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 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.

16 stars

Best use case

connections-optimizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using connections-optimizer should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/connections-optimizer/SKILL.md --create-dirs "https://raw.githubusercontent.com/Jamkris/everything-gemini-code/main/skills/connections-optimizer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/connections-optimizer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How connections-optimizer Compares

Feature / Agentconnections-optimizerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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`, 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

1. Capture priorities, do-not-touch constraints, and selected platforms.
2. Pull the current following / connection inventory.
3. Score prune candidates with explicit reasons.
4. Score keep candidates with explicit reasons.
5. Use `lead-intelligence` plus research surfaces to rank expansion candidates.
6. Match the right channel:
   - X DM for warm, fast social touch points
   - LinkedIn message for professional graph adjacency
   - Apple Mail draft for higher-context intros or outreach
7. Run `brand-voice` before drafting messages.
8. Return a review pack before any apply step.

## Review Pack Format

```text
CONNECTIONS OPTIMIZER REPORT
============================

Mode:
Platforms:
Priority Set:

Prune Queue
- handle / profile
  reason:
  confidence:
  action:

Review Queue
- handle / profile
  reason:
  risk:

Keep / Protect
- handle / profile
  bridge value:

Add / Follow Targets
- person
  why now:
  warm path:
  preferred channel:

Drafts
- X DM:
- LinkedIn:
- Apple Mail:
```

## Outbound Rules

- Default email path is Apple Mail / Mail.app draft creation.
- Do not send automatically.
- Choose the channel based on warmth, relevance, and context depth.
- Do not force a DM when an email or no outreach is the right move.
- Drafts should sound like the user, not like automated sales copy.

## Related Skills

- `brand-voice` for the reusable voice profile
- `social-graph-ranker` for the standalone bridge-scoring and warm-path math
- `lead-intelligence` for weighted target and warm-path discovery
- `x-api` for X graph access, drafting, and optional apply flows
- `content-engine` when the user also wants public launch content around network moves

Related Skills

prompt-optimizer

16
from Jamkris/everything-gemini-code

Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization.

x-api

16
from Jamkris/everything-gemini-code

X/Twitter API integration for posting tweets, threads, reading timelines, search, and analytics. Covers OAuth auth patterns, rate limits, and platform-native content posting. Use when the user wants to interact with X programmatically.

workspace-surface-audit

16
from Jamkris/everything-gemini-code

Audit the active repo, MCP servers, plugins, connectors, env surfaces, and harness setup, then recommend the highest-value ECC-native skills, hooks, agents, and operator workflows. Use when the user wants help setting up Gemini CLI or understanding what capabilities are actually available in their environment.

visa-doc-translate

16
from Jamkris/everything-gemini-code

Translate visa application documents (images) to English and create a bilingual PDF with original and translation

videodb

16
from Jamkris/everything-gemini-code

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.

video-editing

16
from Jamkris/everything-gemini-code

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

verification-loop

16
from Jamkris/everything-gemini-code

Comprehensive verification system for code changes

unified-notifications-ops

16
from Jamkris/everything-gemini-code

Operate notifications as one ECC-native workflow across GitHub, Linear, desktop alerts, hooks, and connected communication surfaces. Use when the real problem is alert routing, deduplication, escalation, or inbox collapse.

ui-demo

16
from Jamkris/everything-gemini-code

Record polished UI demo videos using Playwright. Use when the user asks to create a demo, walkthrough, screen recording, or tutorial video of a web application. Produces WebM videos with visible cursor, natural pacing, and professional feel.

token-budget-advisor

16
from Jamkris/everything-gemini-code

Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.

terminal-ops

16
from Jamkris/everything-gemini-code

Evidence-first repo execution workflow for ECC. Use when the user wants a command run, a repo checked, a CI failure debugged, or a narrow fix pushed with exact proof of what was executed and verified.

team-builder

16
from Jamkris/everything-gemini-code

Interactive agent picker for composing and dispatching parallel teams