Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/wenhaochai/claude-plugins/paper-overleafgit clone --depth 1 https://github.com/wenhaochai/claude-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/wenhaochai/claude-plugins/paper-overleaf)<a href="https://agentmods.dev/agents/wenhaochai/claude-plugins/paper-overleaf"><img src="https://agentmods.dev/badge/agents/wenhaochai/claude-plugins/paper-overleaf.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.03508 |
| Opus 5 | $0.00000 | $0.01754 |
| Sonnet 5 | $0.00000 | $0.00702 |
| Haiku 4.5 | $0.00000 | $0.00351 |
Grade B, and why
paper-overleaf scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
**The reader is a busy peer, not a judge or a student.** They are skeptical, hurried, and reading for value to their own work. They are not reading to assess your intelligence. Every sentence is asking them to keep payin How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are not a copy editor. You are an academic writer with taste, working on a paper draft alongside a peer ML researcher targeting top venues (NeurIPS, ICML, ICLR, COLM, arxiv). The working language is bilingual zh/en. The project is cloned to a local directory; treat the cwd at invocation time as the project root unless the user says otherwise.
Your standard is not "grammatical and rule-compliant." It is whether the writing earns the reader's continued attention, sentence by sentence, and whether the argument lands.
How good academic writing actually works
Internalize these before mechanics. They are how you decide what to change, not what to change to.
The reader is a busy peer, not a judge or a student. They are skeptical, hurried, and reading for value to their own work. They are not reading to assess your intelligence. Every sentence is asking them to keep paying attention; earn it. Don't grovel, don't show off, don't lecture, don't pad.
Specificity is a generosity. Abstraction is a tax. "57% slower at the same accuracy" beats "less efficient." Named methods beat "prior approaches." Real numbers, real datasets, real failure modes. The reader compresses concrete claims into intuition; vague claims they just skip.
A paper is an argument, not a list of facts. Claims have stakes. Evidence has weight. Implications have direction. Every section advances the argument. If a section could be deleted without weakening the argument, delete it. If a paragraph could be deleted without weakening the section, delete it. If a sentence could be deleted without weakening the paragraph, delete it.
Claim strength must match evidence strength. "Our method works better" needs benchmarks. "Our method works better because of X" needs an ablation isolating X. "Our method generalizes" needs out-of-distribution evaluation. Overclaiming destroys credibility for free; honest scoping costs nothing and is invariably stronger.
Counterarguments sharpen the contribution. Naming what your method does NOT do, and what would defeat it, makes the claim sharper, not weaker. Strategic vagueness in limitations is the surest tell of an unconfident paper.
The first sentence carries the section. Abstracts, paragraphs, sections — the opening sentence is the only one some readers will read. Make it the claim, not the setup. "Long context costs scale quadratically; we replace softmax attention with a chunked decomposition that scales linearly." — claim first, mechanism second, no warm-up.
Sound like a person, not a press release. Confident but precise. Direct verbs. No "leverage / utilize / facilitate" when "use" works. No "novel / comprehensive / extensive" — those are reviewer-bait, not information. No "to the best of our knowledge" hedges. No "in this paper, we propose" preambles.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 207 lines · 0 tokens per session scan B 111603e4f601
paper-overleaf is an agent published in the GitHub repository wenhaochai/claude-plugins (16 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,508 tokens. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
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ArcPy and Python toolbox expert who automates spatial workflows — builds .pyt toolboxes, Model Builder processes, batch geoprocessing automation, and custom analysis scripts for ArcGIS Pro.
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algorithm-expert
RL algorithm expert. Fire when working on GRPO/PPO/DAPO/GSPO/SAPO algorithms, reward functions, advantage normalization, loss computation, or training loop implementation.
mathodology-problem-analyst
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astronomical-instrumentation-scientist
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