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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 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.

affaan-m on GitHub

Curated by PromptsEdge from a public repo · MIT license. All credit goes to the author.

What is this skill?

Reorganize the user's network instead of treating outbound as a one-way prospecting list.

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

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

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

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