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.
npx agentmods add agents/jamkris/everything-gemini-code/mutual-mappergit clone --depth 1 https://github.com/Jamkris/everything-gemini-codeWrote 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.
[](https://agentmods.dev/agents/jamkris/everything-gemini-code/mutual-mapper)<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>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.
| Model | Per session | Once 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 |
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- mutual-mapper — 100% identical, 0 lines differ
- mutual-mapper — 100% identical, 0 lines differ
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
- Pull the user's X following list (via X API)
- For each prospect, check if any of the user's followings also follow or are followed by the prospect
- For each mutual found, assess the strength of the connection
- 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:
- Direct mutual (warmest) — Both user and target follow this person
- Portfolio/advisory — Mutual invested in or advises target's company
- Co-worker/alumni — Shared employer or educational institution
- Event overlap — Both attended same conference, accelerator, or program
- 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)
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.
- 2d ago First seen · 76 lines · 32 tokens per session scan A fb741c0f2ee3
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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