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affaan-m/connections-optimizer

affaan-m

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.

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0installs0uses~1.4k
v1.1Saved Apr 20, 2026

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

  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

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.
  • 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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Overall Score

84/100

Grade

B

Good

Safety

88

Quality

82

Clarity

86

Completeness

78

Summary

A network management skill that helps users strategically reorganize their X and LinkedIn networks through review-first pruning, targeted expansion, and warm-path outreach. The skill prioritizes relationship quality and relevance over raw follower counts, with explicit safety gates preventing auto-send operations and requiring human review before any network modifications.

Detected Capabilities

X API integration for graph inspection and following analysisLinkedIn connection and follow status analysis (API-first with browser fallback)Account scoring model using engagement, reciprocity, bridge value, and priority alignmentMulti-mode pruning (light-pass, default, aggressive) with review-first safety gatesWarm-path discovery and ranking via lead-intelligence integrationVoice-matched outreach draft generation across X DM, LinkedIn, and Apple MailDo-not-touch list and priority constraint enforcementStructured review pack generation before apply step

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

clean up networkprune following listnetwork rebalancingwarm outreachconnection optimizationunfollow candidatesfollow recommendationsnetwork bridge value

Risk Signals

INFO

Integration with social media APIs and potential account modification capabilities

Tool Requirements section, Platform Rules
INFO

Draft generation for outreach messages that will be sent to real accounts

Outbound Rules, Workflow step 7-8
INFO

Browser automation fallback for LinkedIn when API access is unavailable

Tool Requirements - Fallbacks section
WARNING

No explicit mention of rate limiting or throttling guardrails for API operations

Tool Requirements section

Use Cases

  • Clean up X following list by removing low-signal accounts while preserving valuable mutual relationships
  • Rebalance LinkedIn connections to align with current professional priorities and role changes
  • Identify and reach out to warm-path contacts across X, LinkedIn, and email with personalized outreach
  • Score network accounts by engagement quality, reciprocity, and bridge value rather than raw metrics
  • Generate channel-appropriate drafts (DM, message, email) that match the user's authentic voice

Quality Notes

  • Excellent safety design: default is review-first with explicit gates (review pack before apply), never auto-sends DMs/emails/invites
  • Clear mode definitions (light-pass, default, aggressive) with well-articulated risk-confidence tradeoffs
  • Well-documented scoring model distinguishes positive and negative signals with nuanced handling of reciprocity vs one-way follows
  • Platform-specific rules show domain knowledge (e.g., mutuals stickier than one-way, LinkedIn 1st-degree connections require review)
  • Structured review pack template makes output auditable and actionable for human review
  • Strong integration narrative with related skills (brand-voice, social-graph-ranker, lead-intelligence) shows modular design
  • Tool requirements clearly separate preferred (API-first) from fallbacks (browser automation), reducing fragility
  • Outbound rules appropriately constrain channel selection and tone (user's real voice, not sales copy)
  • Limitations: skill does not explicitly document rate limits, error handling for API failures, or rollback procedures if pruning decisions are later regretted
  • Missing: no guidance on conflict resolution if do-not-touch list contradicts scoring (e.g., low-value account in protected list)
Model: claude-haiku-4-5-20251001Analyzed: Apr 20, 2026

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Version History

v1.1

Content updated

2026-04-20

Latest
v1.0

No changelog

2026-04-12

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