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 growth-loggit 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/growth-log)<a href="https://agentmods.dev/skills/ufy2024/auc/growth-log"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/growth-log/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/growth-log"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/growth-log.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 57 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- medium Agent Snooping · line 20 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00034 | $0.01586 |
| Opus 5.5 | $0.00014 | $0.00634 |
| Sonnet 5.5 | $0.00007 | $0.00317 |
| Haiku 4.5 | $0.00003 | $0.00159 |
Grade A, and why
growth-log 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 1mo 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- growth-log — 94% identical, 26 lines differ
- growth-log — 94% identical, 26 lines differ
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Log Skill
The problem: Most people write "fixed a bug in X" as a learning log. That's a diary entry, not a learning artifact. A real growth log extracts the pattern so you recognize it next time.
This skill teaches: How to write learning entries that compound across sessions. Works with any note-taking system — Markdown files, Notion, Obsidian, plain text. Templates are generic; adapt to your setup.
When to Activate
- After completing a complex task (multi-file, new feature, architecture change)
- After a failure, mistake, or "that was harder than expected" moment
- When you want to review what you've learned over a period
When NOT to activate: Trivial changes (typo fixes, single-line tweaks, config value changes with no debugging). The threshold: did this task involve debugging, redoing, rollback, or a non-obvious decision? If yes → write an entry. If no → skip.
The Three Rules
Rule 1: Failures > Achievements
A failure is nutritionally denser than a success. One bug that took 2 hours to find teaches more than 3 features that worked first try.
Bad: "Successfully implemented the login flow."
Good (web dev): "Login flow: session token wasn't persisting because the cookie SameSite defaulted to Lax in Chrome 128+. Pattern: always explicitly set SameSite=None; Secure when cross-origin. Signal to recognize: auth breaks after browser upgrade or when crossing origin boundaries."
Good (data pipeline): "CSV import failed silently on empty rows because pandas.read_csv(dropna=False) keeps zero-width rows that len() counts as valid. Pattern: always df.dropna(how='all', inplace=True) before row-count validation."
Rule 2: The Bole Principle (伯乐原则)
Before writing a new entry, ask: "Is this fundamentally the same as something I already recorded?"
Same root cause, different symptom → merge, don't duplicate. New root cause → new entry.
How to check: Search existing entries for keywords from your root cause before writing. If you find a match, add your new symptom as an additional example under the existing entry rather than creating a duplicate.
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.
- 1mo ago First seen · 145 lines · 34 tokens per session scan A 096b6d001d9a
growth-log is a skill published in the GitHub repository ufy2024/AuC (1,072 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 1,586 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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