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/jamkris/everything-gemini-code/connections-optimizernpx skills add Jamkris/everything-gemini-code --skill connections-optimizergit clone --depth 1 https://github.com/Jamkris/everything-gemini-codeWrote 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/jamkris/everything-gemini-code/connections-optimizer)<a href="https://agentmods.dev/skills/jamkris/everything-gemini-code/connections-optimizer"><img src="https://agentmods.dev/badge/skills/jamkris/everything-gemini-code/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 | $0.01199 |
| Opus 5 | $0.00032 | $0.00600 |
| Sonnet 5 | $0.00013 | $0.00240 |
| Haiku 4.5 | $0.00006 | $0.00120 |
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.
This is a copy
92% identical to connections-optimizer — 30 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 — 190 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 Use
- 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 · 190 lines · 64 tokens per session scan A 1f159ec08759
connections-optimizer is a skill published in the GitHub repository Jamkris/everything-gemini-code (87 stars, last pushed 3mo ago), licensed MIT. It adds 64 tokens to every session and 1,199 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to connections-optimizer, differing in 30 lines, and is treated as a copy.
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