Rank X and LinkedIn connections by their value as potential introducers to target people or companies. Weight connection paths, target relevance, and relationship strength to identify promising introduction routes, conditional options, and gaps in the network.
Weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow layered on top of it.
This skill ranks people in your X and LinkedIn networks who could introduce you to someone you want to meet. It considers target importance, the distance of introduction paths, additional connections held by direct contacts, responsiveness, and relationship strength. Results distinguish promising introducers, paths that may work under certain conditions, and targets with no connection that could lead to an introduction.
Use it when you have particular people or companies in mind and need to decide whom to ask for an introduction. You can evaluate your network against priorities such as role, industry, and location, or use the results to choose between an introduction and direct contact. You can also examine how the ranking calculations work.
Inputs include target people or companies, your current connections, and weighting priorities. The related skill x-api provides access to X connections. Broader sales activities and network organization or growth are handled by separate related skills.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.
The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 19, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
Canonical weighted graph-ranking layer for network-aware outreach.
Use this when the user needs to:
lead-intelligence or connections-optimizerChoose this skill when the user primarily wants the ranking engine:
Do not use this by itself when the user really wants:
lead-intelligenceconnections-optimizerCollect or infer:
Given:
T = weighted target setM = your current mutuals / direct connectionsd(m, t) = shortest hop distance from mutual m to target tw(t) = target weight from signal scoringBase bridge score:
B(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)
Where:
λ is the decay factor, usually 0.5Second-order expansion:
B_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \\ M} Σ_{t ∈ T} w(t) · λ^(d(m',t))
Where:
N(m) \\ M is the set of people the mutual knows that you do notα discounts second-order reach, usually 0.3Response-adjusted final ranking:
R(m) = B_ext(m) · (1 + β · engagement(m))
Where:
engagement(m) is normalized responsiveness or relationship strengthβ is the engagement bonus, usually 0.2Interpretation:
R(m) and direct bridge paths -> warm intro asksR(m) and one-hop bridge paths -> conditional intro asksR(m) or no viable bridge -> direct outreach or follow-gap fillWeight targets before graph traversal with whatever matters for the current priority set:
Weight mutuals after traversal with:
R(m).SOCIAL GRAPH RANKING
====================
Priority Set:
Platforms:
Decay Model:
Top Bridges
- mutual / connection
base_score:
extended_score:
best_targets:
path_summary:
recommended_action:
Conditional Paths
- mutual / connection
reason:
extra hop cost:
No Warm Path
- target
recommendation: direct outreach / fill graph gap
lead-intelligence uses this ranking model inside the broader target-discovery and outreach pipelineconnections-optimizer uses the same bridge logic when deciding who to keep, prune, or addbrand-voice should run before drafting any intro request or direct outreachx-api provides X graph access and optional execution pathsNo reviews yet. Tell others what you thought.
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