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commitment-ladder-design

Use when designing or auditing onboarding, activation flows, habit loops, pledges, public commitments, progressive profiling, retention programs, be…

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Commitment Ladder Design

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

| File | Purpose |

| --- | --- |

| references/commitment/knowledge.md | Core concepts and source-grounded definitions |

| references/commitment/rules.md | Rules, boundaries, and practical guidelines |

| references/commitment/examples.md | Bad/better examples for applied situations |

| references/commitment/smells.md | Red flags and anti-patterns to detect |

| references/commitment/checklist.md | Fast review checklist |

| workflows/build-commitment-ladder.md | Create 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.

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