---
name: hashgraph-online/commitment-ladder-design
source: https://app.decimal.ai/s/hashgraph-online-commitment-ladder-design@1/SKILL.md
source_sha256: a65c5ed4dba7
---

# 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.