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 agentmods add skills/ufy2024/auc/connections-optimizernpx skills add ufy2024/AuC --skill connections-optimizergit 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/connections-optimizer)<a href="https://agentmods.dev/skills/ufy2024/auc/connections-optimizer"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/connections-optimizer.svg" alt="Measured on agentmods" 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 | $0.00064 | $0.01302 |
| Opus 5 | $0.00032 | $0.00651 |
| Sonnet 5 | $0.00013 | $0.00260 |
| Haiku 4.5 | $0.00006 | $0.00130 |
Grade A, and why
connections-optimizer 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 2d 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
5 near-identical copies found in the catalogue:
- connections-optimizer — 92% identical, 30 lines differ
- connections-optimizer — 91% identical, 28 lines differ
- connections-optimizer — 91% identical, 28 lines differ
- connections-optimizer — 89% identical, 29 lines differ
- connections-optimizer — 89% identical, 29 lines differ
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Connections Optimizer
Reorganize the user's network instead of treating outbound as a one-way prospecting list.
This skill handles:
- X following cleanup and expansion
- LinkedIn follow and connection analysis
- review-first prune queues
- add and follow recommendations
- warm-path identification
- Apple Mail, X DM, and LinkedIn draft generation in the user's real voice
When to Activate
- the user wants to prune their X following
- the user wants to rebalance who they follow or stay connected to
- the user says "clean up my network", "who should I unfollow", "who should I follow", "who should I reconnect with"
- outreach quality depends on network structure, not just cold list generation
Required Inputs
Collect or infer:
- current priorities and active work
- target roles, industries, geos, or ecosystems
- platform selection: X, LinkedIn, or both
- do-not-touch list
- mode:
light-pass,default, oraggressive
If the user does not specify a mode, use default.
Tool Requirements
Preferred
x-apifor X graph inspection and recent activitylead-intelligencefor target discovery and warm-path rankingsocial-graph-rankerwhen the user wants bridge value scored independently of the broader lead workflow- Exa / deep research for person and company enrichment
brand-voicebefore drafting outbound
Fallbacks
- browser control for LinkedIn analysis and drafting
- browser control for X if API coverage is constrained
- Apple Mail or Mail.app drafting via desktop automation when email is the right channel
Safety Defaults
- default is review-first, never blind auto-pruning
- X: prune only accounts the user follows, never followers
- LinkedIn: treat 1st-degree connection removal as manual-review-first
- do not auto-send DMs, invites, or emails
- emit a ranked action plan and drafts before any apply step
Platform Rules
X
- mutuals are stickier than one-way follows
- non-follow-backs can be pruned more aggressively
- heavily inactive or disappeared accounts should surface quickly
- engagement, signal quality, and bridge value matter more than raw follower count
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.
- 2d ago First seen · 212 lines · 64 tokens per session scan A 6a67a9e44f9d
connections-optimizer is a skill published in the GitHub repository ufy2024/AuC (1,091 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 1,302 once invoked, about $0.0003 per session on Opus 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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