Trust and Liking

SkillCommunication

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.

Use Trust and Liking in Claude, ChatGPT or Ahel Desktop

Free. Sign in, add Trust and Liking and connect your AI. About a minute.

Also: Claude Code · Cursor · Codex

Then ask your AI: use the Trust and Liking skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Trust and LikingStart free

What this skill tells your AI

The instructions your AI receives, as published by hashgraph-online/awesome-codex-plugins in plugins/LVTD-LLC/skills/skills/trust-and-liking/SKILL.md and read by Ahel’s review.

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

FilePurpose
references/liking/knowledge.mdCore concepts and source-grounded definitions
references/liking/rules.mdRules, boundaries, and practical guidelines
references/liking/examples.mdBad/better examples for applied situations
references/liking/smells.mdRed flags and anti-patterns to detect
references/liking/checklist.mdFast review checklist
workflows/build-rapport-without-flattery.mdImprove 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.

Signals

GitHub stars
1k
Forks
316
Last commit
Oct 2026
Advanced
Item type
skill
Key
trust-and-liking
Source
github.com/hashgraph-online/awesome-codex-plugins