AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
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 ufy2024/AuC --skill content-research-writergit clone --depth 1 https://github.com/ufy2024/AuCWrote 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/ufy2024/auc/content-research-writer)<a href="https://agentmods.dev/skills/ufy2024/auc/content-research-writer"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/content-research-writer/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/ufy2024/auc/content-research-writer"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/content-research-writer.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 | $0.00049 | $0.03484 |
| Opus 5 | $0.00024 | $0.01742 |
| Sonnet 5 | $0.00010 | $0.00697 |
| Haiku 4.5 | $0.00005 | $0.00348 |
Grade A, and why
content-research-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 9d 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.
This is a copy
91% identical to content-research-writer — 25 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 — 558 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Research Writer
This skill acts as your writing partner, helping you research, outline, draft, and refine content while maintaining your unique voice and style.
When to Use This Skill
- Writing blog posts, articles, or newsletters
- Creating educational content or tutorials
- Drafting thought leadership pieces
- Researching and writing case studies
- Producing technical documentation with sources
- Writing with proper citations and references
- Improving hooks and introductions
- Getting section-by-section feedback while writing
What This Skill Does
- Collaborative Outlining: Helps you structure ideas into coherent outlines
- Research Assistance: Finds relevant information and adds citations
- Hook Improvement: Strengthens your opening to capture attention
- Section Feedback: Reviews each section as you write
- Voice Preservation: Maintains your writing style and tone
- Citation Management: Adds and formats references properly
- Iterative Refinement: Helps you improve through multiple drafts
How to Use
Setup Your Writing Environment
Create a dedicated folder for your article:
mkdir ~/writing/my-article-title
cd ~/writing/my-article-title
Create your draft file:
touch article-draft.md
Open Claude Code from this directory and start writing.
Basic Workflow
- Start with an outline:
Help me create an outline for an article about [topic]
- Research and add citations:
Research [specific topic] and add citations to my outline
- Improve the hook:
Here's my introduction. Help me make the hook more compelling.
- Get section feedback:
I just finished the "Why This Matters" section. Review it and give feedback.
- Refine and polish:
Review the full draft for flow, clarity, and consistency.
Instructions
When a user requests writing assistance:
-
Understand the Writing Project
Ask clarifying questions:
- What's the topic and main argument?
- Who's the target audience?
- What's the desired length/format?
- What's your goal? (educate, persuade, entertain, explain)
- Any existing research or sources to include?
- What's your writing style? (formal, conversational, technical)
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.
- 9d ago First seen · 558 lines · 49 tokens per session scan A fb6d4d0c6f9f
content-research-writer is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 3,484 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to content-research-writer, differing in 25 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…