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-discoverygit 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-discovery)<a href="https://agentmods.dev/skills/ufy2024/auc/brand-discovery"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/brand-discovery/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-discovery"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/brand-discovery.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 | $0.00080 | $0.01800 |
| Opus 5 | $0.00040 | $0.00900 |
| Sonnet 5 | $0.00016 | $0.00360 |
| Haiku 4.5 | $0.00008 | $0.00180 |
Grade A, and why
brand-discovery 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
91% identical to brand-discovery — 33 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Discovery
Use this skill to conduct a structured, adaptive brand identity interview.
The goal is a complete 90_SYNTHESIS.md — a master brandbook the
organization can use to brief designers, writers, and external
collaborators.
The interview runs across multiple sessions. Capture answers to disk as you go so that no elicited knowledge is lost when a conversation ends, and so a later session can resume from where the last one stopped.
When to Activate
- A brand is being created, repositioned, or needs a written identity reference to brief collaborators.
- Multiple sessions are expected — the conversation will span days or weeks.
- Multiple founders or stakeholders need individual interviews before a reconciliation pass.
- The user wants a structured, repeatable method rather than an ad-hoc chat.
- Existing brand documentation is scattered, implicit, or founder-dependent and needs to be made explicit.
Session start protocol
On every activation, perform these steps before asking any interview question:
- Check for prior progress. Look for an existing set of module files
and a
state.jsoncheckpoint in the project's brand-identity directory. If none exists, this is a fresh start — confirm the brand name, participants, and where to save the brand-identity files, then begin at the first module. - Read the current module file if one is in progress, and scan its Raw section for previously captured answers.
- Report to the user in two or three sentences: which module we are in, its status, and what remains. Then ask: "Continue here, or switch module?"
Interview discipline
Apply these rules throughout every module:
- One question at a time. Never present a list of questions.
- After each answer: short paraphrase → one deepening probe OR close the thread if the topic is saturated. Never move on silently.
- Laddering: for every "what" answer, follow with "Why does that matter to you?" until a core value surfaces (typically two to four iterations).
- 5 Whys: for beliefs or positioning claims — push until the root reason, not the surface declaration, is on the table.
- Detect thin answers: if generic, jargon-heavy, or vague, ask for one concrete example, a client story, or a number.
- Projective techniques (use once per module to break a plateau):
- "If the brand were a person, how would they walk into a room?"
- Brand obituary: "If the organization closed in five years, what would customers miss? What would you regret not having said?"
- Competitive contrast: "Name one peer you admire but would never want to become. What specifically makes them the wrong model?"
- Saturation signal: when two consecutive probes produce no new information, summarise and close the module.
- End of module: write a structured module file with two sections:
## Raw— verbatim quotes and examples.## Synthesis— your interpretation, three candidate formulations, open questions, contradictions between participants. Then update thestate.jsoncheckpoint (see State protocol below).
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 · 165 lines · 80 tokens per session scan A dcee51e2dba5
brand-discovery is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 1,800 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to brand-discovery, differing in 33 lines, and is treated as a copy.
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