Commitment Ladder Design

SkillMedia

Use when designing or auditing onboarding, activation flows, habit loops, pledges, public commitments, progressive profiling, retention programs, behavior-change plans, pricing flows, or lowball-risk funnels where consistency pressure may shape user decisions.

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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.

Commitment Ladder DesignStart 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/commitment-ladder-design/SKILL.md and read by ahel’s review.

Design commitments that help users act on real intent, not commitments that trap them into defending a decision. The consistency drive is useful when it supports identity-aligned action and risky when it hides changing terms.

Quick Start

  1. Read guidelines.md to choose the smallest useful reference set.
  2. Load references/commitment/knowledge.md for concepts and references/commitment/rules.md for operating rules.
  3. Use workflows/build-commitment-ladder.md for repeatable tasks.
  4. For audits, surface both the active influence cue and the ethical rewrite.

Contents

FilePurpose
references/commitment/knowledge.mdCore concepts and source-grounded definitions
references/commitment/rules.mdRules, boundaries, and practical guidelines
references/commitment/examples.mdBad/better examples for applied situations
references/commitment/smells.mdRed flags and anti-patterns to detect
references/commitment/checklist.mdFast review checklist
workflows/build-commitment-ladder.mdCreate a sequence of small, authentic commitments that lead toward a meaningful user goal.

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
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Forks
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Last commit
Oct 2026
Advanced
Item type
skill
Key
commitment-ladder-design
Source
github.com/hashgraph-online/awesome-codex-plugins