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Grok Bot+18 agentsPaper Writing: Evidence-Grounded Academic Revision
Draft and revise research papers with clear arguments, precise prose, and consistent evidence.
$5
Paper Writing: Evidence-Grounded Academic Revision
Example session with this skill installed
Revise the abstract below into one coherent paragraph of 100–130 words. Keep the supplied numbers and evaluation scope. Then give a short explanation of substantive edits and any claims that need more evidence.
This is a fictional worked example. Its results are illustrative, not findings from a real paper.
Verified facts within this example
- A benchmark of 1,200 paired images for object localization under partial visibility.
- Object visibility annotations and held-out evaluation splits.
- A reference model trained with additional views available only during training.
- Accuracy on the 300-example held-out split rises from 62.0% to 68.5% under matched evaluation.
- No component ablation or cross-dataset testing was run.
Draft abstract
Object localization with partial visibility is very important. We propose a revolutionary unified framework called ViewBench. It includes 1,200 paired images, object visibility annotations, held-out splits, and a teacher-student model with a training-only additional-view branch. Our benchmark solves occlusion and provides robust generalization across all scenes. The accuracy increases from 62.0% to 68.5% on 300 held-out examples. This proves that the additional-view branch is responsible for the entire gain. The model and benchmark are excellent and useful for future research.
- Read your context and instructions
- Compiled the paper writing
Revised abstract
In this fictional worked example, ViewBench is a benchmark for object localization under partial visibility, comprising 1,200 paired images, object visibility annotations, and held-out evaluation splits. The paired images support training with additional views, while the annotations and splits define the benchmark’s evaluation setting. To illustrate its use, we evaluate a reference model with additional views available only during training. Under matched evaluation on the 300-example held-out split, accuracy rises from 62.0% to 68.5%. These illustrative results provide a reference comparison for the complete model configuration and demonstrate how the benchmark can be used to evaluate localization under partial visibility in a defined setting.
Editorial notes
The revision centers the benchmark, connects its contents to its use, and preserves the numbers, training-only additional views, and matched evaluation scope. It replaces promotional language and universal claims with the supported comparison. Attributing the entire gain to the additional-view branch would require a controlled component ablation; claims of cross-dataset generalization would require testing on other datasets. Neither was performed. The results remain explicitly fictional and illustrative.
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About this skill
Turn a draft into a coherent research argument
Paper Writing helps your AI agent draft, restructure, and refine research papers while keeping the claims tied to the evidence you provide. It supports focused paragraph edits, section revisions, and consistency checks across a manuscript.
It is designed for researchers, graduate students, and technical authors who need to explain what their work contributes, how it is evaluated, and what the results establish.
What it helps you do
- Rewrite abstracts and introductions around the research question, contribution, and supported findings.
- Explain methods, datasets, and benchmarks without forcing every paper into the same template.
- Improve related-work structure, technical prose, transitions, and terminology.
- Write experiment discussions and result captions with the correct metrics and comparison scope.
- Check whether claims, symbols, numbers, training conditions, and test-time inputs agree across sections.
- Separate manuscript-ready text from editorial notes about missing evidence.
What you receive
A complete SKILL.md package with Codex interface metadata and eight focused reference guides: Abstract and Introduction; Benchmark and Dataset Exposition; Related Work; Method and Technical Exposition; Experiments and Result Captions; Conclusion and Limitations; Local Prose; Whole-Manuscript Consistency.
How to use it
Install the skill, provide your draft and relevant evidence, and specify the edit you want. For example:
"Use paper-writing to revise this Introduction so the problem, contribution, and evaluation connect clearly. Preserve all verified results and references."
The agent returns revised text and a concise explanation of substantive edits and unresolved evidence gaps. File edits and rendering checks depend on the tools available in your agent.
Ongoing maintenance
This marketplace edition will be maintained with refinements to the writing workflow, reference guides, and compatibility as needs evolve. Future updates will be published here first. The public GitHub version will remain available and may not include every subsequent marketplace update.
Updates released for this listing are available to existing buyers through Agensi.
Open-source availability
Created and maintained in codex-skills-library. The initial release matches the core skill currently available free on GitHub under the MIT license:
https://github.com/sidiangongyuan/codex-skills-library/tree/main/skills/paper-writing
This listing provides a ready-to-install package with ongoing marketplace maintenance. The package retains its MIT license.
Scope and limitations
You supply the scientific content and evidence. Output quality depends on that material and the host model. Citation verification requires access to primary sources; this workflow does not guarantee error-free references or publication acceptance. Peer-review risk assessment, official rebuttal management, and new experiment design are separate tasks.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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