social-graph-ranker
by affaan-msocial-graph-ranker is the weighted graph-ranking layer for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use the social-graph-ranker skill when you need a reusable ranking engine for Lead Research, not a full outbound or network-maintenance workflow.
This skill scores 74/100, which means it is worth listing but best framed as a focused, moderately opinionated utility rather than a fully turnkey workflow. For directory users, it looks installable if they specifically need weighted social-graph ranking for warm intro discovery and bridge analysis, but they should expect to supply some context and rely on the surrounding Claude workflow for end-to-end execution.
- Clear standalone trigger language for ranking mutuals, mapping warm paths, and bridge scoring across X and LinkedIn.
- Good operational boundaries: it explicitly says when to use this skill versus lead-intelligence or connections-optimizer, reducing misuse.
- Substantive body content with workflow-oriented sections and no placeholder/test markers, which supports real agent execution.
- No install command, support files, or references, so users must infer setup and integration details from SKILL.md alone.
- The skill is narrowly scoped to the ranking engine; it does not cover broader outreach or network-maintenance workflows.
Overview of social-graph-ranker skill
social-graph-ranker is the weighted graph-ranking layer for network-aware outreach: it helps you score mutuals, bridge paths, and warm-intro options across X and LinkedIn. It is the right fit when you want the ranking engine itself, not a full lead-gen or network-maintenance workflow. If you are trying to answer “who should introduce me to this target?” or “which bridge in my graph is strongest?”, this social-graph-ranker skill gives you a clearer decision model than a generic prompt.
Best fit for lead research and warm intros
Use social-graph-ranker for Lead Research when you already have a target list, ICP, or set of people and need to rank your network against it. The output is most useful for prioritizing outreach paths, identifying bridge value, and separating strong warm routes from weak or speculative ones.
What it actually ranks
The skill focuses on intro value, bridge scoring, and network gap analysis. That means it is useful for mutual ranking, second-order connection analysis, and warm-path discovery, but less useful if you need a broad outbound system, CRM automation, or lead sourcing from scratch.
When not to install it
Do not choose social-graph-ranker if your main need is lead generation, sequencing, or list building. If you want network growth and cleanup, a broader connections workflow is a better fit. This skill is strongest when the graph already exists and the question is how to use it.
How to Use social-graph-ranker skill
Install and open the right files first
Install social-graph-ranker with npx skills add affaan-m/everything-claude-code --skill social-graph-ranker. Then read SKILL.md first, because this repo is currently a single-file skill with no helper folders to cross-check. Since there are no rules/, references/, or scripts/ files, the install decision and the prompt quality both depend on that one source of truth.
Give the skill structured graph inputs
The social-graph-ranker usage pattern works best when you supply: target people or companies, your current network on X or LinkedIn, and the weighting priorities you care about. For example, instead of “find warm intros for me,” give “rank these 20 targets by intro likelihood using role match, geography, and second-degree connectivity.”
Turn a rough ask into a complete prompt
A strong prompt for this social-graph-ranker guide should include the graph scope, target set, and the ranking goal. For example: “Use social-graph-ranker to score my LinkedIn mutuals against these 12 SaaS founders. Weight direct overlap and responsiveness higher than industry similarity, and show the top 5 bridge paths with brief reasoning.” That gives the model enough context to apply the graph logic instead of inventing a generic outreach plan.
Workflow that improves output quality
Start with a small target set, review the top-ranked bridges, then expand only after the scoring logic looks right. If the first pass feels vague, tighten the inputs by naming the platform, the depth limit, and what “best” means in your case. The skill is most useful when you ask it to rank and explain, not just list connections.
social-graph-ranker skill FAQ
Is social-graph-ranker only for X or LinkedIn?
No. The repo description mentions X and LinkedIn, but the core idea is graph-based ranking of relationship paths. It works best when you can represent the network clearly enough for the scoring logic to compare paths.
How is it different from a normal prompt?
A normal prompt can ask for warm intros, but social-graph-ranker adds a repeatable ranking lens: it helps compare mutuals, bridge strength, and path value in a more structured way. That is useful when the decision matters and you want the same logic applied across multiple targets.
Is this beginner-friendly?
Yes, if you can provide a target list and a basic view of your network. You do not need to be a graph-theory expert, but you do need enough input detail for the ranking to be meaningful. The main beginner mistake is giving an underspecified goal and expecting the skill to infer the whole network.
When should I use something else?
Use a broader outreach or network-ops skill if you need lead sourcing, sequencing, or network maintenance. social-graph-ranker is the better choice when the question is specifically about ranking bridges and warm paths for Lead Research.
How to Improve social-graph-ranker skill
Make the ranking criteria explicit
The fastest way to improve social-graph-ranker is to say what should matter most: seniority match, industry overlap, geography, responsiveness, closeness, or second-degree path quality. If you do not state priorities, the output may overweight obvious but low-value connections.
Provide the graph in a usable shape
The skill performs better when you give it a compact, structured network view instead of a loose narrative. A simple list like “person, platform, relation type, known overlap, recent interaction” is much more useful than “I know a lot of people in tech.”
Watch for the common failure mode
The most common failure mode is overconfidence from thin data: a bridge can look strong because it is well-connected, not because it is relevant. Ask the social-graph-ranker skill to separate “reachable” from “credible” paths so you do not confuse access with fit.
Iterate with a second pass
After the first ranking, ask for a narrower rerank: “remove weak ties,” “favor direct mutuals,” or “optimize for a single target company.” That second pass usually produces more actionable output than trying to perfect the prompt on the first try, especially in social-graph-ranker for Lead Research.