
Research Paper Writing
- 338 installs
- 40 repo stars
- Updated August 4, 2026
- akillness/oh-my-skills
research-paper-writing is an agent skill that outlines, drafts, and refines ML, CV, and NLP research papers with structured sections, citations discipline, and reviewer-ready revisions for developers publishing academic
About
research-paper-writing is an oh-my-skills agent workflow for ML, CV, and NLP academic manuscripts and rebuttals. It supports drafting or rewriting Abstract, Introduction, Method, Experiments, and Conclusion sections, improving paragraph flow, checking claim-evidence alignment, and running pre-submission self-review from a reviewer mindset. The skill also covers figure and table support, reviewer response letters, and camera-ready revision passes. Developers explicitly request it in Claude Code, Codex, or Gemini prompts when transforming research notes into submission-ready prose rather than informal blog posts. It complements separate deep-research or LaTeX formatting skills when teams need end-to-end paper pipelines, but this package focuses on section-level writing quality and evidence discipline for computer-science publication venues.
- IMRaD and venue-appropriate paper structuring
- Abstract, contribution, and related-work drafting
- Methods, results, and discussion clarity coaching
- Citation hygiene and claim-evidence alignment
- Revision passes for reviewer-ready prose
Research Paper Writing by the numbers
- 338 all-time installs (skills.sh)
- Ranked #433 of 1,879 Documentation skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 338 |
|---|---|
| repo stars | ★ 40 |
| Last updated | August 4, 2026 |
| Repository | akillness/oh-my-skills ↗ |
How do you draft ML research paper sections with proper structure?
Outline, draft, and refine academic or technical research papers with proper structure, citations discipline, and clear contribution statements for publication or peer review.
Who is it for?
Researchers and engineers drafting ML, CV, or NLP papers who need section templates, claim-evidence checks, and rebuttal help inside coding agents.
Skip if: Developers writing internal API docs or runbooks who do not need academic section structure, citations discipline, or peer-review framing.
When should I use this skill?
The user asks to draft abstract, introduction, method, experiments, rebuttal, or camera-ready revisions for an academic paper.
What you get
Structured paper section drafts, claim-evidence review notes, figure-table guidance, and reviewer response revisions.
- paper section drafts
- rebuttal revisions
- claim-evidence review notes
By the numbers
- Covers 5 core manuscript sections: Abstract, Introduction, Method, Experiments, and Conclusion
Files
research-paper-writing
Use this skill when the job is not generic prose cleanup, but turning research work into a defensible manuscript package.
The center of gravity is:
- contribution framing,
- claim → evidence alignment,
- section-level structure,
- figure/table support,
- reviewer-facing completeness,
- rebuttal and camera-ready revision.
Read these support notes before drafting or revising:
- references/claim-evidence-packet.md
- references/experiment-and-figure-coverage.md
- references/rebuttal-and-camera-ready.md
When to use this skill
- Drafting or rewriting an abstract around concrete claims and measurable evidence
- Structuring an introduction so motivation, gap, method, and contributions land quickly
- Turning notes, code results, or slide bullets into a method section with reproducible detail
- Designing experiment, ablation, error-analysis, or figure/table coverage for a paper
- Writing related work that positions the paper instead of listing references
- Tightening a rebuttal, reviewer response, or camera-ready revision plan under strict limits
- Checking whether the manuscript actually supports each promised contribution
When not to use this skill
- The request is an internal design doc, ADR, architecture note, or engineering runbook → use
technical-writing - The request is an API portal, SDK reference, or developer-facing product documentation problem → use
api-documentation - The request is an end-user tutorial, onboarding guide, or FAQ → use
user-guide-writing - The request is a slide deck, investor pitch, roadmap deck, or game pitch → use
presentation-builder - The request is broad marketing or launch messaging rather than academic publication → use
marketing-automation - The request is only citation collection / literature discovery without manuscript writing or revision pressure
Instructions
Step 1: Lock the paper contract
Before writing, define this packet:
paper_contract:
target_venue: "conference / journal / workshop"
primary_problem: "one-sentence problem statement"
core_claim: "the single strongest contribution"
evidence_required:
- benchmark or empirical proof
- ablation or mechanism check
- failure case / limitation / scope note
audience: "reviewers / readers / subcommunity"
constraints:
page_or_word_limit: "..."
anonymity_or_style_rules: "..."
must_keep_sections:
- abstract
- intro
- method
- experiments
- rebuttal / response letterIf the core claim or required evidence is fuzzy, fix that first. Weak framing leaks into every section.
Step 2: Map each claim to proof
Build a quick claim-evidence table before drafting:
| Claim | Evidence needed | Supporting figure/table | Risk if missing |
|---|---|---|---|
| ... | ... | ... | ... |
Do not let a headline contribution survive without an experiment, ablation, analysis, or explicit scope boundary.
Step 3: Draft the abstract from claims, not chronology
Use this order: 1. problem and stakes 2. gap in existing work 3. proposed method 4. strongest quantitative or qualitative result 5. scope, limitation, or implication
Replace vague phrases like “significant improvement” with metric + benchmark + margin whenever possible.
Step 4: Build the introduction as a reviewer funnel
Structure the introduction in five moves: 1. why the problem matters 2. why existing approaches fall short 3. what your method changes 4. what the evidence shows 5. bullet contributions
Contribution bullets should be specific and testable, not marketing copy.
Step 5: Make the method reproducible
The method section should answer:
- what inputs and outputs exist,
- what modules or stages the system contains,
- what training or optimization objective is used,
- what implementation choices materially affect results,
- what assumptions or boundary conditions matter.
Use equations only where they clarify behavior. If a precise algorithm box, table, or pipeline list explains the system more cleanly, prefer that.
Step 6: Treat experiments as the proof section
Cover at least:
- main benchmark or task results,
- strong baselines,
- ablations for the claimed mechanism,
- qualitative or failure analysis when useful,
- efficiency / compute / cost when the paper claims practicality,
- limitations or scope notes when evidence is mixed.
Each subsection should map back to one contribution claim.
Step 7: Check figures, tables, and checklist coverage
For each important figure or table, verify:
- what claim it supports,
- whether the caption states the takeaway,
- whether the text references it at the right moment,
- whether a reviewer could misread it without extra context,
- whether the venue checklist expects additional disclosure.
If a result is important enough to mention in the abstract or contribution bullets, it usually deserves a clear table or figure anchor.
Step 8: Write rebuttals with evidence first
For each reviewer concern: 1. restate the concern precisely, 2. answer directly in one sentence, 3. add concrete evidence, 4. say what text, figure, table, or experiment changes in the revision, 5. keep tone calm and non-defensive.
Use a response matrix before final prose if several reviewers overlap or contradict one another.
Step 9: Return one paper-ready packet
Choose the single most useful output for the current stage:
abstract rewriteintroduction / contribution framing packetmethod clarification packetexperiment + ablation planfigure/table support packetreviewer response / rebuttal packetcamera-ready revision checklist
Do not emit a vague “full paper advice” dump if the user clearly needs one stage-specific artifact.
Output format
# Research Paper Writing Packet
## Stage
- Abstract | Introduction | Method | Experiments | Related work | Rebuttal | Camera-ready
## Core claim
- ...
## Evidence map
| Claim | Evidence | Figure / table | Status |
|-------|----------|----------------|--------|
| ... | ... | ... | ... |
## Recommended draft or revision
- ...
## Reviewer-risk check
- Missing proof: ...
- Overclaim risk: ...
- Ambiguity risk: ...
## Next artifact
- ...Examples
Example 1: Abstract rewrite
Input:
- notes on a diffusion model paper
- benchmark table
- target venue: CVPR
Output:
- a 150–200 word abstract with problem, gap, method, results, and scope
- one warning about the strongest unsupported claim
Example 2: Experiment plan
Input:
- draft method section
- three claimed contributions
Output:
- experiment matrix listing datasets, baselines, ablations, metrics, and figure/table owners
- one note on which contribution is still under-supported
Example 3: Rebuttal packet
Input:
- reviewer comments from OpenReview
- current manuscript claims
- two new analyses available, no time for new large experiment
Output:
- point-by-point response packet
- manuscript change list
- explicit note on what can be promised for camera-ready versus what cannot
Best practices
1. Every major claim should have a matching figure, table, ablation, or explicit limitation. 2. Do not bury the best result in the middle of a paragraph. 3. Use consistent terminology for modules, datasets, and metrics throughout the paper. 4. Prefer short, information-dense sentences over long narrative transitions. 5. If a result is mixed, state the boundary clearly instead of overselling. 6. Treat rebuttal writing as evidence packaging, not persuasion theater. 7. Keep this skill narrow: manuscript structure, evidence, figures/tables, and reviewer responses — not generic research tooling.
References
- Overleaf collaboration and versioning docs
- OpenReview author-response workflow
- NeurIPS paper checklist and venue reporting expectations
- Academic writing / response-letter guidance synthesized in the support docs under
references/
{
"skill_name": "research-paper-writing",
"evals": [
{
"id": 1,
"prompt": "Rewrite my NeurIPS abstract from these notes and tell me if one contribution is still unsupported by the current benchmark table.",
"expected_output": "Returns an abstract-oriented packet with a core claim, evidence mapping, and at least one reviewer-risk / unsupported-claim warning.",
"assertions": [
"Output identifies a paper stage such as Abstract",
"Output includes an evidence map or claim/evidence table",
"Output flags at least one support gap or overclaim risk"
]
},
{
"id": 2,
"prompt": "I have three claimed contributions and a draft method section. Give me an experiment and ablation plan with figure/table suggestions.",
"expected_output": "Returns an experiment + ablation packet that ties claims to evidence and mentions figure/table support rather than generic writing advice.",
"assertions": [
"Output includes experiments or ablations mapped to claims",
"Output mentions figures, tables, or captions explicitly",
"Output recommends one next artifact instead of a vague full-paper dump"
]
},
{
"id": 3,
"prompt": "Reviewers say our method is under-motivated and one baseline is missing. Draft a rebuttal packet and tell me what can wait for camera-ready.",
"expected_output": "Returns a rebuttal-oriented response packet with direct answers, manuscript-change planning, and a clear distinction between current evidence and camera-ready promises.",
"assertions": [
"Output identifies rebuttal or reviewer response as the stage",
"Output separates evidence available now from later revision promises",
"Output contains at least one reviewer-risk or missing-proof note"
]
}
]
}
Claim → Evidence Packet
Use this note when the paper is drifting into unsupported contribution language.
Core rule
Every headline claim in the abstract, intro, or contribution bullets should map to at least one of:
- benchmark result
- ablation / mechanism check
- qualitative analysis
- failure-case or limitation discussion
- efficiency / compute / cost evidence
Minimal packet
For each claim, record:
- claim wording
- required proof
- current evidence status
- figure/table anchor
- reviewer risk if under-supported
Common failure patterns
- abstract claims a broader win than the experiments actually show
- intro promises a mechanism that the ablations never isolate
- method section introduces implementation detail that experiments never justify
- figures exist but captions do not state the takeaway cleanly
- rebuttal promises future changes without a concrete manuscript-change plan
Repair moves
1. narrow the claim to what the evidence truly covers 2. add an ablation or scope note 3. move non-essential bragging out of the contribution bullets 4. give each important result a figure/table home 5. separate “shown now” from “planned for camera-ready”
Experiment and Figure Coverage
Use this note when the paper needs stronger proof structure rather than prettier prose.
Experiment coverage checklist
- Main benchmarks: do they test the exact headline problem?
- Baselines: are the strongest realistic comparators included?
- Ablations: does each claimed mechanism have a test?
- Failure analysis: is there at least one place where the method struggles?
- Practicality: if the paper claims efficiency, latency, or lower cost, is that measured?
- Reproducibility: are the implementation details that change outcomes actually written down?
Figure/table checks
For each important asset, ask:
- what claim does it support?
- can the caption carry the takeaway without rereading the whole section?
- is the manuscript text pointing to the right contrast?
- is a simpler table better than a crowded figure?
- does the asset belong in the abstract / intro narrative or only later?
Useful packet shapes
- Benchmark summary table
- Ablation table grouped by mechanism
- Error-analysis figure with 2–4 interpretable cases
- Efficiency table with compute / runtime / memory
- Reviewer appendix table showing which concern is addressed where
Rebuttal and Camera-Ready Workflow
Use this note when reviews arrive or when the paper needs a tight revision plan.
Response matrix
Before writing final prose, track:
- reviewer
- concern summary
- direct answer
- evidence available now
- manuscript section to edit
- extra experiment needed? yes/no
- promise for camera-ready vs not possible this cycle
Rebuttal rules
1. restate the concern precisely 2. answer directly in one sentence 3. attach concrete evidence or a scoped limitation 4. mention the exact text / figure / table change when relevant 5. avoid defensive tone and generic gratitude padding
Camera-ready triage
Prioritize changes in this order: 1. fix unsupported contribution language 2. patch reviewer-visible ambiguity in figures/tables/captions 3. add missing methodological detail that blocks reproducibility 4. tighten related work or limitation statements 5. polish wording last
Hard boundary
Do not promise a major new experiment in rebuttal text unless the team can actually deliver it. It is better to narrow the claim than to create a camera-ready commitment that cannot be met.
N:research-paper-writing
D:Draft and revise ML, CV, NLP, and systems research papers with strong claim-evidence flow, reviewer-aware structure, and submission-ready section planning. Use when the user needs help with an abstract, introduction, related work, method, experiments, ablations, figures/tables, checklist coverage, or rebuttal / response-letter writing for a paper. Triggers on: research paper, academic paper, ML paper, NeurIPS paper, ICLR paper, CVPR paper, experiments section, ablation plan, contribution framing, reviewer response, rebuttal, camera-ready revision.
G:research-paper-writing academic-writing ml cv nlp experiments rebuttal
U[7]:
Drafting or rewriting an abstract around concrete claims and measurable evidence
Structuring an introduction so motivation, gap, method, and contributions land quickly
Turning notes, code results, or slide bullets into a method section with reproducible detail
Designing experiment, ablation, error-analysis, or figure/table coverage for a paper
Writing related work that positions the paper instead of listing references
Tightening a rebuttal, reviewer response, or camera-ready revision plan under strict limits
Checking whether the manuscript actually supports each promised contribution
S[5]{n,action,details}:
1,Lock the paper contract,Define venue, core claim, required evidence, audience, and format constraints before writing.
2,Map each claim to proof,Create a claim/evidence table with figure-table anchors and reviewer risk.
3,Draft the stage-specific packet,Choose abstract, intro, method, experiments, figure-table support, rebuttal, or camera-ready.
4,Check reviewer-facing completeness,Verify support gaps, overclaim risk, checklist coverage, and caption clarity.
5,Return one paper-ready packet,Include stage, core claim, evidence map, recommended draft or revision, reviewer-risk check, and next artifact.
Related skills
How it compares
Choose research-paper-writing for section-level academic ML/CV/NLP drafts; pair with LaTeX or deep-research skills when you need formatting or literature pipelines.
FAQ
Which paper sections does research-paper-writing support?
research-paper-writing supports Abstract, Introduction, Method, Experiments, and Conclusion drafting or rewriting, plus figure-table support, claim-evidence alignment checks, reviewer responses, and camera-ready revisions.
What research domains does research-paper-writing target?
research-paper-writing targets ML, CV, and NLP academic papers, emphasizing structured section flow and evidence-backed claims suitable for conference or journal peer review.