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academic-writing-cs

Comprehensive toolkit for writing high-quality computer science research papers (conference, journal, thesis). Provides narrative construction guidance, sentence-level clarity principles (Gopen & Swan), academic phrasebank, CS-specific conventions, and section-by-section quality checklists. Use when assisting with academic paper writing, revision, or structure planning across all stages from drafting to submission.

$ 安裝

git clone https://github.com/SipengXie2024/Helios-Writing /tmp/Helios-Writing && cp -r /tmp/Helios-Writing/academic-writing-cs ~/.claude/skills/Helios-Writing

// tip: Run this command in your terminal to install the skill

SKILL.md
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name: academic-writing-cs description: Comprehensive toolkit for writing high-quality computer science research papers (conference, journal, thesis). Provides narrative construction guidance, sentence-level clarity principles (Gopen & Swan), academic phrasebank, CS-specific conventions, and section-by-section quality checklists. Use when assisting with academic paper writing, revision, or structure planning across all stages from drafting to submission.

Academic Writing for Computer Science

Overview

This skill provides end-to-end support for writing high-quality computer science research papers. It focuses on constructing clear, compelling technical narratives while adhering to field-specific conventions.

Core Philosophy:

  • Academic papers are narrative arcs (Problem → Solution → Evidence → Implications), not template fill-ins
  • Clarity comes from structure: place familiar information first, new information last
  • Every design choice must be justified; every claim must be supported

Scope:

  • Conference papers (6-12 pages, competitive venues)
  • Journal articles (15-30 pages, comprehensive)
  • Thesis chapters (flexible length, deep coverage)
  • All CS subfields: AI/ML, Systems, Theory, HCI, Security, etc.

When to Use This Skill

Invoke this skill when:

  • Planning paper structure and narrative flow
  • Drafting any section (Abstract, Introduction, Methods, Results, Discussion, Conclusion)
  • Revising for clarity, coherence, or compliance with venue requirements
  • Reviewing sentence-level writing for clarity issues
  • Seeking CS-specific conventions (notation, figures, citations)
  • Checking completeness with section-by-section quality checklists
  • Responding to reviewer comments

Workflow Decision Tree

Stage 1: Planning and Structure

When starting a new paper or major revision:

  1. Define the Narrative Arc

    • What problem does this solve, and why does it matter? (1-2 sentences)
    • What is the single main contribution? (1 sentence)
    • What are the 3 key results that support the contribution?
    • What are the main limitations?

    Reference: references/narrative_framework.md — Read the "Core Principle" and "Section-Level Narrative Structure" sections to understand how to structure the paper's story.

  2. Identify Target Venue and Constraints

    • Conference or journal?
    • Page limits, formatting requirements, anonymization rules?
    • Subfield conventions (ML vs. Systems vs. Theory)?

    Reference: references/cs_conventions.md (Section 8: Venue-Specific Guidelines, Section 5: Subfield-Specific Conventions)

  3. Outline Section-by-Section

    • For each major section, define:
      • What is the purpose of this section?
      • What are the 2-3 key points to convey?
      • What figures/tables will support this?

    Tool: Use assets/section_checklists.md (Quick Pre-Draft Planning Checklist) to ensure all key questions are answered before writing begins.

Stage 2: Drafting

For each section, follow this process:

Abstract

  1. Use the 4-sentence structure: Context → Gap → Contribution → Impact
  2. Check against assets/section_checklists.md (Abstract Checklist)
  3. Ensure it's self-contained and within word limit (150-250 words)

Common mistakes:

  • Vague contribution: "We improve X" → Be specific: "We achieve 15% higher accuracy"
  • No concrete results: Always include numbers/metrics

Introduction

  1. Follow the funnel structure: Broad → Narrow → Specific

    • Para 1: Problem domain and importance
    • Para 2-3: Specific problem, motivation, why existing work falls short
    • Para 4: Gap statement ("However, existing approaches lack...")
    • Para 5: Contribution overview (what this paper provides)
    • Para 6: Results summary (2-3 concrete findings)
    • Para 7: Paper organization (optional)

  2. Key requirement: By the end of paragraph 4-5, the reader must clearly understand the contribution.

  3. Include at least one figure (architecture or key result) for ML/systems papers.

  4. Check against assets/section_checklists.md (Introduction Checklist)

Reference: references/narrative_framework.md (Introduction section) for detailed guidance and examples.

Related Work

  1. Organize thematically (not chronologically): Group into 3-5 categories

  2. For each category:

    • Describe the general approach
    • Cite 3-5 representative works with 1-sentence descriptions
    • Point out limitations relevant to your contribution

  3. End with positioning paragraph: "In contrast to [X], our approach..."

    • Clearly articulate differences and advantages

  4. Check against assets/section_checklists.md (Related Work Checklist)

Common mistakes:

  • Laundry list of citations without synthesis
  • Failing to position your work relative to prior work
  • Being dismissive (respect prior work while differentiating)

Methodology

  1. Dual objectives:

    • Reproducibility: Enough detail for reimplementation
    • Intuition: Explain why the approach works

  2. Structure varies by paper type:

    • ML/AI papers: Problem Formulation → Overview + Figure → Detailed Design → Implementation → Complexity
    • Systems papers: Architecture Overview → Component Design → Key Mechanisms → Implementation
    • Theory papers: Formal Definitions → Main Results (theorems) → Proof Sketch

  3. Always include:

    • Clear notation (define all symbols on first use)
    • High-level overview before diving into details
    • Justification for design choices (or defer to Ablations)

  4. Check against assets/section_checklists.md (Methodology Checklist)

Reference: references/narrative_framework.md (Methodology section) and references/cs_conventions.md (Section 1: Notation and Mathematical Writing)

Experiments/Results

  1. Experimental Setup (subsection):

    • Datasets: Size, splits, preprocessing
    • Baselines: What you compare against (with citations)
    • Metrics: What you measure and why
    • Hardware/Software: Infrastructure and versions
    • Hyperparameters: How selected

  2. Main Results (subsection):

    • Table/figure showing primary comparison
    • Text: "Table 1 shows that our method outperforms..."
    • Highlight key findings with concrete numbers
    • Report statistical significance (confidence intervals, p-values, or std dev)

  3. Ablation Studies (subsection, critical):

    • Demonstrate necessity of each component
    • Table: effect of removing/modifying components

  4. Analysis (subsection):

    • Where does the method excel? Where does it fail?
    • Qualitative analysis, error analysis, failure cases

  5. Computational Cost (if relevant):

    • Training time, inference time, memory usage
    • Comparison with baselines

  6. Check against assets/section_checklists.md (Experiments/Results Checklist)

Reference: references/narrative_framework.md (Experiments/Results section)

Discussion

  1. Summarize findings (1 para): Restate key results

  2. Interpret results (1-2 paras): Why does the method work? What insights?

  3. Acknowledge limitations (0.5-1 para): Be honest about scope and failure cases

  4. Broader implications (0.5-1 para): Impact on the field, applications, future directions

  5. Check against assets/section_checklists.md (Discussion Checklist)

Tone: Balanced—confident but not overselling. Limitations increase credibility.

Conclusion

  1. Restate contribution (1 para): Recap problem, solution, key findings

  2. Broader impact (0.5 para): Significance and applications

  3. Future work (0.5 para): Open questions and extensions

    • Phrase as opportunities: "An interesting direction is..." (not "In future work, we will...")

  4. Check against assets/section_checklists.md (Conclusion Checklist)

Do NOT: Introduce new ideas, copy-paste Abstract, or be vague.

Stage 3: Revision for Clarity

After drafting, apply sentence-level clarity principles:

The Three Golden Rules (Gopen & Swan)

  1. Old Before New: Start sentences with familiar information; end with new information

    • This creates coherent flow where each sentence builds on what came before

  2. Subject-Verb Proximity: Keep the verb close to the subject

    • Long gaps between subject and verb strain comprehension

  3. Stress Position Power: Place the most important information at sentence end

    • Readers remember and emphasize what comes at the end

Apply these rules systematically:

  • For each paragraph, check that sentences flow (old-to-new)
  • For each sentence, check that:
    • Topic position (start) contains familiar info
    • Stress position (end) contains important new info
    • Verb appears soon after subject

Reference: references/sentence_clarity.md — Read this in full for detailed principles, examples, and common anti-patterns.

Practical Checklist:

  • Familiar information at sentence start (topic position)
  • Important new information at sentence end (stress position)
  • Verb close to subject
  • Active voice (unless passive is intentionally better)
  • Parallel structures for parallel ideas

Common anti-patterns to fix:

  • "Buried Verb" Syndrome: Converting verbs to nouns (nominalization)
    • ❌ "The comparison of the methods is shown..."
    • ✅ "Table 1 compares the methods..."
  • "Throat-Clearing": Weak starts like "It is important to note that..."
    • ❌ "It is important to note that our method improves accuracy."
    • ✅ "Our method improves accuracy."
  • "Dangling Emphasis": Ending sentences with weak elements
    • ❌ "This approach significantly improves performance, as shown in [23]."
    • ✅ "As shown in [23], this approach significantly improves performance."

Stage 4: Polishing and Compliance

Language and Phrasing

When writing or revising specific academic functions, consult references/phrasebank.md:

  • Introducing work: Establishing territory, identifying gaps, stating contributions
  • Referring to sources: Integral vs. non-integral citations
  • Describing methods: Sequential actions, conditional logic, implementation details
  • Reporting results: Presenting findings, comparing baselines, interpreting
  • Discussing findings: Explaining success, acknowledging limitations, stating implications
  • Writing conclusions: Summarizing, broader impact, future work

General language functions:

  • Being cautious (hedging): "may", "appears to", "likely"
  • Being critical: Identifying weaknesses, questioning validity
  • Compare and contrast: Similarity, difference
  • Describing trends: Increasing, decreasing, stability
  • Explaining causality: Causes, effects, conditions

Usage: Adapt templates to your context; don't copy verbatim. Vary expressions to maintain natural flow.

CS-Specific Conventions

Ensure compliance with field norms:

  1. Notation:

    • Define all symbols on first use
    • Use consistent conventions (bold for vectors, italic for scalars, etc.)
    • Integrate equations into sentences with punctuation

  2. Figures and Tables:

    • Reference all figures/tables in text before they appear
    • Self-contained captions
    • High-resolution, readable fonts (≥8pt)
    • Colorblind-friendly palettes

  3. Citations:

    • Follow venue citation style (author-year or numbered)
    • Cite all prior work you build on or compare against
    • Accurate and complete bibliography

  4. Code and Reproducibility:

    • State code availability
    • Provide sufficient implementation details
    • Report hyperparameters, random seeds, number of runs

  5. Subfield-Specific Variations:

    • ML/AI: Emphasis on ablations, statistical significance, computational cost
    • Systems: Architecture diagrams, throughput/latency, scalability
    • Theory: Formal definitions, theorems, proofs, complexity bounds
    • HCI: User studies, qualitative feedback, interface screenshots
    • Security: Threat models, attack scenarios, defense mechanisms

Reference: references/cs_conventions.md — Comprehensive guide covering notation, figures, citations, code, subfield norms, and venue requirements.

Quality Assurance

Before submission, use assets/section_checklists.md:

  1. Section-by-Section Review:

    • Run through each section's checklist
    • Ensure all required elements are present
    • Check for common pitfalls

  2. Pre-Submission Checklist:

    • Content completeness (all sections, figures, citations)
    • Formatting (venue template, page limits, margins)
    • Anonymization (if double-blind)
    • Reproducibility (sufficient detail, code availability)
    • Final quality checks (spell-check, grammar, co-author review)

  3. Emergency Checklist (if deadline is imminent):

    • Prioritize: Abstract, Introduction contribution statement, Main results table, At least one ablation, Readable figures, Correct bibliography

Stage 5: Responding to Reviews

After receiving reviewer feedback:

  1. Analyze comments systematically:

    • Categorize: Major issues (experiments, clarity, claims) vs. Minor issues (typos, formatting)
    • Prioritize: Address major issues first

  2. Plan revisions:

    • List all changes to be made
    • If experiments are requested, plan them carefully
    • If clarifications are needed, identify which sections to revise

  3. Revise and respond:

    • Address every comment (in rebuttal or revision)
    • Use respectful, professional tone
    • Clearly mark changes (if required by venue)

  4. Check revised version:

    • Ensure all changes are integrated
    • Re-run relevant checklists from assets/section_checklists.md (Revision Checklist)
    • Verify still within page limits

Reference: assets/section_checklists.md (Revision Checklist)

Key Resources Summary

Narrative and Structure

  • references/narrative_framework.md: Core paper structure (Abstract, Introduction, Related Work, Methods, Results, Discussion, Conclusion). Use for understanding the narrative arc and section-specific guidance.

Sentence-Level Clarity

  • references/sentence_clarity.md: Gopen & Swan principles (topic position, stress position, old-to-new flow). Use for revising individual sentences and paragraphs for maximum clarity.

Academic Phrases

  • references/phrasebank.md: Templates for common academic writing functions (introducing work, citing sources, reporting results, discussing findings). Use when drafting or seeking variation in phrasing.

CS Conventions

  • references/cs_conventions.md: Field-specific norms (notation, figures, citations, code, subfield variations, venue requirements). Use for ensuring compliance with CS writing standards.

Quality Checklists

  • assets/section_checklists.md: Comprehensive checklists for every section, plus pre-submission, revision, and emergency checklists. Use for planning, reviewing, and final quality assurance.

Example Workflows

Workflow 1: Starting from Scratch

User: "I need to write a conference paper on my new semi-supervised learning method."

Process:

  1. Planning (Stage 1):

    • Define narrative arc: Problem (labeled data is expensive) → Solution (our semi-supervised method) → Evidence (experiments on 3 datasets) → Implications (reduces labeling cost)
    • Read references/narrative_framework.md (Core Principle)
    • Use assets/section_checklists.md (Quick Pre-Draft Planning Checklist)

  2. Drafting (Stage 2):

    • Abstract: 4-sentence structure (Context: deep learning needs data; Gap: labeling is expensive; Contribution: our method STCR; Impact: 82% accuracy with 10% labels)
    • Introduction: Funnel (broad: DL success → narrow: labeling cost → gap: existing semi-supervised methods lack X → contribution: STCR leverages consistency → results: 7% improvement)
    • Check each section against assets/section_checklists.md

  3. Revision (Stage 3):

    • Apply references/sentence_clarity.md principles to every paragraph
    • Ensure old-to-new flow, stress position usage

  4. Polishing (Stage 4):

    • Use references/phrasebank.md for varied phrasing
    • Ensure compliance with references/cs_conventions.md (ML/AI conventions)
    • Run Pre-Submission Checklist from assets/section_checklists.md

Workflow 2: Revising for Clarity

User: "My introduction is confusing. Reviewers said they couldn't understand the contribution."

Process:

  1. Diagnose issue:

    • Check against assets/section_checklists.md (Introduction Checklist)
    • Is the contribution stated clearly by paragraph 4-5?
    • Is the funnel structure followed (broad → narrow)?

  2. Restructure if needed:

    • Read references/narrative_framework.md (Introduction section)
    • Ensure: Opening → Background → Gap → Contribution → Results → Organization
    • Explicitly state: "In this paper, we present [X], which addresses [Y] by [Z]."

  3. Revise at sentence level:

    • Apply references/sentence_clarity.md principles
    • Check that each sentence flows from the previous one (old-to-new)
    • End key sentences with the important information (stress position)

Workflow 3: Drafting the Results Section

User: "How should I present my experimental results?"

Process:

  1. Structure:

    • Read references/narrative_framework.md (Experiments/Results section)
    • Follow: Setup → Main Results → Ablations → Analysis → Cost

  2. Create tables/figures:

    • Main results table: Methods (rows) vs. Metrics (columns)
    • Bold best results; include standard deviations
    • Check references/cs_conventions.md (Figures and Tables section)

  3. Write accompanying text:

    • "Table 1 shows that our method achieves X, outperforming the strongest baseline by Y%."
    • Use references/phrasebank.md (Section 4: Reporting Results) for phrasing

  4. Quality check:

    • Run through assets/section_checklists.md (Experiments/Results Checklist)
    • Ensure: Statistical significance, Ablations present, Analysis included

Workflow 4: Ensuring CS Compliance

User: "Is my notation and citation style correct for ICML?"

Process:

  1. Check venue requirements:

    • Read references/cs_conventions.md (Section 8: Venue-Specific Guidelines)
    • ICML uses numbered citations [1], double-blind review, LaTeX template

  2. Notation:

    • Read references/cs_conventions.md (Section 1: Notation and Mathematical Writing)
    • Ensure: Vectors are bold, scalars are italic, all symbols defined

  3. Citations:

    • Read references/cs_conventions.md (Section 3: Citations and References)
    • Use numbered format: "Method X [1] achieves..."
    • Anonymize self-citations for double-blind

  4. Final check:

    • assets/section_checklists.md (Pre-Submission Checklist → Compliance section)

Common Pitfalls and How to Avoid Them

Pitfall 1: Vague Contributions

Problem: "We improve performance on X." Solution: Be specific. "We achieve 15% higher accuracy than the strongest baseline on ImageNet."

Pitfall 2: Missing Ablations

Problem: Claiming design choices are important without evidence. Solution: Include ablation studies. Remove each component and measure the performance drop.

Pitfall 3: Poor Information Flow

Problem: Sentences feel disjointed; readers get lost. Solution: Apply old-to-new flow. Each sentence should start with information from the previous sentence. Reference: references/sentence_clarity.md

Pitfall 4: Weak Stress Position

Problem: Sentences end with citations or minor details. Example: ❌ "This approach significantly improves performance, as shown in [23]." Solution: ✅ "As shown in [23], this approach significantly improves performance."

Pitfall 5: Ignoring Limitations

Problem: Overselling without acknowledging scope or failure cases. Solution: Dedicate a paragraph in Discussion to honest limitations. This increases credibility.

Pitfall 6: Inconsistent Notation

Problem: Using x for input in one section, X in another. Solution: Define all notation upfront. Create a notation table (appendix) if needed. Reference: references/cs_conventions.md (Section 1)

Tips for Efficient Writing

  1. Draft quickly, revise thoroughly:

    • Don't aim for perfection in the first draft
    • Get ideas down, then refine structure and clarity

  2. Write sections out of order:

    • Start with Methods and Results (most concrete)
    • Then Introduction and Related Work
    • Finally Abstract and Conclusion

  3. Use figures early:

    • Create key figures (architecture, main results) before writing
    • Figures clarify your thinking and guide the narrative

  4. Get feedback early:

    • Share drafts with co-authors and colleagues
    • Mock reviews identify issues before submission

  5. Iterate on structure:

    • If a section feels wrong, revisit the narrative arc
    • Ensure every section advances Problem → Solution → Evidence → Implications

  6. Use the checklists proactively:

    • Before drafting a section, read the checklist to know what to include
    • After drafting, use the checklist to verify completeness

Advanced: Handling Special Cases

Writing for Top-Tier Venues

  • Higher bar for novelty and rigor: Ensure the contribution is significant, not incremental
  • Strong baselines: Compare against state-of-the-art, not just simple methods
  • Comprehensive evaluation: Multiple datasets, extensive ablations, sensitivity analyses
  • Polished presentation: High-quality figures, clear writing, consistent notation

Writing Rebuttals

  • Address all concerns: Even if you disagree, engage respectfully
  • Provide evidence: If reviewers doubt a claim, provide additional results or citations
  • Be concise: Rebuttals have strict length limits; prioritize major issues
  • Highlight changes: "We added an experiment (Table 3) showing..."

Writing Thesis Chapters

  • More comprehensive: Deeper background, extended related work, lessons learned
  • Narrative continuity: Ensure chapters connect (e.g., Chapter 3 builds on Chapter 2)
  • Broader scope: Can include negative results and explorations that didn't pan out
  • Use assets/section_checklists.md (Long-Form Paper Checklist)

Summary: The Golden Workflow

  1. Plan the narrative: Problem → Solution → Evidence → Implications
  2. Draft section-by-section: Use structure guidelines from references/narrative_framework.md
  3. Revise for clarity: Apply principles from references/sentence_clarity.md
  4. Polish and comply: Use references/phrasebank.md and references/cs_conventions.md
  5. Quality check: Run through assets/section_checklists.md

Remember:

  • Papers are stories, not templates
  • Clarity comes from structure (old-to-new, topic/stress positions)
  • Every claim needs evidence; every design choice needs justification
  • Honest limitations increase credibility

When in doubt, ask:

  • "Does this advance the narrative arc?"
  • "Can a reader reproduce this?"
  • "Is this claim supported?"
  • "Is this the simplest, clearest way to express this?"

Getting Started

For a new paper:

  1. Read references/narrative_framework.md (Core Principle)
  2. Use assets/section_checklists.md (Quick Pre-Draft Planning Checklist)
  3. Outline your paper's narrative arc in 4 sentences (Problem, Solution, Evidence, Implications)
  4. Draft section-by-section, checking checklists as you go

For revising an existing draft:

  1. Identify the issue (structure, clarity, compliance)
  2. Consult the relevant reference file
  3. Apply fixes systematically
  4. Re-check with the appropriate checklist

For sentence-level issues:

  1. Read references/sentence_clarity.md (Three Golden Rules)
  2. Apply to each problematic paragraph
  3. Check: Old-to-new flow, stress position usage, subject-verb proximity

Ready to write? Let's build a clear, compelling paper together.

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