commitment-ladder-design
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.
- License 许可
- MIT
- 最近更新
- 2026-11-11
- Stars
- ★ 1.6k
- Forks
- 468
01 前言 Front Matter
- name
- commitment-ladder-design
- license
- MIT
- metadata
- { "tags": "influence,persuasion,commitment,onboarding", "version": "0.1.0", "category": "Marketing", "displayName": "Commitment Ladder Design" }
- description
- 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.
02 资源文件 9
- 参考文档agents/openai.yaml192 B
- 参考文档evals/evals.json852 B
- 参考文档guidelines.md1.7 KB
- 参考文档references/commitment/checklist.md907 B
- 参考文档references/commitment/examples.md1.0 KB
- 参考文档references/commitment/knowledge.md2.8 KB
- 参考文档references/commitment/rules.md2.1 KB
- 参考文档references/commitment/smells.md1.6 KB
- 参考文档workflows/build-commitment-ladder.md3.0 KB
03 SKILL.md 正文
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
- Read
guidelines.mdto choose the smallest useful reference set. - Load
references/commitment/knowledge.mdfor concepts andreferences/commitment/rules.mdfor operating rules. - Use
workflows/build-commitment-ladder.mdfor repeatable tasks. - 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
- Diagnosis - name the influence principle or cue.
- Evidence Check - state what proof supports or is missing from the cue.
- Risk - explain manipulation, trust, or decision-quality risk.
- 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.
04 同仓库更多 Skills
deja-search
deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.
deja-reflect
End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.
revid
Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.
review-study
Review a QuestionPunk survey before launch for wording, answer choices, branching, and missing settings. Use for check my survey or review this draft before publishing.
manage-study
Find QuestionPunk studies, check status, publish an explicitly requested draft, or retrieve its participant link. Use for find my survey, publish this study, or how many people responded.
create-study
Create a saved QuestionPunk survey draft from a research brief or edit specified questions. Use for requests such as make a customer feedback survey or shorten my onboarding study.