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 john-data-chen/hermes-agent-backup --skill research-paper-writinggit clone --depth 1 https://github.com/john-data-chen/hermes-agent-backupWrote 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/john-data-chen/hermes-agent-backup/research-paper-writing)<a href="https://agentmods.dev/skills/john-data-chen/hermes-agent-backup/research-paper-writing"><img src="https://agentmods.dev/badge/skills/john-data-chen/hermes-agent-backup/research-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/john-data-chen/hermes-agent-backup/research-paper-writing"><img src="https://agentmods.dev/badge/skills/john-data-chen/hermes-agent-backup/research-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 | $0.00022 | $0.24773 |
| Opus 5 | $0.00011 | $0.12387 |
| Sonnet 5 | $0.00004 | $0.04955 |
| Haiku 4.5 | $0.00002 | $0.02477 |
Grade D, and why
research-paper-writing scanned grade D with 3 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 6d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- ascii-guard-ignore --> Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
# Linux: sudo apt install latexdiff Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get( This is a copy
89% identical to research-paper-writing — 532 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 — 2,378 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Paper Writing Pipeline
End-to-end pipeline for producing publication-ready ML/AI research papers targeting NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. This skill covers the full research lifecycle: experiment design, execution, monitoring, analysis, paper writing, review, revision, and submission.
This is not a linear pipeline — it is an iterative loop. Results trigger new experiments. Reviews trigger new analysis. The agent must handle these feedback loops.
┌─────────────────────────────────────────────────────────────┐
│ RESEARCH PAPER PIPELINE │
│ │
│ Phase 0: Project Setup ──► Phase 1: Literature Review │
│ │ │ │
│ ▼ ▼ │
│ Phase 2: Experiment Phase 5: Paper Drafting ◄──┐ │
│ Design │ │ │
│ │ ▼ │ │
│ ▼ Phase 6: Self-Review │ │
│ Phase 3: Execution & & Revision ──────────┘ │
│ Monitoring │ │
│ │ ▼ │
│ ▼ Phase 7: Submission │
│ Phase 4: Analysis ─────► (feeds back to Phase 2 or 5) │
│ │
└─────────────────────────────────────────────────────────────┘
When To Use This Skill
Use this skill when:
- Starting a new research paper from an existing codebase or idea
- Designing and running experiments to support paper claims
- Writing or revising any section of a research paper
- Preparing for submission to a specific conference or workshop
- Responding to reviews with additional experiments or revisions
- Converting a paper between conference formats
- Writing non-empirical papers — theory, survey, benchmark, or position papers (see Paper Types Beyond Empirical ML)
- Designing human evaluations for NLP, HCI, or alignment research
- Preparing post-acceptance deliverables — posters, talks, code releases
What ships with it
50 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/autoreason-methodology.md 19 KB
- references/checklists.md 13 KB
- references/citation-workflow.md 15 KB
- references/experiment-patterns.md 25 KB
- references/human-evaluation.md 18 KB
- references/paper-types.md 16 KB
- references/reviewer-guidelines.md 13 KB
- references/sources.md 9.2 KB
- references/writing-guide.md 16 KB
- templates/aaai2026/aaai2026-unified-supp.tex 4.4 KB
- templates/aaai2026/aaai2026-unified-template.tex 62 KB
- templates/aaai2026/aaai2026.bib 4.7 KB
- templates/aaai2026/aaai2026.bst 29 KB
- templates/aaai2026/aaai2026.sty 12 KB
- templates/aaai2026/README.md 18 KB
- templates/acl/acl_latex.tex 14 KB
- templates/acl/acl_lualatex.tex 3.0 KB
- templates/acl/acl_natbib.bst 44 KB
- templates/acl/acl.sty 11 KB
- templates/acl/anthology.bib.txt 1.1 KB
- templates/acl/custom.bib 2.0 KB
- templates/acl/formatting.md 18 KB
- templates/acl/README.md 2.1 KB
- templates/colm2025/colm2025_conference.bib 496 B
- templates/colm2025/colm2025_conference.bst 26 KB
- templates/colm2025/colm2025_conference.sty 7.5 KB
- templates/colm2025/colm2025_conference.tex 13 KB
- templates/colm2025/fancyhdr.sty 20 KB
- templates/colm2025/math_commands.tex 12 KB
- templates/colm2025/natbib.sty 44 KB
- templates/colm2025/README.md 51 B
- templates/iclr2026/fancyhdr.sty 20 KB
- templates/iclr2026/iclr2026_conference.bib 629 B
- templates/iclr2026/iclr2026_conference.bst 26 KB
- templates/iclr2026/iclr2026_conference.sty 8.8 KB
- templates/iclr2026/iclr2026_conference.tex 17 KB
- templates/iclr2026/math_commands.tex 12 KB
- templates/iclr2026/natbib.sty 44 KB
- templates/icml2026/algorithm.sty 2.2 KB
- templates/icml2026/algorithmic.sty 7.2 KB
- templates/icml2026/example_paper.bib 2.0 KB
- templates/icml2026/example_paper.tex 29 KB
- templates/icml2026/fancyhdr.sty 31 KB
- templates/icml2026/icml2026.bst 27 KB
- templates/icml2026/icml2026.sty 27 KB
- templates/neurips2025/extra_pkgs.tex 2.8 KB
- templates/neurips2025/main.tex 574 B
- templates/neurips2025/Makefile 1.0 KB
- templates/neurips2025/neurips.sty 11 KB
- templates/README.md 6.5 KB
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.
- 6d ago First seen · 2,378 lines · 22 tokens per session scan D 46868cf4a15f
research-paper-writing is a skill published in the GitHub repository john-data-chen/hermes-agent-backup (2 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 24,773 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it D with 3 findings (hidden instructions, asks for root, makes network calls). It is 89% identical to research-paper-writing, differing in 532 lines, and is treated as a copy.
Other skills, from other repositories
papyrus-writing
Base skill for the Papyrus writing assistant — the conduct, paper-quality principles, and LaTeX house style that every Papyrus editing task builds on. Load this first when working on a .tex paper in Papyrus. For the mechanics of a specific task, also load papyrus-latex-comments, papyrus-latex-suggestions…
experiment_management
Set up and manage the experiment folder structure. This is Phase 0 — it runs before any analysis begins. All bookkeeping files are JSON (never markdown).
evaluate
Compare baseline and new implementation results. Produce the machine-readable final report result.json, update experiments.json, and append a row to comparison.json.
implement
Create a new Jupyter notebook implementing the method from the research paper, using the same data as the baseline. Record implementation details and measured metrics as a structured JSON entry.
progress
Maintain a machine-readable progress file so dashboards, CLIs, and notebooks can poll the experiment's state at any time. The file is a JSON document — never markdown, never human-prose-first.
research
Read the materialized research source and extract actionable information needed to implement the proposed method. Record the findings as a structured JSON entry.