skills/conference-writing-adapter/SKILL.md
Adapt ML/AI paper writing to a target venue. Use for venue style, structure, positioning, reviewer-friendly prose, and section or paragraph guidance.
npx skillsauth add a-green-hand-jack/ml-research-skills conference-writing-adapterInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Adapt an existing research paper to the writing conventions, reviewer expectations, and paper archetypes of a target ML/AI conference. The goal is to make the paper easier for the right reviewers to understand, trust, and champion without inventing unsupported claims or new experimental results.
Use this skill for:
Do not use this skill as a substitute for running experiments, proving claims, or checking final submission compliance. Pair it with submit-paper for final readiness checks and with experiment skills when the paper has evidence gaps. Use figure-results-review when the main issue is whether a figure/table's claim support, style, caption, or visual encoding is paper-ready.
Pair this skill with research-project-memory when rewriting changes paper claims, section structure, figure/table roles, writing risks, or experiment actions.
<installed-skill-dir>/
├── SKILL.md
└── references/
├── conference-profiles.md
├── exemplar-analysis.md
├── memory-model.md
├── paper-archetypes.md
├── quality-gates.md
└── paragraph-protocol.md
references/paper-archetypes.md and references/paragraph-protocol.md.references/conference-profiles.md when the user names a target conference or asks to compare venues.references/exemplar-analysis.md when the task involves learning from accepted, oral, spotlight, best-paper, or otherwise strong target-venue papers.references/quality-gates.md before finalizing a rewrite plan or rewritten section.references/memory-model.md whenever learning from accepted papers, saving venue knowledge, or reusing prior knowledge.Identify:
If the user only says a venue name and provides a draft, default to:
Prefer primary draft files and notes over memory.
Look for:
main.tex, paper.tex, sections/*.tex, *.bib, README.md, docs/, notes/Extract the paper into this working summary:
## Paper Snapshot
- Current title:
- Target venue:
- Claimed contribution:
- Core technical idea:
- Primary evidence:
- Strongest result:
- Most likely reviewer concern:
- Missing or weak evidence:
- Current structure:
If the draft is too incomplete to rewrite, produce a structural plan and a list of missing evidence instead of pretending the paper is ready.
Read references/conference-profiles.md, then refine it with current evidence.
When learning from multiple exemplar papers, read references/exemplar-analysis.md and produce a compact style matrix before recommending a rewrite strategy.
Sources to prefer:
When using OpenReview:
Extract patterns from exemplars:
Do not copy phrasing from exemplar papers. Distill patterns.
Read references/paper-archetypes.md and classify the user's draft.
Use one primary archetype and optional secondary archetypes:
method: new algorithm, architecture, objective, training recipe, inference procedure, or theoretical methodempirical-study: systematic finding about models, data, scaling, evaluation, or failure modesbenchmark-dataset: new dataset, task, benchmark, protocol, or evaluation suitetheory: theorem-led paper with formal assumptions, guarantees, or impossibility resultssystems: infrastructure, serving, training, tool, compiler, data pipeline, or large-scale engineering contributionanalysis: interpretability, diagnostic, causal, or mechanistic analysisapplication: strong domain result where novelty comes from ML adaptation plus evidence in a demanding settingThen decide whether the current venue favors this archetype and what style variant fits best.
Write a narrative diagnosis before rewriting:
## Venue Fit Diagnosis
- Target venue:
- Paper archetype:
- Best-fit style:
- Reviewer promise:
- Main tension:
- What must be obvious by the end of page 1:
- What must be proven by experiments/theory:
- Main figure/table role and visual style:
- What should move to appendix:
- Writing risks:
The "reviewer promise" is the sentence a reviewer should be able to say after reading the introduction:
This paper matters because [problem], and it deserves acceptance because [specific contribution] is supported by [specific evidence].
For a standard ML conference paper, plan:
For each section, specify:
If official page limits are relevant, verify current rules and state the source.
Read references/paragraph-protocol.md.
For every important paragraph, output:
### [Section] P[N]
- Function:
- Reader question answered:
- Claim:
- Evidence or support:
- Opening move:
- Closing move:
- Keep:
- Cut or move:
- Risk if weak:
Use this level of detail especially for the abstract, introduction, method overview, main experiment setup, and result interpretation.
When rewriting, preserve traceability:
[INSERT MAIN RESULT: metric, baseline, dataset].For LaTeX projects, edit the smallest relevant file. Do not reorganize the entire source tree unless the user asks.
Read references/quality-gates.md before finalizing a plan or rewritten section.
At minimum, check:
If a gate fails, revise once before returning the result. If it still fails because evidence is missing, report the blocker instead of smoothing it over.
Read references/memory-model.md.
When the task involved studying target-venue exemplars or the user explicitly wants knowledge to persist:
.agent/conference-writing/.agent/conference-writing/venues/<venue>.md.agent/conference-writing/exemplars/<venue>-<year>.md.agent/conference-writing/project-style.mdMemory entries must separate:
Do not store copyrighted paper text beyond short titles, bibliographic metadata, and brief paraphrased notes.
If the project uses research-project-memory, also update:
memory/claim-board.md: revised claim wording, paper locations, and unsupported claims to cut or weakenmemory/evidence-board.md: figure/table roles and evidence items cited by rewritten sectionsmemory/risk-board.md: writing, positioning, venue-fit, and evidence-gap risksmemory/action-board.md: missing experiments, citation checks, section rewrites, or figure updates exposed by writingpaper/.agent/paper-status.md: section map, paragraph-level decisions, and stale figures/tablespaper/.agent/visual-style.md or .agent/conference-writing/project-style.md: venue-facing figure style decisions, including palette, symbol, typography, and table conventionsDo not strengthen claims in memory beyond the evidence available in the paper.
# Conference Writing Diagnosis
## Paper Snapshot
## Venue Fit Diagnosis
## Archetype Classification
## Main Reviewer Risks
## Recommended Rewrite Strategy
# [Venue] Rewrite Plan
## Paper Snapshot
## Venue Pattern Summary
## Narrative Strategy
## Section-Level Plan
## Paragraph-Level Blueprint
## Evidence Gaps
## Appendix / Page-Budget Plan
## Memory Updates
# Rewritten [Section Name]
## Before/After Intent
## Draft Text
## Remaining Placeholders
## Why This Fits [Venue]
Before finalizing:
testing
Bootstrap project-local ml-research-skills. Use from global installs when creating a new ML research project, enabling this collection in an existing ML research repo, or deciding whether to install the full bundle locally. Route to project-init for new projects; do not handle paper or experiment work directly.
development
Route project operations tasks — git, memory, bootstrap, remote, workspace, code review, timeline, ops — to the correct skill. Use when the task involves commits, pushes, worktrees, project memory, enabling project-local skills, SSH/server coordination, sidecar runners, or audits. Do not solve the ops task directly.
testing
Route ML/AI paper writing tasks to the correct skill — contract planning, prose drafting, section writing, consistency editing, review simulation, rebuttal, submission, or citation work. Use when the task involves writing, revising, reviewing, or submitting a paper instead of guessing between paper-writing-assistant, paper-writing-contract-planner, paper-reviewer-simulator, auto-paper-improvement-loop, or citation skills. Do not draft prose directly.
data-ai
Project-local router for ML research skill selection. Use inside an initialized ML research project, or while maintaining this skill repo, when the user describes an ML research/paper/experiment/discovery/ops/release workflow and may not know the skill; route to a domain router or high-signal leaf. Do not use for generic non-ML projects.