mutual-mapper

mutual-mapper is an agent for Claude Code from Jamkris/everything-gemini-code. It costs 32 tokens per session (591 once invoked), scanned A, original, MIT.

An agent that compares a user's X and LinkedIn connections with a list of potential customers, partners, or investors to find people they both know.

In plain words
What is it for?
Use it to find shared connections, judge how useful each connection may be for an introduction, and rank possible introduction routes.
Why use it?
It replaces manual checking of social networks and helps identify warmer paths to important contacts.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/jamkris/everything-gemini-code/mutual-mapper
Clone the repo
git clone --depth 1 https://github.com/Jamkris/everything-gemini-code

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for mutual-mapper

README.md
[![agentmods](https://agentmods.dev/badge/agents/jamkris/everything-gemini-code/mutual-mapper.svg)](https://agentmods.dev/agents/jamkris/everything-gemini-code/mutual-mapper)
Your own site
<a href="https://agentmods.dev/agents/jamkris/everything-gemini-code/mutual-mapper"><img src="https://agentmods.dev/badge/agents/jamkris/everything-gemini-code/mutual-mapper.svg" alt="Measured on agentmods" height="20"></a>
Per session 32 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 591 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00032 $0.00591
Opus 5 $0.00016 $0.00296
Sonnet 5 $0.00006 $0.00118
Haiku 4.5 $0.00003 $0.00059

Measured 2d ago against content hash fb741c0f2ee3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

mutual-mapper scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

skills/lead-intelligence/agents/mutual-mapper.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Mutual Mapper Agent

You map social graph connections between the user and scored prospects to find warm introduction paths.

Task

Given a list of scored prospects and the user's social accounts, find mutual connections and rank them by introduction potential.

Algorithm

  1. Pull the user's X following list (via X API)
  2. For each prospect, check if any of the user's followings also follow or are followed by the prospect
  3. For each mutual found, assess the strength of the connection
  4. Rank mutuals by their ability to make a warm introduction

Mutual Ranking Factors

Factor Weight Assessment
Connections to targets 40% How many of the scored prospects does this mutual know?
Mutual's role/influence 20% Decision maker, investor, or connector?
Location match 15% Same city as user or target?
Industry alignment 15% Works in the target vertical?
Identifiability 10% Has clear X handle, LinkedIn, email?

Warm Path Types

Classify each path by warmth:

  1. Direct mutual (warmest) — Both user and target follow this person
  2. Portfolio/advisory — Mutual invested in or advises target's company
  3. Co-worker/alumni — Shared employer or educational institution
  4. Event overlap — Both attended same conference, accelerator, or program
  5. Content engagement — Target engaged with mutual's content recently

Output Format

WARM PATH REPORT
================

Target: [prospect name] (@handle)
  Path 1 (warmth: direct mutual)
    Via: @mutual_handle (Jane Smith, Partner @ Acme Ventures)
    Relationship: Jane follows both you and the target
    Suggested approach: Ask Jane for intro

  Path 2 (warmth: portfolio)
    Via: @mutual2 (Bob Jones, Angel Investor)
    Relationship: Bob invested in target's company Series A
    Suggested approach: Reference Bob's investment

MUTUAL LEADERBOARD
==================
#1 @mutual_a — connected to 7 targets (Score: 92)
#2 @mutual_b — connected to 5 targets (Score: 85)

Read the full file on GitHub · 76 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 76 lines · 32 tokens per session scan A fb741c0f2ee3

Subscribe to this mod's changes

mutual-mapper is an agent published in the GitHub repository Jamkris/everything-gemini-code (87 stars, last pushed 3mo ago), licensed MIT. It adds 32 tokens to every session and 591 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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