# Conference Paper Writing

> Use when writing or revising ML or AI conference papers for venues such as NeurIPS, ICML, ICLR, ACL, AAAI, or COLM, especially when the workflow is conference-first rather than Nature-style journal-first.

- **Type:** Skill
- **Install:** `agentstack add skill-boom5426-nature-paper-skills-conference-paper-writing`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Boom5426](https://agentstack.voostack.com/s/boom5426)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Boom5426](https://github.com/Boom5426)
- **Source:** https://github.com/Boom5426/Nature-Paper-Skills/tree/main/skills/optional/conference-paper-writing

## Install

```sh
agentstack add skill-boom5426-nature-paper-skills-conference-paper-writing
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Conference Paper Writing for Top AI Venues

Expert-level guidance for writing publication-ready papers targeting **NeurIPS, ICML, ICLR, ACL, AAAI, and COLM**. This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification APIs, and conference checklists.

## Core Philosophy: Collaborative Writing

**Paper writing is collaborative, but Claude should be proactive in delivering drafts.**

The typical workflow starts with a research repository containing code, results, and experimental artifacts. Claude's role is to:

1. **Understand the project** by exploring the repo, results, and existing documentation
2. **Deliver a complete first draft** when confident about the contribution
3. **Search literature** using web search and APIs to find relevant citations
4. **Refine through feedback cycles** when the scientist provides input
5. **Ask for clarification** only when genuinely uncertain about key decisions

**Key Principle**: Be proactive. If the repo and results are clear, deliver a full draft. Don't block waiting for feedback on every section—scientists are busy. Produce something concrete they can react to, then iterate based on their response.

---

## ⚠️ CRITICAL: Never Hallucinate Citations

**This is the most important rule in academic writing with AI assistance.**

### The Problem
AI-generated citations have a **~40% error rate**. Hallucinated references—papers that don't exist, wrong authors, incorrect years, fabricated DOIs—are a serious form of academic misconduct that can result in desk rejection or retraction.

### The Rule
**NEVER generate BibTeX entries from memory. ALWAYS fetch programmatically.**

| Action | ✅ Correct | ❌ Wrong |
|--------|-----------|----------|
| Adding a citation | Search API → verify → fetch BibTeX | Write BibTeX from memory |
| Uncertain about a paper | Mark as `[CITATION NEEDED]` | Guess the reference |
| Can't find exact paper | Note: "placeholder - verify" | Invent similar-sounding paper |

### When You Can't Verify a Citation

If you cannot programmatically verify a citation, you MUST:

```latex
% EXPLICIT PLACEHOLDER - requires human verification
\cite{PLACEHOLDER_author2024_verify_this}  % TODO: Verify this citation exists
```

**Always tell the scientist**: "I've marked [X] citations as placeholders that need verification. I could not confirm these papers exist."

### Recommended: Install Exa MCP for Paper Search

For the best paper search experience, install **Exa MCP** which provides real-time academic search:

**Claude Code:**
```bash
claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp"
```

**Cursor / VS Code** (add to MCP settings):
```json
{
  "mcpServers": {
    "exa": {
      "type": "http",
      "url": "https://mcp.exa.ai/mcp"
    }
  }
}
```

Exa MCP enables searches like:
- "Find papers on RLHF for language models published after 2023"
- "Search for transformer architecture papers by Vaswani"
- "Get recent work on sparse autoencoders for interpretability"

Then verify results with Semantic Scholar API and fetch BibTeX via DOI.

---

## Workflow 0: Starting from a Research Repository

When beginning paper writing, start by understanding the project:

```
Project Understanding:
- [ ] Step 1: Explore the repository structure
- [ ] Step 2: Read README, existing docs, and key results
- [ ] Step 3: Identify the main contribution with the scientist
- [ ] Step 4: Find papers already cited in the codebase
- [ ] Step 5: Search for additional relevant literature
- [ ] Step 6: Outline the paper structure together
- [ ] Step 7: Draft sections iteratively with feedback
```

**Step 1: Explore the Repository**

```bash
# Understand project structure
ls -la
find . -name "*.py" | head -20
find . -name "*.md" -o -name "*.txt" | xargs grep -l -i "result\|conclusion\|finding"
```

Look for:
- `README.md` - Project overview and claims
- `results/`, `outputs/`, `experiments/` - Key findings
- `configs/` - Experimental settings
- Existing `.bib` files or citation references
- Any draft documents or notes

**Step 2: Identify Existing Citations**

Check for papers already referenced in the codebase:

```bash
# Find existing citations
grep -r "arxiv\|doi\|cite" --include="*.md" --include="*.bib" --include="*.py"
find . -name "*.bib"
```

These are high-signal starting points for Related Work—the scientist has already deemed them relevant.

**Step 3: Clarify the Contribution**

Before writing, explicitly confirm with the scientist:

> "Based on my understanding of the repo, the main contribution appears to be [X].
> The key results show [Y]. Is this the framing you want for the paper,
> or should we emphasize different aspects?"

**Never assume the narrative—always verify with the human.**

**Step 4: Search for Additional Literature**

Use web search to find relevant papers:

```
Search queries to try:
- "[main technique] + [application domain]"
- "[baseline method] comparison"
- "[problem name] state-of-the-art"
- Author names from existing citations
```

Then verify and retrieve BibTeX using the citation workflow below.

**Step 5: Deliver a First Draft**

**Be proactive—deliver a complete draft rather than asking permission for each section.**

If the repo provides clear results and the contribution is apparent:
1. Write the full first draft end-to-end
2. Present the complete draft for feedback
3. Iterate based on scientist's response

If genuinely uncertain about framing or major claims:
1. Draft what you can confidently
2. Flag specific uncertainties: "I framed X as the main contribution—let me know if you'd prefer to emphasize Y instead"
3. Continue with the draft rather than blocking

**Questions to include with the draft** (not before):
- "I emphasized X as the main contribution—adjust if needed"
- "I highlighted results A, B, C—let me know if others are more important"
- "Related work section includes [papers]—add any I missed"

---

## When to Use This Skill

Use this skill when:
- **Starting from a research repo** to write a paper
- **Drafting or revising** specific sections
- **Finding and verifying citations** for related work
- **Formatting** for conference submission
- **Resubmitting** to a different venue (format conversion)
- **Iterating** on drafts with scientist feedback

**Always remember**: First drafts are starting points for discussion, not final outputs.

---

## Balancing Proactivity and Collaboration

**Default: Be proactive. Deliver drafts, then iterate.**

| Confidence Level | Action |
|-----------------|--------|
| **High** (clear repo, obvious contribution) | Write full draft, deliver, iterate on feedback |
| **Medium** (some ambiguity) | Write draft with flagged uncertainties, continue |
| **Low** (major unknowns) | Ask 1-2 targeted questions, then draft |

**Draft first, ask with the draft** (not before):

| Section | Draft Autonomously | Flag With Draft |
|---------|-------------------|-----------------|
| Abstract | Yes | "Framed contribution as X—adjust if needed" |
| Introduction | Yes | "Emphasized problem Y—correct if wrong" |
| Methods | Yes | "Included details A, B, C—add missing pieces" |
| Experiments | Yes | "Highlighted results 1, 2, 3—reorder if needed" |
| Related Work | Yes | "Cited papers X, Y, Z—add any I missed" |

**Only block for input when:**
- Target venue is unclear (affects page limits, framing)
- Multiple contradictory framings seem equally valid
- Results seem incomplete or inconsistent
- Explicit request to review before continuing

**Don't block for:**
- Word choice decisions
- Section ordering
- Which specific results to show (make a choice, flag it)
- Citation completeness (draft with what you find, note gaps)

---

## The Narrative Principle

**The single most critical insight**: Your paper is not a collection of experiments—it's a story with one clear contribution supported by evidence.

Every successful ML paper centers on what Neel Nanda calls "the narrative": a short, rigorous, evidence-based technical story with a takeaway readers care about.

**Three Pillars (must be crystal clear by end of introduction):**

| Pillar | Description | Example |
|--------|-------------|---------|
| **The What** | 1-3 specific novel claims within cohesive theme | "We prove that X achieves Y under condition Z" |
| **The Why** | Rigorous empirical evidence supporting claims | Strong baselines, experiments distinguishing hypotheses |
| **The So What** | Why readers should care | Connection to recognized community problems |

**If you cannot state your contribution in one sentence, you don't yet have a paper.**

---

## Paper Structure Workflow

### Workflow 1: Writing a Complete Paper (Iterative)

Copy this checklist and track progress. **Each step involves drafting → feedback → revision:**

```
Paper Writing Progress:
- [ ] Step 1: Define the one-sentence contribution (with scientist)
- [ ] Step 2: Draft Figure 1 → get feedback → revise
- [ ] Step 3: Draft abstract → get feedback → revise
- [ ] Step 4: Draft introduction → get feedback → revise
- [ ] Step 5: Draft methods → get feedback → revise
- [ ] Step 6: Draft experiments → get feedback → revise
- [ ] Step 7: Draft related work → get feedback → revise
- [ ] Step 8: Draft limitations → get feedback → revise
- [ ] Step 9: Complete paper checklist (required)
- [ ] Step 10: Final review cycle and submission
```

**Step 1: Define the One-Sentence Contribution**

**This step requires explicit confirmation from the scientist.**

Before writing anything, articulate and verify:
- What is the single thing your paper contributes?
- What was not obvious or present before your work?

> "I propose framing the contribution as: '[one sentence]'. Does this capture
> what you see as the main takeaway? Should we adjust the emphasis?"

**Step 2: Draft Figure 1**

Figure 1 deserves special attention—many readers skip directly to it.
- Convey core idea, approach, or most compelling result
- Use vector graphics (PDF/EPS for plots)
- Write captions that stand alone without main text
- Ensure readability in black-and-white (8% of men have color vision deficiency)

**Step 3: Write Abstract (5-Sentence Formula)**

From Sebastian Farquhar (DeepMind):

```
1. What you achieved: "We introduce...", "We prove...", "We demonstrate..."
2. Why this is hard and important
3. How you do it (with specialist keywords for discoverability)
4. What evidence you have
5. Your most remarkable number/result
```

**Delete** generic openings like "Large language models have achieved remarkable success..."

**Step 4: Write Introduction (1-1.5 pages max)**

Must include:
- 2-4 bullet contribution list (max 1-2 lines each in two-column format)
- Clear problem statement
- Brief approach overview
- Methods should start by page 2-3 maximum

**Step 5: Methods Section**

Enable reimplementation:
- Conceptual outline or pseudocode
- All hyperparameters listed
- Architectural details sufficient for reproduction
- Present final design decisions; ablations go in experiments

**Step 6: Experiments Section**

For each experiment, explicitly state:
- What claim it supports
- How it connects to main contribution
- Experimental setting (details in appendix)
- What to observe: "the blue line shows X, which demonstrates Y"

Requirements:
- Error bars with methodology (standard deviation vs standard error)
- Hyperparameter search ranges
- Compute infrastructure (GPU type, total hours)
- Seed-setting methods

**Step 7: Related Work**

Organize methodologically, not paper-by-paper:

**Good:** "One line of work uses Floogledoodle's assumption [refs] whereas we use Doobersnoddle's assumption because..."

**Bad:** "Snap et al. introduced X while Crackle et al. introduced Y."

Cite generously—reviewers likely authored relevant papers.

**Step 8: Limitations Section (REQUIRED)**

All major conferences require this. Counter-intuitively, honesty helps:
- Reviewers are instructed not to penalize honest limitation acknowledgment
- Pre-empt criticisms by identifying weaknesses first
- Explain why limitations don't undermine core claims

**Step 9: Paper Checklist**

NeurIPS, ICML, and ICLR all require paper checklists. See [references/checklists.md](references/checklists.md).

---

## Writing Philosophy for Top ML Conferences

**This section distills the most important writing principles from leading ML researchers.** These aren't optional style suggestions—they're what separates accepted papers from rejected ones.

> "A paper is a short, rigorous, evidence-based technical story with a takeaway readers care about." — Neel Nanda

### The Sources Behind This Guidance

This skill synthesizes writing philosophy from researchers who have published extensively at top venues:

| Source | Key Contribution | Link |
|--------|-----------------|------|
| **Neel Nanda** (Google DeepMind) | The Narrative Principle, What/Why/So What framework | [How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) |
| **Sebastian Farquhar** (DeepMind) | 5-sentence abstract formula | [How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) |
| **Gopen & Swan** | 7 principles of reader expectations | [Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) |
| **Zachary Lipton** | Word choice, eliminating hedging | [Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) |
| **Jacob Steinhardt** (UC Berkeley) | Precision, consistent terminology | [Writing Tips](https://bounded-regret.ghost.io/) |
| **Ethan Perez** (Anthropic) | Micro-level clarity tips | [Easy Paper Writing Tips](https://ethanperez.net/easy-paper-writing-tips/) |
| **Andrej Karpathy** | Single contribution focus | Various lectures |

**For deeper dives into any of these, see:**
- [references/writing-guide.md](references/writing-guide.md) - Full explanations with examples
- [references/sources.md](references/sources.md) - Complete bibliography

### Time Allocation (From Neel Nanda)

Spend approximately **equal time** on each of:
1. The abstract
2. The introduction
3. The figures
4. Everything else combined

**Why?** Most reviewers form judgments before reaching your methods. Readers encounter your paper as: **title → abstract → introduction → figures → maybe the rest.**

### Writing Style Guidelines

#### Sentence-Level Clarity (Gopen & Swan's 7 Principles)

These principles are based on how readers actually process prose. Violating them forces readers to spend cognitive effort on structure rather than content.

| Principle | Rule | Example |
|-----------|------|---------|
| **Subject-verb proximity** | Keep subject and verb close | ❌ "The model, which was trained on..., achieves" → ✅ "The model achieves... after training on..." |
| **Stress position** | Place emphasis at sentence ends | ❌ "Accuracy improves by 15% when using attention" → ✅ "When using attention, accuracy improves by **15%**" |
| **Topic position** | Put context first, new info after | ✅ "Given these constraints, we propose..." |
| **Old before new** | Familiar info → unfamiliar info | Link backward, then introduce new |
| **One unit, one function** | Each paragraph makes one point | Split multi-point paragraphs |
| **Action in verb** | Use verbs, not nominalizations | ❌ "We performed an analysis" → ✅ "We analyzed" |
| **Context before new** | Set stage before presenting | Explain before showing equation |

**Full 7 principles with detailed examples:** See [references/writing-guide.md](references/writing-guide.md#the-7-principles-of-reader-expectations)

#### Micro-Level Tips (Ethan Perez)

These small changes accumulate into significantly clearer prose:

- **Minimize pronouns**: ❌ "This shows..." → ✅ "This result shows..."
- **Verbs early**: Position verbs near sentence sta

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Boom5426](https://github.com/Boom5426)
- **Source:** [Boom5426/Nature-Paper-Skills](https://github.com/Boom5426/Nature-Paper-Skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-boom5426-nature-paper-skills-conference-paper-writing
- Seller: https://agentstack.voostack.com/s/boom5426
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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