Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/paper-writingWrote 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/skills/raja21068/autoresearch/paper-writing)<a href="https://agentmods.dev/skills/raja21068/autoresearch/paper-writing"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/paper-writing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/raja21068/autoresearch/paper-writing"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/paper-writing.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00145 | $0.08968 |
| Opus 5 | $0.00072 | $0.04484 |
| Sonnet 5 | $0.00029 | $0.01794 |
| Haiku 4.5 | $0.00015 | $0.00897 |
Grade B, and why
paper-writing 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 7d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
in `~/.claude/settings.json`: This is a copy
88% identical to paper-writing — 313 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 691 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow 3: Paper Writing Pipeline
Orchestrate a complete paper writing workflow for: $ARGUMENTS
Overview
This skill chains five sub-skills into a single automated pipeline:
/paper-plan → /paper-figure → /paper-write → /paper-compile → /auto-paper-improvement-loop
(outline) (plots) (LaTeX) (build PDF) (review & polish ×2)
Each phase builds on the previous one's output. The final deliverable is a polished, reviewed paper/ directory with LaTeX source and compiled PDF.
In this hybrid pack, the pipeline itself is unchanged, but paper-plan and paper-write use Orchestra-adapted shared references for stronger story framing and prose guidance.
Constants
- VENUE =
ICLR— Target venue. Options:ICLR,NeurIPS,ICML,CVPR,ACL,AAAI,ACM,IEEE_JOURNAL(IEEE Transactions / Letters),IEEE_CONF(IEEE conferences). Affects style file, page limit, citation format. - MAX_IMPROVEMENT_ROUNDS = 2 — Number of review→fix→recompile rounds in the improvement loop.
- REVIEWER_MODEL =
gpt-5.4— Model used via Codex MCP for plan review, figure review, writing review, and improvement loop. - AUTO_PROCEED = true — Auto-continue between phases. Set
falseto pause and wait for user approval after each phase. - HUMAN_CHECKPOINT = false — When
true, the improvement loop (Phase 5) pauses after each round's review to let you see the score and provide custom modification instructions. Whenfalse(default), the loop runs fully autonomously. Passed through to/auto-paper-improvement-loop. - ILLUSTRATION =
figurespec— Architecture/illustration generator for Phase 2b:figurespec(default, deterministic JSON→SVG via/figure-spec, best for architecture/workflow/topology),gemini(AI-generated via/paper-illustration, best for qualitative method illustrations; needsGEMINI_API_KEY),codex-image2(AI-generated via/paper-illustration-image2through the local Codex native image bridge — no external API key, uses your ChatGPT Plus/Pro quota; experimental),mermaid(Mermaid syntax via/mermaid-diagram, free, best for flowcharts), orfalse(skip Phase 2b, manual only).
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.
- 7d ago First seen · 691 lines · 145 tokens per session scan B 86c0b6167318
paper-writing is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 145 tokens to every session and 8,968 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). It is 88% identical to paper-writing, differing in 313 lines, and is treated as a copy.
Other skills, from other repositories
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…