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 skills add kgraph57/paper-writer-skill --skill paper-writer-skillgit clone --depth 1 https://github.com/kgraph57/paper-writer-skillWrote 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/kgraph57/paper-writer-skill/paper-writer-skill)<a href="https://agentmods.dev/skills/kgraph57/paper-writer-skill/paper-writer-skill"><img src="https://agentmods.dev/badge/skills/kgraph57/paper-writer-skill/paper-writer-skill/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/kgraph57/paper-writer-skill/paper-writer-skill"><img src="https://agentmods.dev/badge/skills/kgraph57/paper-writer-skill/paper-writer-skill.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 | $0.00106 | $0.19705 |
| Opus 5 | $0.00053 | $0.09853 |
| Sonnet 5 | $0.00021 | $0.03941 |
| Haiku 4.5 | $0.00011 | $0.01971 |
Grade A, and why
paper-writer scanned grade A with 0 findings 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 10d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 1,685 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Writer Skill
Full-pipeline academic paper writing assistant. From literature search to submission-ready manuscript.
Overview
This skill manages the entire paper writing workflow:
[Discovery] → Literature Search → Outline → Tables/Figures → Draft → Humanize → References → Quality Review → [Adversarial Review] → Pre-Submission → [Revision] → [Post-Acceptance] → [Rejection → Resubmission]
Each paper is a project directory containing structured Markdown files for every section, a literature matrix, and quality checklists.
AI-for-Science Operating Model
This skill is not only a manuscript factory (write → format → submit). It is a
research engine that wraps the writing pipeline in a discovery loop and names the
two things only a human can supply. Before doing anything else, read
~/.claude/skills/paper-writer/references/ai-for-science-model.md — it defines:
- The two human-sovereign inputs. 💡 IDEA (what is worth asking, what it means, what is ethical) and 📊 DATA (real, IRB-approved, never machine-originated). AI proposes and executes everything else at full power; the human owns exactly these two gates. AI must never originate a data point, participant, or result.
- The loop. Phase −1 Discovery (hypothesis → novelty → design → pre-registration lock) feeds the existing pipeline; Phase 6.5 Adversarial Review red-teams the central claim before any journal sees it. A red-team KILL sends the project back to Discovery — that is the system working.
- The three integrity guardrails that make AI-accelerated research more rigorous, not less: pre-registration (anti-HARKing), novelty check (anti-reinvention/inflation), adversarial self-review (anti-slop). Each prevents a documented frontier failure mode.
- The autonomy dial (Manual / Co-pilot / Autopilot). Hard rule for clinical work: the 💡 IDEA gate, the 📊 DATA gate, and the pre-registration lock are never autopilot.
The rest of this document is the execution detail. When a phase touches a sovereign gate, stop and get the human; everywhere else, run at full power.
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.
- 10d ago First seen · 1,685 lines · 106 tokens per session scan A 6805e9cde60c
paper-writer is a skill published in the GitHub repository kgraph57/paper-writer-skill (55 stars, last pushed 29d ago), licensed MIT. It adds 106 tokens to every session and 19,705 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
anti-defensive-writing-en
Stops defensive writing across the entire paper lifecycle — writing, revising, cutting, and organizing experiments. Treats the paper as a press conference, not a project summary, lab log, or self-audit: identify the single most publishable strength of the work and build the most favorable, complete, and persuasive…
anti-defensive-writing
A Chinese-language writing guide for presenting a research paper around its strongest supported contribution. It treats the paper as a focused academic presentation rather than a project diary or complete lab record.
exploring-data
Exploratory data analysis. Use when users upload .csv/.xlsx/.json/.parquet files or request "explore data", "analyze dataset", "EDA", "profile data". Small files get ydata-profiling HTML/JSON reports; large files (over 200MB or 5M rows) get fixed-memory DuckDB/sketch profiling. Also covers near-duplicate row…
food-research
Run a comprehensive, multi-source literature and evidence-synthesis workflow for food & nutrition science. Use when the user wants to research a food/nutrition topic in depth, do a literature review, build an evidence brief, screen and synthesize many sources, verify citations, or scope a systematic review.…
paper-review
Deep single-paper academic analysis — journal credibility, author profiling, reference verification, citation impact, and critical appraisal. Like a teaching hospital journal club report. Use this skill whenever the user wants to deeply review, evaluate, or dissect a single academic paper, prepare a journal club…
food-paper
Multi-subagent manuscript system for food & nutrition science covering the whole research process: understand the field, frame research questions, curate and analyze data, run statistics, build figures and tables, construct the discussion, draft, polish, and self-review — journal-aware throughout. Includes a…