ccs-writing-style

ccs-writing-style is a skill for Claude Code from brycewang-stanford/Awesome-Journal-Skills. It costs 60 tokens per session (784 once invoked), scanned A, original, MIT.

Writing guidance for ACM CCS security research papers, focused on clear threat models, measured evidence, honest claims, and fitting the main argument into a 12-page format. A threat model states what an attacker can do, knows, and wants.

In plain words
What is it for?
Use it to revise a security paper, define its attacker and contribution, connect claims to measurements or proofs, compress material, and preserve double-blind wording.
Why use it?
It helps prevent security papers from hiding assumptions, overstating results, or making claims without evidence.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the ACM-CCS-Skills plugin — 12 skills shipped together

Good fit Use it to revise a security paper, define its attacker and contribution, connect claims to measurements or proofs, compress material, and preserve double-blind wording.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/brycewang-stanford/awesome-journal-skills/ccs-writing-style
About the project

Awesome Journal Skills is a collection of agent skill packs tailored to hundreds of academic journals across fields including economics, social science, medicine, science, and engineering. Researchers use the packs for tasks such as choosing topics, designing empirical strategies, preparing tables and figures, submitting papers, and responding to reviewers. The catalogue entries are the project's journal-specific skills and related plugins.

brycewang-stanford/Awesome-Journal-Skills · 1,097 stars · on GitHub · copaper.ai

Install

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.

Any agent
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ccs-writing-style
Clone the repo
git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills

Made for: Claude Code.

Or install ACM-CCS-Skills, the plugin that ships this one along with the rest of its 12 skills.

Wrote 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.

agentmods badge for ccs-writing-style

README.md
[![agentmods](https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ccs-writing-style/github.svg)](https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ccs-writing-style)
Your own site
<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ccs-writing-style"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ccs-writing-style/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.

agentmods 80×15 button for ccs-writing-style

Your own site · 80×15
<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ccs-writing-style"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ccs-writing-style.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 784 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00060 $0.00784
Opus 5 $0.00030 $0.00392
Sonnet 5 $0.00012 $0.00157
Haiku 4.5 $0.00006 $0.00078

Measured 13d ago against content hash 5e3e71c5b7c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

ccs-writing-style 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 13d 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.

ACM-CCS-Skills/skills/ccs-writing-style/SKILL.md · 67 lines

How it starts

The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CCS Writing Style

Use this when revising the main paper. CCS papers must state who the attacker is, what the contribution defends or breaks, and enough measured evidence that an adversarial reviewer cannot poke the central claim.

Revision rules

  • Put the security contribution on the first page: the problem, the attacker, the gap in existing defenses or attacks, the mechanism, and the evidence that it works.
  • Make the threat model explicit and early. State attacker capabilities, knowledge, position, and goal before results; CCS reviewers reject on hidden or shifting adversary assumptions.
  • Pair every claim with proof, measurement, exploit demonstration, or a cost number. A security claim without a bound on the adversary or a measured cost reads as marketing.
  • Use the 12-page body for the core argument; move protocol transcripts, extra measurements, and formal proofs to appendices without making the body unintelligible.
  • Do not overstate exploitability beyond the conditions tested; scope every "practical" claim to the environment, versions, and configuration measured.
  • Keep double-blind wording in self-citations, tool names, acknowledgements, and artifact descriptions.

Threat-model discipline

  • Give the adversary a named model with capabilities enumerated once and referenced by name; CCS readers audit whether every attack step stays inside those capabilities.
  • Separate what the attacker knows from what it can do from where it sits in the system; a conflated model is the most common source of "the attack assumes too much" reviews.
  • State the security goal as a property (confidentiality, integrity, availability, unlinkability) and say what "broken" means quantitatively.
  • When a defense is evaluated, name the adaptive attacker it was tested against; a defense measured only against the attack it was designed to stop is a standing CCS complaint.
  • Label conjecture, heuristic argument, and proved result distinctly; mixing them near a security claim is a credibility leak at this venue.

Read the full file on GitHub · 67 lines

Changes

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.

  1. 13d ago First seen · 67 lines · 60 tokens per session scan A 5e3e71c5b7c8

Subscribe to this mod's changes

ccs-writing-style is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,097 stars, last pushed 16d ago), licensed MIT. It adds 60 tokens to every session and 784 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

fin-submit-check

A pre-submission checklist for an academic paper and its LaTeX files. It checks journal requirements such as anonymous authorship, length, spacing, margins, citations, figures, data availability, and other submission details.

csmar432/finai-research · 37 tokens

alterlab-paper-writer

Drafts and revises academic papers through a 12-agent pipeline with hardened LaTeX output (apa7 document class, justified text, table column-width formula, centered bilingual abstracts, standardized font stack, PDF compiled from LaTeX), supporting IMRaD, literature review, theoretical, case study, policy brief, and…

AlterLab-IEU/AlterLab-Academic-Skills · 276 tokens

alterlab-imaging-data-commons

Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…

AlterLab-IEU/AlterLab-Academic-Skills · 90 tokens

alterlab-pyhealth

Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare…

AlterLab-IEU/AlterLab-Academic-Skills · 117 tokens

alterlab-phylogenetics

Build phylogenetic trees end-to-end from raw sequences — MAFFT multiple sequence alignment, optional TrimAl trimming, IQ-TREE 2 maximum-likelihood inference with model selection and bootstraps, FastTree for large datasets, then visualize with ETE3 or FigTree. Use when reconstructing trees from sequences (FASTA) for…

AlterLab-IEU/AlterLab-Academic-Skills · 152 tokens

alterlab-qiime2-amplicon

Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 amplicon distribution (2026.1; renamed to "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier classify-sklearn against a…

AlterLab-IEU/AlterLab-Academic-Skills · 266 tokens