| type | skill |
| lifecycle | stable |
| inheritance | inheritable |
| name | academic-paper-drafting |
| description | End-to-end academic paper drafting for CHI, HBR, journals, and conferences with venue-specific templates, drafting workflows, and revision strategies. |
| tier | standard |
| applyTo | **/*academic*,**/*paper*,**/*drafting*,**/*journal*,**/*conference* |
| currency | "2026-05-02T00:00:00.000Z" |
| lastReviewed | "2026-05-02T00:00:00.000Z" |
Academic Paper Drafting Skill
Turn research into published scholarship. From blank page to accepted manuscript.
This skill provides structured workflows for drafting academic papers, with venue-specific guidance for HCI conferences (CHI), business publications (HBR), and academic journals (Cognitive Systems Research, Minds & Machines).
Drafting Philosophy
The Academic Writing Paradox
| Challenge | Reality | Strategy |
|---|
| "I need to read more first" | Reading is procrastination | Write to discover what you don't know |
| "I need the perfect first sentence" | First drafts are meant to be bad | Start with the section you know best |
| "I'll write when I have time" | Time expands to fill available space | Write in focused sprints |
| "It needs to be original" | Synthesis is originality | Combine existing ideas in new ways |
The Write-to-Think Method
Messy Draft → Clarity → Structure → Polish → Submit
↑ ↓
└────── Reviewer Feedback ───────────┘
Key insight: Writing IS thinking. You don't figure out your argument first and then write it — you figure it out BY writing it.
Venue Quick Reference
User's Target Pipeline
| Venue | Type | Word Limit | Review Time | Focus |
|---|
| ACM CHI | Conference | 7,500 (full) / 3,000 (LBW) | 3-4 months | HCI, interaction design |
| Harvard Business Review | Magazine | 2,500-3,000 | 2-4 weeks | Business practice, executives |
| Cognitive Systems Research | Journal | 8,000-12,000 | 3-6 months | Cognitive science, AI systems |
| Minds & Machines | Journal | 8,000-12,000 | 3-6 months | Philosophy of AI, consciousness |
Venue Selection Matrix
| Your Research Has... | Best Venue |
|---|
| User study with metrics | CHI |
| Business implications, case study | HBR |
| Cognitive architecture, theory | Cognitive Systems Research |
| Philosophical argument about AI | Minds & Machines |
| Quick preliminary findings | CHI LBW or Workshop |
ACM CHI Papers
CHI Paper Types
| Type | Length | Purpose | Acceptance Rate |
|---|
| Full Paper | 7,500 words | Complete research contribution | ~25% |
| Late-Breaking Work (LBW) | 3,000 words | Preliminary findings | ~40% |
| Workshop Paper | 2,000-4,000 words | Community discussion | Varies |
| Case Study | 7,500 words | In-depth design exploration | ~25% |
| Alt.CHI | 7,500 words | Provocative/unconventional | ~30% |
CHI Full Paper Template
# Title: Catchy but Accurate (10-12 words max)
## Abstract (150 words)
[One sentence: Problem/gap]
[One sentence: Approach/method]
[One sentence: Key findings (quantified)]
[One sentence: Contribution category]
[One sentence: Implications]
## Author Keywords
keyword1; keyword2; keyword3; keyword4
## CCS Concepts
• Human-centered computing → Human computer interaction (HCI)
---
## 1. Introduction (~800 words)
### 1.1 Opening Hook
[Compelling opening that establishes stakes]
### 1.2 Problem Statement
[Clear articulation of the gap/challenge]
### 1.3 Research Questions
RQ1: [Question]
RQ2: [Question]
### 1.4 Contributions
We contribute:
1. [Empirical contribution: findings from study]
2. [Artifact contribution: system/tool/design]
3. [Methodological contribution: new approach] (if applicable)
### 1.5 Paper Structure
Section 2 reviews... Section 3 describes...
---
## 2. Related Work (~1,200 words)
### 2.1 [Theme 1]
[Position your work relative to prior art]
### 2.2 [Theme 2]
[Identify gap your work fills]
### 2.3 Summary and Gap
[Explicit gap statement leading to your research]
---
## 3. System/Method/Design (~1,500 words)
### 3.1 Design Rationale
[Why did you build/design it this way?]
### 3.2 Implementation
[Technical details sufficient for replication]
### 3.3 [Component Description]
[Architecture, features, etc.]
---
## 4. User Study (~1,500 words)
### 4.1 Participants
[N=X, demographics, recruitment, compensation]
### 4.2 Procedure
[Step-by-step protocol]
### 4.3 Measures
[What you measured and how]
### 4.4 Analysis
[Qualitative: coding approach. Quantitative: statistical tests]
---
## 5. Findings (~1,500 words)
### 5.1 [Finding 1]
"P5 noted that..." [Quote + interpretation]
### 5.2 [Finding 2]
[Quantitative: "Participants completed task significantly faster (M=X, SD=Y), t(df)=Z, p<.001"]
### 5.3 [Finding 3]
[Theme with supporting evidence]
---
## 6. Discussion (~1,000 words)
### 6.1 Implications for Design
[What should designers do differently?]
### 6.2 Implications for Research
[What research directions does this open?]
### 6.3 Limitations
[Honest assessment: sample, method, scope]
### 6.4 Future Work
[Concrete next steps]
---
## 7. Conclusion (~200 words)
[Synthesis of contributions and significance]
---
## Acknowledgments
[Funding, participants, collaborators]
## References
[ACM format, recent work emphasized]
CHI Contribution Types
CHI values explicit contribution statements. Choose your type:
| Type | Description | Evidence Needed |
|---|
| Empirical | New knowledge about people/technology | User study, data |
| Artifact | Novel system, tool, or interaction | Implementation, evaluation |
| Methodological | New way to study/design | Comparison to existing methods |
| Theoretical | New framework or model | Grounding, application |
| Dataset | New resource for community | Description, access, ethics |
| Survey | Comprehensive literature synthesis | Systematic review |
| Opinion/Essay | Perspective on field direction | Argument, evidence |
CHI Writing Tips
| Do | Don't |
|---|
| Use participant quotes (P1, P2...) | Generalize without evidence |
| State contribution type explicitly | Assume readers will infer |
| Include representative figures | Over-rely on text |
| Acknowledge limitations early | Hide weaknesses |
| Cite recent CHI papers | Ignore venue norms |
Harvard Business Review (HBR)
HBR Article Types
| Type | Length | Purpose |
|---|
| Feature Article | 3,000-4,000 words | In-depth analysis |
| Spotlight | 2,500-3,000 words | Focused insight |
| Big Idea | 2,000-2,500 words | Provocative argument |
| Case Study | 2,500-3,000 words | Company narrative |
| Web Article | 800-1,200 words | Quick insight |
HBR Template
# Title: Action-Oriented, Benefit-Focused
## Subtitle: One Sentence Elaboration
### The Hook (100 words)
[Surprising statistic, provocative question, or vivid anecdote]
[Why executives should keep reading]
### The Problem (300 words)
[What challenge are leaders facing?]
[Why is it getting worse or more urgent?]
[What's at stake?]
### The Insight (500 words)
[Your key finding or framework]
[What did you discover that changes the game?]
[Name your concept/framework if introducing one]
### The Evidence (800 words)
[Case study 1: Company that did this well]
[Case study 2: Contrasting example]
[Data that supports your argument]
[Quote from executive or expert]
### The Framework/How-To (600 words)
[Step 1: What to do first]
[Step 2: Next action]
[Step 3: How to sustain]
[Pitfalls to avoid]
### The Conclusion (200 words)
[Synthesis of the opportunity]
[Call to action for leaders]
[The future if they act (or don't)]
---
**About the Author**
[2-3 sentence bio emphasizing relevant expertise]
HBR Writing Style
| Academic Style | HBR Style |
|---|
| "The study found that..." | "When we surveyed 300 executives..." |
| "Participants reported..." | "One CEO told us..." |
| "Hypothesis 1 was supported" | "The data confirms what many leaders suspect:" |
| "Implications include..." | "Here's what this means for your organization:" |
| Passive voice | Active, direct voice |
| Citations in text | Minimal citations, conversational |
HBR Submission Tips
- Pitch first — HBR prefers pitches before full drafts
- Lead with "What's new" — Why now? What's changed?
- Name your framework — Memorable concepts spread (e.g., "The Innovator's Dilemma")
- Include real companies — Anonymized is OK, but real examples work better
- Write for the airport — Busy executive on a flight should get value
Cognitive Systems Research
CSR Paper Types
| Type | Focus | Length |
|---|
| Original Article | Novel research findings | 8,000-12,000 words |
| Review Article | Comprehensive field synthesis | 10,000-15,000 words |
| Short Communication | Preliminary findings | 3,000-5,000 words |
| Commentary | Response to published work | 2,000-4,000 words |
CSR Template (the AI assistant Architecture Paper)
# Title: Declarative but Specific
## Abstract (200-250 words)
**Background.** [Problem context and gap in current systems]
**Objective.** [What this paper presents]
**Method.** [Architecture approach, implementation duration]
**Results.** [Key metrics, qualitative findings]
**Contributions.** [Named concepts introduced]
**Significance.** [Why this matters for cognitive systems]
## Keywords
cognitive architecture; persistent memory; human-AI interaction; [specific terms]
---
## 1. Introduction
### 1.1 The Memory Problem in AI Assistants
[Motivating problem: stateless AI, lost context]
### 1.2 Research Questions
RQ1: How can persistent memory improve AI assistance?
RQ2: What architectural patterns support knowledge retention?
RQ3: [Specific question]
### 1.3 Approach Overview
[Brief description of the architecture]
### 1.4 Contributions
1. [Architectural contribution]
2. [Empirical contribution from deployment]
3. [Framework/taxonomy contribution]
---
## 2. Theoretical Background
### 2.1 Cognitive Architectures
[ACT-R, SOAR, Global Workspace Theory]
### 2.2 Memory Systems in Cognition
[Declarative, procedural, episodic, semantic]
### 2.3 AI Memory Approaches
[RAG, vector databases, context windows]
### 2.4 Gap: Biologically-Inspired Persistent Memory
[What's missing from current approaches]
---
## 3. Architecture Design
### 3.1 Design Principles
[Biologically-grounded, modular, scalable]
### 3.2 Memory Types
#### 3.2.1 Procedural Memory (.instructions.md)
[How-to knowledge, automatic activation]
#### 3.2.2 Declarative Memory (SKILL.md)
[Domain knowledge, explicit retrieval]
#### 3.2.3 Episodic Memory (.prompt.md)
[Session records, temporal context]
### 3.3 Synaptic Connections
[Connection types, strengths, activation patterns]
### 3.4 Implementation
[Technical stack, file formats, integration]
---
## 4. Longitudinal Deployment
### 4.1 Deployment Context
[18+ months, 62+ projects, single user intensive use]
### 4.2 Metrics Collection
[Connection count, skill usage, memory file growth]
### 4.3 Qualitative Observations
[Emergent behaviors, user experience notes]
---
## 5. Results
### 5.1 Memory Growth Patterns
[Quantitative: 945+ connections, 47+ memory files]
### 5.2 Cross-Project Knowledge Transfer
[Evidence of knowledge reuse]
### 5.3 Emergent Properties
[Unexpected capabilities]
---
## 6. Discussion
### 6.1 Contributions to Cognitive Systems
[Theory extension, practical framework]
### 6.2 Comparison to Prior Architectures
[How this differs from ACT-R, SOAR, etc.]
### 6.3 Limitations
[Single user, specific platform, no controlled study]
### 6.4 Future Research
[Multi-user, formal evaluation, consciousness implications]
---
## 7. Conclusion
[Synthesis of contribution and significance]
## Acknowledgments
## References (APA 7 or journal style)
Minds & Machines
Philosophy of AI Focus
Minds & Machines emphasizes philosophical arguments about:
- Consciousness and AI
- Ethics of artificial agents
- Epistemology of machine learning
- Philosophy of mind implications
M&M Template
# Title: Philosophical Claim + Context
## Abstract (200 words)
[Philosophical question addressed]
[Position taken]
[Argument structure preview]
[Implications for AI development/policy]
---
## 1. Introduction
### The Philosophical Problem
[Frame the question in philosophy of mind context]
### Why This Matters Now
[Connect to current AI capabilities]
### Thesis Statement
[Clear articulation of your position]
### Argument Structure
[Roadmap of philosophical moves]
---
## 2. Background: The Debate
### 2.1 [Position A in the literature]
### 2.2 [Position B in the literature]
### 2.3 [Why neither fully succeeds]
---
## 3. [Your Framework/Argument]
### 3.1 [First premise with support]
### 3.2 [Second premise with support]
### 3.3 [Conclusion from premises]
---
## 4. Objections and Replies
### 4.1 Objection 1: [Strongest counterargument]
**Reply:** [Your response]
### 4.2 Objection 2: [Another challenge]
**Reply:** [Your response]
---
## 5. Implications
### 5.1 For Philosophy of Mind
### 5.2 For AI Development
### 5.3 For Ethics/Policy
---
## 6. Conclusion
[Restate thesis, summarize argument, future questions]
## References
Drafting Workflow
The 5-Phase Drafting Process
Preparation → Rough Draft → Revision → Feedback → Submission
- Preparation (1-2 weeks): Literature review → Outline creation → Key arguments mapped
- Rough Draft: Write messy first draft → Focus on getting ideas down
- Revision: Structural revision → Paragraph-level clarity → Sentence-level polish
- Feedback: Peer review → Advisor review → Incorporate feedback
- Submission: Final formatting → Submission
Phase 1: Preparation (1-2 weeks)
| Step | Output |
|---|
| Literature immersion | Annotated bibliography |
| Gap identification | 2-3 sentence gap statement |
| Contribution clarity | Explicit contribution list |
| Outline creation | Section-by-section plan |
| Figure sketches | Hand-drawn or rough diagrams |
Phase 2: Rough Draft (1-2 weeks)
Rules for first draft:
- Don't edit while writing — Separate generation from editing
- Start with what you know — Often Methods or Results, not Intro
- Use placeholder brackets — "[CITE Smith here]", "[need better transition]"
- Aim for "shitty first draft" — Anne Lamott's term; embrace imperfection
Phase 3: Revision (1-2 weeks)
| Level | Focus | Questions to Ask |
|---|
| Structure | Overall argument | Does it flow logically? |
| Section | Each section's job | Does this section earn its place? |
| Paragraph | One idea per paragraph | What's the topic sentence? |
| Sentence | Clarity, precision | Can this be simpler? |
| Word | Precision, concision | Is this the right word? |
Phase 4: Feedback (2-4 weeks)
| Feedback Source | What to Ask |
|---|
| Advisor | Is the argument sound? Scope appropriate? |
| Peer in field | Is this interesting? What's missing? |
| Peer outside field | Is this clear? What's confusing? |
| Writing group | How's the prose? What drags? |
Phase 5: Polish & Submit
Citation Integration
Weaving Citations Naturally
| Pattern | Example | Use When |
|---|
| Narrative | "Smith (2024) argues that..." | Discussing specific work |
| Parenthetical | "...has been demonstrated (Smith, 2024)" | Supporting a general claim |
| Integrated | "Smith's (2024) framework suggests..." | Referencing framework/concept |
| Multiple | "...is well-established (Smith, 2024; Jones, 2023)" | Broad support |
How Many Citations?
| Section | Citation Density |
|---|
| Abstract | 0 (usually) |
| Introduction | Medium (establish context) |
| Related Work | High (comprehensive review) |
| Methods | Low-Medium (justify choices) |
| Results | Low (your data, not others') |
| Discussion | Medium (connect to literature) |
Handling Rejection
Reviewer Response Framework
| Reviewer Says | What It Means | Response Strategy |
|---|
| "Missing related work" | Gap in literature coverage | Add citations, explain positioning |
| "Claims not supported" | Evidence insufficient | Add data or soften claim |
| "Unclear methodology" | Can't assess validity | Expand method description |
| "Contribution unclear" | Buried or vague value | Make contribution explicit in intro |
| "Writing needs work" | Surface issues distract | Get editing help, revise prose |
| "Not right for venue" | Mismatch with audience | Try different venue, reframe |
Response Letter Template
# Response to Reviewers
Dear Editors and Reviewers,
Thank you for your thoughtful feedback on our manuscript "[Title]".
We have carefully addressed all comments and believe the paper
is significantly strengthened. Below we detail our responses.
Major changes include:
1. [Summary of major change 1]
2. [Summary of major change 2]
3. [Summary of major change 3]
---
## Response to Reviewer 1
### R1.1: "[Direct quote of concern]"
We appreciate this observation. [Brief explanation of how you addressed it].
Changes made:
- Section X, paragraph Y: [quoted new text or description of change]
- [Additional changes if needed]
### R1.2: "[Next concern]"
[Response pattern repeats]
---
## Response to Reviewer 2
[Same pattern]
---
We believe these revisions address all concerns raised. We are
grateful for the opportunity to improve our work.
Sincerely,
[Authors]
the AI assistant Assistance Commands
Draft Generation
| Command | Action |
|---|
| "Draft a CHI paper on [topic]" | Generate CHI template with content |
| "Help me write the related work for [topic]" | Literature synthesis assistance |
| "Structure my HBR pitch about [finding]" | HBR framing guidance |
| "Turn dissertation chapter into journal paper" | Restructure and condense |
Review Assistance
| Command | Action |
|---|
| "Review this abstract for CHI" | Venue-specific feedback |
| "Strengthen my contribution statement" | Clarify and sharpen |
| "Help me respond to reviewer concern: [quote]" | Response drafting |
| "Is my related work comprehensive for [topic]?" | Gap identification |
Related Skills
Falsifiability
- This skill is not earning its tokens if drafts produced following its guidance fail peer review on structural grounds (section ordering, citation format, abstract structure) the skill explicitly addresses
- Stale if citation style guidance conflicts with current APA/IEEE/Chicago style guides
- Wrong if the academic conventions it documents are field-specific but presented as universal
