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-enginegit 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-engine)<a href="https://agentmods.dev/skills/ufy2024/auc/content-engine"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/content-engine/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-engine"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/content-engine.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 | $0.00055 | $0.01036 |
| Opus 5 | $0.00028 | $0.00518 |
| Sonnet 5 | $0.00011 | $0.00207 |
| Haiku 4.5 | $0.00006 | $0.00104 |
Grade A, and why
content-engine 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 8d 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
94% identical to content-engine — 27 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Engine
Build platform-native content without flattening the author's real voice into platform slop.
When to Activate
- writing X posts or threads
- drafting LinkedIn posts or launch updates
- scripting short-form video or YouTube explainers
- repurposing articles, podcasts, demos, docs, or internal notes into public content
- building a launch sequence or ongoing content system around a product, insight, or narrative
Non-Negotiables
- Start from source material, not generic post formulas.
- Adapt the format for the platform, not the persona.
- One post should carry one actual claim.
- Specificity beats adjectives.
- No engagement bait unless the user explicitly asks for it.
Source-First Workflow
Before drafting, identify the source set:
- published articles
- notes or internal memos
- product demos
- docs or changelogs
- transcripts
- screenshots
- prior posts from the same author
If the user wants a specific voice, build a voice profile from real examples before writing.
Use brand-voice as the canonical workflow when voice consistency matters across more than one output.
Voice Handling
brand-voice is the canonical voice layer.
Run it first when:
- there are multiple downstream outputs
- the user explicitly cares about writing style
- the content is launch, outreach, or reputation-sensitive
Reuse the resulting VOICE PROFILE here instead of rebuilding a second voice model.
If the user wants Affaan / ECC voice specifically, still treat brand-voice as the source of truth and feed it the best live or source-derived material available.
Hard Bans
Delete and rewrite any of these:
- "In today's rapidly evolving landscape"
- "game-changer", "revolutionary", "cutting-edge"
- "here's why this matters" unless it is followed immediately by something concrete
- ending with a LinkedIn-style question just to farm replies
- forced casualness on LinkedIn
- fake engagement padding that was not present in the source material
Platform Adaptation Rules
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.
- 8d ago First seen · 154 lines · 55 tokens per session scan A 037faa2dde86
content-engine is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 1,036 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to content-engine, differing in 27 lines, and is treated as a copy.
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