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Every researcher knows the feeling.

47 compilation errors at 2 AM. A reviewer who writes "English needs improvement." A CVPR reject that needs to become an ICML submission by Friday.

awesome-latex-skills turns any AI agent into a LaTeX expert — structured workflows, curated knowledge, and guardrails that raw prompts can't replicate.

47 errors → 0  ·  Chinglish → publication-ready  ·  CVPR → NeurIPS  ·  50 pages → structured notes

10-Second Pitch · Skills · Demos · Workflows · Quick Start · Compatibility

You describe the problem. The skill produces the fix.

• 47 compilation errors at 2 AM — latex-rescue auto-corrects typos, fixes mismatched environments, resolves package conflicts. 80+ patterns, zero manual edits.
• "English needs improvement" from Reviewer #2 — latex-polish fixes 18 categories of Chinglish, applies 100+ academic phrasebank templates, adds proper hedging. 3 intensity levels.
• CVPR rejected, ICML deadline Friday — latex-fmt switches \documentclass, removes banned packages, anonymizes, checks page limits. 15 venues covered.
• 50 papers in your reading list — paper-read produces a 5-bullet skim in 30 seconds, a structured analysis in 5 minutes, or a full critical review in 15.
• Lost the .tex, only the PDF — pdf2tex rebuilds LaTeX from any compiled PDF. 97+ math glyph mappings, table reconstruction, 7-phase pipeline.

No LaTeX expertise required. The skill handles the semicolons.

You've tried asking ChatGPT to fix your LaTeX. It guesses. It misses things. It changes your math.

Skills inject hundreds of domain-specific rules that LLMs can't reliably recall from memory. Each skill = structured workflow + reference knowledge + guardrails. Same input, same expert output, every time.

- \textbff{bold}              → Undefined control sequence
+ \textbf{bold}               → auto-fixed

- x_i is important            → Missing $ inserted
+ $x_i$ is important          → auto-fixed

- \begin{figure}...\end{table}
+ \begin{figure}...\end{figure}  → mismatch fixed

- The model can achieves good performance on the dataset.
+ The model achieves strong performance on the benchmark.

- According to the experiment, it makes the accuracy improved by 3.2%.
+ Experiments show that the method improves accuracy by 3.2%.

- Most of methods in this research field can not achieve the same result.
+ Most methods in this field fail to match this result.

- \documentclass{article}
+ \documentclass{neurips_2025}
- \author{Zhang et al.}
+ \author{Anonymous}
- (no Broader Impact section)
+ ⚠ Broader Impact required by NeurIPS — flagged

- "This paper proposes a novel transformer-based approach for..."
+ [skim] Object detection · Wang et al., CVPR 2024
+        Novelty: sparse attention for real-time. Verdict: worth deep read.

- (reading every paper front-to-back)
+ [deep] Key eq: sparse attention. Delta: 10x faster.
+        Gap: only tested on COCO. Overclaim: "SOTA" (margin 0.3%).

- (staring at a compiled PDF, no source files)
+ \documentclass{article}
+ \usepackage{amsmath,amssymb}
+ \section{Introduction}
+ The model achieves $F_1 = 92.3$ on the benchmark.
+ % [UNCERTAIN: math notation — verify subscripts]

Skills compose into pipelines for real academic scenarios:

One command to install all 5 skills:

git clone https://github.com/Calix-L/awesome-latex-skills.git && \
cp -r awesome-latex-skills/{latex-rescue,latex-polish,latex-fmt,paper-read,pdf2tex} ~/.claude/skills/

Then just type /latex-rescue, /latex-polish, etc. in Claude Code.

One skill only:

cp -r awesome-latex-skills/latex-rescue ~/.claude/skills/

Not using Claude Code? Just point your agent to the SKILL.md:

Read awesome-latex-skills/latex-rescue/SKILL.md and follow the workflow.

^{No install needed for latex-polish, latex-fmt, or paper-read. latex-rescue needs LaTeX. pdf2tex needs pip install pymupdf.}

latex-rescue/
├── SKILL.md              # the prompt — role, triggers, workflow, guardrails
├── references/           # domain knowledge the agent reads at each phase
│   ├── error-catalog.md
│   ├── package-conflicts.md
│   └── debug-workflow.md
└── agents/
    └── config.yaml       # auto-activation triggers and platform settings

MIT