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 brand-voicegit 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/brand-voice)<a href="https://agentmods.dev/skills/ufy2024/auc/brand-voice"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/brand-voice/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/brand-voice"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/brand-voice.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 | $0.00053 | $0.00876 |
| Opus 5 | $0.00026 | $0.00438 |
| Sonnet 5 | $0.00011 | $0.00175 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
brand-voice 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.
This is a copy
88% identical to brand-voice — 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Voice
Build a durable voice profile from real source material, then use that profile everywhere instead of re-deriving style from scratch or defaulting to generic AI copy.
When to Activate
- the user wants content or outreach in a specific voice
- writing for X, LinkedIn, email, launch posts, threads, or product updates
- adapting a known author's tone across channels
- the existing content lane needs a reusable style system instead of one-off mimicry
Source Priority
Use the strongest real source set available, in this order:
- recent original X posts and threads
- articles, essays, memos, launch notes, or newsletters
- real outbound emails or DMs that worked
- product docs, changelogs, README framing, and site copy
Do not use generic platform exemplars as source material.
Collection Workflow
- Gather 5 to 20 representative samples when available.
- Prefer recent material over old material unless the user says the older writing is more canonical.
- Separate "public launch voice" from "private working voice" if the source set clearly splits.
- If live X access is available, use
x-apito pull recent original posts before drafting. - If site copy matters, include the current ECC landing page and repo/plugin framing.
What to Extract
- rhythm and sentence length
- compression vs explanation
- capitalization norms
- parenthetical use
- question frequency and purpose
- how sharply claims are made
- how often numbers, mechanisms, or receipts show up
- how transitions work
- what the author never does
Output Contract
Produce a reusable VOICE PROFILE block that downstream skills can consume directly. Use the schema in references/voice-profile-schema.md.
Keep the profile structured and short enough to reuse in session context. The point is not literary criticism. The point is operational reuse.
Affaan / ECC Defaults
If the user wants Affaan / ECC voice and live sources are thin, start here unless newer source material overrides it:
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 · 120 lines · 53 tokens per session scan A dd3e67097193
brand-voice is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 876 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to brand-voice, differing in 27 lines, and is treated as a copy.
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