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Trust And Liking

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Use when improving cold outreach, sales conversations, customer discovery, community moderation, referral asks, partnership messages, interviews, or relationship-based persuasion where similarity, compliments, familiarity, cooperation, association, or rapport may affect trust.

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  • Added September 12, 2026
ai-agentsrustgogit

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Scanned September 12, 2026

npx -y skills add thedixitjain/the-mega-skill-library --skill trust-and-liking --agent claude-code

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SKILL.md
---
name: trust-and-liking
description: "Use when improving cold outreach, sales conversations, customer discovery, community moderation, referral asks, partnership messages, interviews, or relationship-based persuasion where similarity, compliments, familiarity, cooperation, association, or rapport may affect trust."
category: marketing-and-growth
source_repo: hashgraph-online/awesome-codex-plugins
source_path: "plugins/LVTD-LLC/skills/skills/trust-and-liking/SKILL.md"
source_url: https://github.com/hashgraph-online/awesome-codex-plugins/blob/HEAD/plugins/LVTD-LLC/skills/skills/trust-and-liking/SKILL.md
---


# Trust and Liking

Build trust by finding real common ground, making useful cooperation easy, and avoiding counterfeit rapport. Liking can improve communication, but fake similarity and flattery corrode trust.

## Quick Start

1. Read `guidelines.md` to choose the smallest useful reference set.
2. Load `references/liking/knowledge.md` for concepts and `references/liking/rules.md` for operating rules.
3. Use `workflows/build-rapport-without-flattery.md` for repeatable tasks.
4. For audits, surface both the active influence cue and the ethical rewrite.

## Contents

| File | Purpose |
| --- | --- |
| references/liking/knowledge.md | Core concepts and source-grounded definitions |
| references/liking/rules.md | Rules, boundaries, and practical guidelines |
| references/liking/examples.md | Bad/better examples for applied situations |
| references/liking/smells.md | Red flags and anti-patterns to detect |
| references/liking/checklist.md | Fast review checklist |
| workflows/build-rapport-without-flattery.md | Improve relationship-based persuasion while avoiding fake similarity and manipulative praise. |

## Operating Principles

- Use only honest evidence. Do not invent popularity, scarcity, credentials, endorsements, or social connection.
- Separate helping a good decision from pushing a shortcut response. If the cue is counterfeit, treat it as a red flag.
- When rewriting, preserve user agency: add context, alternatives, and enough time to decide when stakes are meaningful.

## Output Pattern

1. **Diagnosis** - name the influence principle or cue.
2. **Evidence Check** - state what proof supports or is missing from the cue.
3. **Risk** - explain manipulation, trust, or decision-quality risk.
4. **Rewrite or Recommendation** - provide an ethical alternative.

## Validation

Use the prompts in `evals/evals.json` as smoke tests. A good result identifies the relevant Influence principle, preserves user agency, and avoids fabricated evidence or coercive pressure.

---

**Source:** [`hashgraph-online/awesome-codex-plugins`](https://github.com/hashgraph-online/awesome-codex-plugins) → `plugins/LVTD-LLC/skills/skills/trust-and-liking/SKILL.md`

Files in this skill

  • SKILL.md2.7 KB
  • agents/openai.yaml177 B
  • evals/evals.json788 B
  • guidelines.md1.7 KB
  • references/liking/checklist.md903 B
  • references/liking/examples.md1.1 KB
  • references/liking/knowledge.md2.5 KB
  • references/liking/rules.md2 KB
  • references/liking/smells.md1.6 KB
  • workflows/build-rapport-without-flattery.md3 KB

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