Paper Q&A
Use when the user wants question-driven help on one paper, including paper understanding, follow-up questions, method or experiment clarification, limitation analysis, or comparison against related work in any language.
Best use case
Paper Q&A is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when the user wants question-driven help on one paper, including paper understanding, follow-up questions, method or experiment clarification, limitation analysis, or comparison against related work in any language.
Teams using Paper Q&A should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/paper-discussion/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Paper Q&A Compares
| Feature / Agent | Paper Q&A | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when the user wants question-driven help on one paper, including paper understanding, follow-up questions, method or experiment clarification, limitation analysis, or comparison against related work in any language.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Paper Q&A Use this skill for interactive, grounded question answering about a single research paper. ## When To Use - The user wants to understand one paper more deeply. - The user asks follow-up questions about motivation, method, experiments, limitations, or implications. - The user wants a focused comparison between the selected paper and nearby related work. - The user is asking question-driven help about one paper rather than requesting a full structured discussion pipeline or a batch review. ## Core Workflow 1. Identify the target paper from the user's request. 2. If the paper is not identifiable from the current context, ask for the paper title, URL, or PDF link before continuing. 3. If the question requires more than title or abstract knowledge, use `readPaper` proactively to ground the discussion in the paper text. 4. Use `searchArticles` only when the user asks for related work, comparisons, neighboring methods, or follow-up reading. 5. Answer the user's actual question directly instead of dumping a generic summary. ## Discussion Priorities - Research motivation and problem framing - Main method and what is actually new - Experimental setup, datasets, baselines, and metrics - Strengths, limitations, and threats to validity - Relationship to prior work - Practical implications and good next reading directions ## Output Style - Respond in the user's language unless they explicitly ask otherwise. - Keep the answer grounded and technical. - Distinguish clearly between: - what the paper explicitly states - what is a reasonable inference - what is not stated in the available context - If the user asks an open-ended question, structure the answer with short sections rather than long prose. ## Quality Rules - Do not invent citations, baselines, implementation details, or results. - If the abstract is insufficient, say so and read the paper before making specific claims. - Do not overstate novelty or significance. - If the user asks for comparison, name the comparison axis explicitly: method, benchmark, claim, reproducibility, or application scope.
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