optional External Second Opinion (paperreview)

If the user provides a near-final or final PDF, complete the local review first and then explicitly ask whether they also want an external second opinion via paperreview.

  • Ask only when the input is clearly a near-final or final PDF.
  • Do not auto-submit to external services. Get explicit confirmation first.
  • If the user agrees, hand off to paperreview as a follow-up step; otherwise finish with the local pipeline result only.

When to Use

  • Pre-submission quality check (ICML/ICLR/NeurIPS/AAAI).
  • After a draft is “mostly done” but clarity/logic is shaky.
  • When you suspect citation problems (missing, inconsistent, unverified, or claim-to-citation mismatch).
  • Before sending to advisor/collaborators for feedback (to reduce “obvious issues”).
  • After receiving reviews: draft rebuttal and a revision plan.

When NOT to Use

  • If the user wants new research or new experiments invented: require the user to provide results/artifacts.
  • If the user asks for verbatim PDF-to-LaTeX copying or large-scale reformatting without source.
  • If the task is pure BibTeX generation from memory: do not do it; use verified metadata workflows.

Non-Negotiable Guardrails

  1. No hallucinated citations.
    • If a citation cannot be verified, mark it as [CITATION NEEDED] / placeholder and tell the user explicitly.
    • Do not fabricate authors/years/venues/DOIs.
  2. Do not change technical meaning.
    • When proposing edits, preserve claims and numbers unless the user provides corrected data.
  3. Preserve LaTeX semantics when editing source.
    • Do not break \\cite{}, \\ref{}, \\label{}, math environments, figures/tables, or bibliography hooks.
  4. Respect blind review constraints (if applicable).
    • Avoid identity-revealing self-citations, acknowledgments, or repo links unless the user confirms it is camera-ready.

Inputs (Ask for the Minimum Needed)

For pre-submission review

  • Paper source: LaTeX section text, or the relevant excerpts pasted in chat (preferred), and optionally the PDF for context.
  • Target venue + track (ICML/ICLR/NeurIPS/AAAI) and any required sections (e.g., limitations / broader impact / ethics).
  • One-sentence contribution (if the user has it). If not, infer and ask for confirmation.

For rebuttal / review response

  • Reviewer comments (verbatim text, ideally grouped by reviewer).
  • Rebuttal constraints: word/page limit, formatting (ICLR OpenReview vs PDF), and timeline.
  • What the user is willing to change: “clarify only” vs “add experiments” vs “major rewrite”.

Output Contract (Always Produce Both Views)

View A — Section-by-Section Review

For each section, output:

  • What works (1–3 bullets)
  • What’s missing / unclear (P0/P1/P2 tagged bullets)
  • Concrete fixes (rewrite suggestions or structural moves)

Use the checklists in:

  • references/section-review-checklist.md

View B — P0/P1/P2 Issue List (Prioritized)

Format each issue like:

  • Priority: P0 (blocking) / P1 (important) / P2 (nice-to-have)
  • Category: Narrative / Evidence / Experimental Design / Statistics / Reproducibility / Citations / Writing / Figures-Tables / Format
  • Where: section name + a short quote anchor (or LaTeX label if available)
  • Problem
  • Fix
  • Verification (if needed): what evidence/log/search is required before claiming it is correct

Use the taxonomy in:

  • references/p0-p2-taxonomy.md

Modules (Routing Rules)

Use these modules to preserve legacy feature parity:

  • Paper-level QA: references/modules/paper-self-review.md
  • Rebuttal / review response: references/modules/review-response.md and references/rebuttal-workflow.md
  • LaTeX + BibTeX toolbox: references/modules/academic-paper-helper.md
  • Claim-level citation audit: references/modules/citation-validator.md and references/citation-integrity.md
  • Anti-AI polish: references/modules/writing-anti-ai.md
  • LaTeX rhythm pass: references/modules/latex-rhythm-refiner.md
  • Literature discovery (extended): references/modules/claude-scholar-ml-paper-writing.md

Workflow (Default)

Pass 0 — Triage (5–10 minutes)

  1. Identify the one-sentence contribution and confirm it with the user.
  2. Extract the top 3–7 claims the paper relies on.
  3. For each claim, note the current support:
    • empirical result (table/figure)
    • theorem/proof
    • citation / prior work
    • ablation / analysis
  4. Flag immediate P0 risks (typical: missing baselines, unclear experimental protocol, unverified citations, paper “about X” but experiments test Y).

Pass 1 — Section Review (primary deliverable)

Review sections in order (and check alignment between them):

  1. Abstract
  2. Introduction (motivation → gap → contribution bullets)
  3. Related Work (positioning + not a bibliography dump)
  4. Method (reproducible description + design justification)
  5. Experiments / Results (fair baselines + full setup + statistical reporting)
  6. Analysis / Ablations (claim-driven, not exploratory noise)
  7. Limitations / Broader Impact / Ethics (venue-dependent)
  8. Conclusion (tight restatement + constraints + future work without overclaim)

Pass 2 — Consolidate into P0/P1/P2

Turn findings into an actionable issue list:

  • P0 first (blockers / likely desk-reject causes)
  • P1 next (acceptance probability movers)
  • P2 last (polish)

Pass 3 (Optional) — Revision Plan

If the user wants an execution plan, produce:

  • 3–8 tasks with measurable acceptance criteria
  • suggested order (dependency-aware)
  • what can be done in parallel (writing vs experiments vs citations)

Full-Parallel Workflow (Comprehensive)

When mode is full-parallel, do not collapse everything into one voice. Instead:

  1. Run all tracks (A–G) as independent “mini-reviewers” and keep each track output visible.
  2. Then produce the Final Synthesis:
    • View A: consolidated section-by-section review
    • View B: consolidated P0/P1/P2 issue list
    • minimal revision plan
    • conflicts & resolutions between tracks

Use:

  • references/full-parallel-protocol.md
  • references/report-template.md

Rebuttal / Review Response Module

When reviews arrive, do:

  1. Parse and classify each comment: Major / Minor / Clarification / Missing baseline / Missing experiment / Writing / Citation / Misunderstanding.
  2. Choose a strategy per item: Accept + change, Clarify, Defend, Add experiment, Defer (explain constraints).
  3. Draft point-by-point responses with:
    • gratitude + precise restatement
    • what changed (or why not)
    • where to find it (section/figure/table)
    • evidence-based tone (no overpromising)

Use:

  • references/rebuttal-workflow.md

Citation Integrity Module (Hard Requirement)

Before submission, ensure:

  • every non-trivial factual claim has a citation or empirical evidence
  • every citation key resolves in the bibliography
  • any newly added citations are verified (paper exists; BibTeX not fabricated)

If deep citation audit is requested, follow:

  • references/citation-integrity.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Paper Review Pipeline AI skill do?

Use when a mostly complete ML conference paper needs self-review, pre-submission QA, camera-ready checking, section-by-section critique, citation-risk inspection, or rebuttal/review-response drafting. Skip this for initial drafting and use `paperreview` only when the user explicitly wants external submission.

Why use Paper Review Pipeline on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfjlhj/ai-collab-playbook/tree/main/skills/full/paper-review-pipeline. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Paper Review Pipeline?

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 Review Pipeline?

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

Is the Paper Review Pipeline AI skill free?

It is published on GitHub by cnfjlhj. Check the repository for licensing terms. 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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