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connections-optimizer

Reorganize the user's X and LinkedIn network with review-first pruning, add/follow recommendations, and channel-specific warm outreach drafted in the user's…

Skill by unrealandychan

IndexedMIT★ 12
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Reorganize the user's X and LinkedIn network with review-first pruning, add/follow recommendations, and channel-specific warm outreach drafted in the user's real voice. Use when the user wants to clean up following lists, grow toward current priorities, or rebalance a social graph around higher-signal relationships.

Use cases: sales

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What's inside

Skill name
connections-optimizer
Description
Reorganize the user's X and LinkedIn network with review-first pruning, add/follow recommendations, and channel-specific warm outreach drafted in the user's real voice. Use when the user wants to clean up following lists, grow toward current priorities, or rebalance a social graph around higher-signal relationships.
License
MIT
Metadata
origin: ECC
Instructions
185 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# 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`, or `aggressive`

If the user does not specify a mode, use `default`.

## Tool Requirements

### Preferred

- `x-api` for X graph inspection and recent activity
- `lead-intelligence` for target discovery and warm-path ranking
- `social-graph-ranker` when the user wants bridge value scored independently of the broader lead workflow
- Exa / deep research for person and company enrichment
- `brand-voice` before 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

### LinkedIn

- API-first if the user actually has LinkedIn API access
- browser workflow must work when API access is missing
- distinguish outbound follows from accepted 1st-degree connections
- outbound follows can be pruned more freely
- accepted 1st-degree connections should default to review, not auto-remove

## Modes

### `light-pass`

- prune only high-confidence low-value one-way follows
- surface the rest for review
- generate a small add/follow list

### `default`

- balanced prune queue
- balanced keep list
- ranked add/follow queue
- draft warm intros or direct outreach where useful

### `aggressive`

- larger prune queue
- lower tolerance for stale non-follow-backs
- still review-gated before apply

## Scoring Model

Use these positive signals:

- reciprocity
- recent activity
- alignment to current priorities
- network bridge value
- role relevance
- real engagement history
- recent presence and responsiveness

Use these negative signals:

- disappeared or abandoned account
- stale one-way follow
- off-priority topic cluster
- low-value noise
- repeated non-response
- no follow-back when many better replacements exist

Mutuals and real warm-path bridges should be penalized less aggressively than one-way follows.

## Workflow

From unrealandychan/clean-code-skill, MIT.

More from unrealandychan/clean-code-skill

Signals

Stars
12
Forks
1
Open issues
0
Last commit (this folder)
Sep 8, 2026 (1 month ago)
Last release
Apr 17, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

connections-optimizer skill. Agent Mart. https://agentmart.dev/skills/unrealandychan-connections-optimizer (last updated 2026-10-24)