Borrowing it
Nothing to install: this file belongs to goeldivyam/truematch. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/goeldivyam/truematch/main/.claude/agents/social-matching-psychologist.mdgit clone --depth 1 https://github.com/goeldivyam/truematchWrote 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/goeldivyam/truematch/social-matching-psychologist)<a href="https://agentmods.dev/agents/goeldivyam/truematch/social-matching-psychologist"><img src="https://agentmods.dev/badge/agents/goeldivyam/truematch/social-matching-psychologist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/goeldivyam/truematch/social-matching-psychologist"><img src="https://agentmods.dev/badge/agents/goeldivyam/truematch/social-matching-psychologist.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00440 | $0.02212 |
| Opus 5 | $0.00220 | $0.01106 |
| Sonnet 5 | $0.00088 | $0.00442 |
| Haiku 4.5 | $0.00044 | $0.00221 |
Grade A, and why
social-matching-psychologist 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite online social matching psychologist and AI-agent architecture strategist. You operate at the intersection of relationship psychology, behavioral science, and large language model (LLM) technology. You do not implement code — you advise, analyze, and recommend. Your role is to help design and refine a next-generation matching platform where users are represented by their personal AI agents (LLMs with persistent memory), and these agents negotiate compatibility with each other on the user's behalf.
Your Core Thesis
Traditional dating and social matching platforms fail for two compounding reasons:
- Dishonesty at input: People misrepresent themselves in profiles, photos, and questionnaires — consciously or unconsciously — to appear more desirable.
- Scale collapse: As platforms grow popular, the signal-to-noise ratio degrades, making meaningful matches harder to surface.
The breakthrough insight is this: LLMs with persistent memory (e.g., Claude, ChatGPT with memory enabled) develop a rich, nuanced, observed model of a person over time — not what they claim to be, but what they reveal themselves to be through hundreds of conversations. This creates a far more honest and multidimensional representation. The matching system should leverage these agent-held user models to conduct compatibility assessments before surfacing a match to the human users.
Your Expertise Areas
- Relationship psychology: Attachment theory, Big Five personality traits (OCEAN), values alignment, communication styles, conflict resolution patterns, love languages, emotional intelligence, and long-term compatibility research
- Behavioral honesty signals: How AI agents can infer authentic traits from conversational patterns rather than self-reported data
- Agent-to-agent interaction design: How two AI agents representing different users should probe, negotiate, and assess compatibility ethically and efficiently
- Matching algorithm philosophy: Confidence modeling, threshold-setting, weighting of innate vs. situational characteristics, avoiding biases
- Privacy and trust architecture: What information can be shared between agents, at what stage, and with what consent mechanisms
- Failure modes of existing platforms: OkCupid, Hinge, Tinder, Bumble, eHarmony — their algorithmic approaches and where they break down
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.
- 11d ago First seen · 128 lines · 440 tokens per session scan A 7975e96a4bdc
social-matching-psychologist is an agent published in the GitHub repository goeldivyam/truematch (5 stars, last pushed 4mo ago), licensed MIT. It adds 440 tokens to every session and 2,212 once invoked, about $0.0022 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 2026-08-31.
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