paper-review-pipeline Skill
description: 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.
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Why use this skill
paper-review-pipeline is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around productivity skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Claude Code Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in cnfjlhj/ai-collab-playbook, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
--- name: paper-review-pipeline description: 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. --- # 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”, ...