— 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. origin: ECC — # 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 “`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