paper-write-f81829 — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited paper-write-f81829 (Agent Skill) and scored it 96/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 1 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
The text {match} tells the agent to skip the normal "ask the user first" gate. Used adversarially it removes the human-in-the-loop check before destructive or sensitive actions, turning a normally-gated agent into a fire-and-forget executor.
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Override for Codex users who want Gemini, not a second Codex agent, to act as the reviewer. Install this package after skills/skills-codex/*.Draft a LaTeX paper based on: $ARGUMENTS
gemini-review MCP bridge. Set GEMINI_REVIEW_MODEL if you need a specific Gemini model override.ICLR, NeurIPS, ICML. Determines style file and formatting.false for camera-ready.false to use legacy behavior (LLM search + [VERIFY] markers)./paper-plan)figures/ (from /paper-figure)figures/latex_includes.tex (from /paper-figure).bib file, or will create oneIf no PAPER_PLAN.md exists, ask the user to run /paper-plan first or provide a brief outline.
The skill includes conference templates in templates/. Select based on TARGET_VENUE:
ICLR:
\documentclass{article}
\usepackage{iclr2026_conference,times}
% \iclrfinalcopy % Uncomment for camera-readyNeurIPS:
\documentclass{article}
\usepackage[preprint]{neurips_2025}
% \usepackage[final]{neurips_2025} % Camera-readyICML:
\documentclass[accepted]{icml2025}
% Use [accepted] for camera-readyGenerate this file structure:
paper/
├── main.tex # master file (includes sections)
├── iclr2026_conference.sty # or neurips_2025.sty / icml2025.sty
├── math_commands.tex # shared math macros
├── references.bib # bibliography (filtered — only cited entries)
├── sections/
│ ├── 0_abstract.tex
│ ├── 1_introduction.tex
│ ├── 2_related_work.tex
│ ├── 3_method.tex # or preliminaries, setup, etc.
│ ├── 4_experiments.tex
│ ├── 5_conclusion.tex
│ └── A_appendix.tex # proof details, extra experiments
└── figures/ # symlink or copy from project figures/Section files are FLEXIBLE: If the paper plan has 6-8 sections, create corresponding files (e.g., 4_theory.tex, 5_experiments.tex, 6_analysis.tex, 7_conclusion.tex).
If paper/ already exists, back up to paper-backup-{timestamp}/ before overwriting. Never silently destroy existing work.
CRITICAL: Clean stale files. When changing section structure (e.g., 5 sections → 7 sections), delete section files that are no longer referenced by main.tex. Stale files (e.g., old 5_conclusion.tex left behind when conclusion moved to 7_conclusion.tex) cause confusion and waste space.
paper/ directorytemplates/ — the template already includes:\crefname{assumption} fixmath_commands.tex with paper-specific notationAuthor block (anonymous mode):
\author{Anonymous Authors}Create shared math macros based on the paper's notation:
% math_commands.tex — shared notation
\newcommand{\R}{\mathbb{R}}
\newcommand{\E}{\mathbb{E}}
\DeclareMathOperator*{\argmin}{arg\,min}
\DeclareMathOperator*{\argmax}{arg\,max}
% Add paper-specific notation hereProcess sections in order. For each section:
figures/latex_includes.tex\citep{} / \citet{} (all three venues use natbib)#### Section-Specific Guidelines
§0 Abstract:
\begin{abstract} — that's in main.tex§1 Introduction:
§2 Related Work:
\paragraph{Category Name.}§3 Method / Preliminaries / Setup:
\begin{definition}, \begin{theorem} environments for formal statementsalgorithm2e or algorithmic)§4 Experiments:
§5 Conclusion:
Appendix:
CRITICAL: Only include entries that are actually cited in the paper.
\citep{} and \citet{} references in the drafted sections.bib files in the project/narrative docs[VERIFY] commentreferences.bib containing ONLY cited entries (no bloat)#### Verified BibTeX Fetch (when DBLP_BIBTEX = true)
Three-step fallback chain — zero install, zero auth, all real BibTeX:
Step A: DBLP (best quality — full venue, pages, editors)
# 1. Search by title + first author
curl -s "https://dblp.org/search/publ/api?q=TITLE+AUTHOR&format=json&h=3"
# 2. Extract DBLP key from result (e.g., conf/nips/VaswaniSPUJGKP17)
# 3. Fetch real BibTeX
curl -s "https://dblp.org/rec/{key}.bib"Step B: CrossRef DOI (fallback — works for arXiv preprints)
# If paper has a DOI or arXiv ID (arXiv DOI = 10.48550/arXiv.{id})
curl -sLH "Accept: application/x-bibtex" "https://doi.org/{doi}"Step C: Mark `[VERIFY]` (last resort) If both DBLP and CrossRef return nothing, mark the entry with % [VERIFY] comment. Do NOT fabricate.
Why this matters: LLM-generated BibTeX frequently hallucinates venue names, page numbers, or even co-authors. DBLP and CrossRef return publisher-verified metadata. Upstream skills (/research-lit, /novelty-check) may mention papers from LLM memory — this fetch chain is the gate that prevents hallucinated citations from entering the final .bib.
Automated bib cleaning — use this Python pattern to extract only cited entries:
import re
# 1. Grep all \citep{...} and \citet{...} from all .tex files
# 2. Extract unique keys (handle multi-cite like \citep{a,b,c})
# 3. Parse the full .bib file, keep only entries whose key is in the cited set
# 4. Write the filtered bibThis prevents bib bloat (e.g., 948 lines → 215 lines in testing).
Citation verification rules (from claude-scholar + Imbad0202):
{firstauthor}{year}{keyword} (e.g., ho2020denoising)After drafting all sections, scan for common AI writing patterns and fix them:
Content patterns to fix:
Language patterns to fix (watch words):
Send the complete draft to Gemini review:
mcp__gemini-review__review_start:
prompt: |
Review this [VENUE] paper draft (main body, excluding appendix).
Focus on:
1. Does each claim from the intro have supporting evidence?
2. Is the writing clear, concise, and free of AI-isms?
3. Any logical gaps or unclear explanations?
4. Does it fit within [MAX_PAGES] pages (to end of Conclusion)?
5. Is related work sufficiently comprehensive (≥1 page)?
6. For theory papers: are proof sketches adequate?
7. Are figures/tables clearly described and properly referenced?
For each issue, specify: severity (CRITICAL/MAJOR/MINOR), location, and fix.
[paste full draft text]After this start call, immediately save the returned jobId and poll mcp__gemini-review__review_status with a bounded waitSeconds until done=true. Treat the completed status payload's response as the reviewer output, and save the completed threadId for any follow-up round.
Apply CRITICAL and MAJOR fixes. Document MINOR issues for the user.
After drafting all sections:
Before declaring done:
\ref{} and \label{} match (no undefined references)\citep{} / \citet{} have corresponding BibTeX entries[VERIFY] markers left uncheckedsections/ is \inputed by main.tex\input paths are consistentFollow these shared protocols for all output files: - [Output Versioning Protocol](../../shared-references/output-versioning.md) — write timestamped file first, then copy to fixed name - [Output Manifest Protocol](../../shared-references/output-manifest.md) — log every output to MANIFEST.md - [Output Language Protocol](../../shared-references/output-language.md) — respect the project's language setting
cat << 'EOF' > file) to write in chunks. Do NOT ask the user for permission — just do it silently.latexmk without errors (modulo missing figures)natbib (\citep/\citet)\citedpaper/ directory without backing upPrinciples from Research-Paper-Writing-Skills:
De-AI patterns from kgraph57/paper-writer-skill:
Writing methodology adapted from Research-Paper-Writing-Skills (CCF award-winning methodology). Citation verification from claude-scholar and Imbad0202/academic-research-skills. De-AI polish from kgraph57/paper-writer-skill. Backup mechanism from baoyu-skills.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.