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Growth ethics review

Skill duandigi/duandigi-growth-marketing-skill/skills/growth-ethics-review

Use this skill when reviewing a growth idea, experiment, automation, message, incentive, tracking plan, or loop for manipulation, spam, privacy, accessibility, discrimination, brand, legal, or platform-policy risk.From its SKILL.md

Install
npx -y skills add duandigi/duandigi-growth-marketing-skill --skill growth-ethics-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.8 KB, 463 tokens by cl100k_base, as published. Nobody here has run it

Growth Ethics Review

Purpose

Protect durable growth by rejecting tactics that create hidden harm or unmanageable risk.

Inputs

  • Proposed tactic or experiment
  • Target audience and channel
  • Data collected and permissions
  • Incentives, messaging, automation, and expected behavior

If critical input is unavailable, label it unknown and create a research or instrumentation task. Do not invent values.

Workflow

  1. Identify affected users, non-users, employees, partners, platforms, and communities.
  2. Check informed choice, truthfulness, reversibility, privacy, accessibility, fairness, and data minimization.
  3. Screen for spam, fake social proof, dark patterns, deceptive urgency, impersonation, policy evasion, and exploitative targeting.
  4. Assess likely harm severity, likelihood, detectability, and reversibility.
  5. Classify the proposal as approve, approve with controls, redesign, or reject.
  6. Specify controls, disclosures, consent, rate limits, monitoring, and escalation paths.
  7. Document the decision and unresolved legal or platform questions.

Required output

Return a concise, decision-oriented response containing:

  • Risk classification
  • Affected parties
  • Risk findings
  • Required controls
  • Decision
  • Open questions

Label important statements as confirmed, inferred, assumed, or unknown when the distinction affects the decision.

Guardrails

Do not:

  • Treating legal compliance as the only ethical standard

  • Using consent buried in unclear language

  • Optimizing short-term conversion at the expense of user autonomy

  • Claim guaranteed growth or present an estimate as observed fact.

  • Recommend spam, fake reviews, impersonation, deceptive urgency, dark patterns, policy evasion, or unauthorized production changes.

  • Hide material uncertainty, tracking limitations, or possible harm.

When an action can spend money, publish content, contact people, change production systems, delete data, or alter access, produce a plan and request explicit authorization rather than executing automatically.

Completion check

Before finishing, verify that the output:

  • answers a specific growth decision;
  • uses the supplied business context;
  • separates evidence from assumptions;
  • defines a measurable next step;
  • includes risks, constraints, and missing data;
  • is no longer than necessary for the decision.

What ships with it: 1 file

1.3 KB alongside SKILL.md

evals/

Gives 0 of the 12 instructions most review quality skills give in 463 tokens

Counted across 1,048 of the 1,783 authors here whose files we hold, read 2026-08-07

  • Ask questions one at a timein 81 of 1048, across 64 files
  • Provide a recommended answer for each questionin 73 of 1048, across 50 files
  • Explore the codebase instead of asking answerable questionsin 66 of 1048, across 42 files
  • Resolve dependencies between decisions one-by-onein 42 of 1048, across 17 files
  • Interview the user relentlessly about the planin 38 of 1048, across 13 files
  • Order findings by severityin 31 of 1048
  • Resolve each branch of the decision treein 27 of 1048, across 5 files
  • Run a grilling sessionin 26 of 1048, across 5 files
  • Update CONTEXT.md immediately when a term is resolvedin 26 of 1048, across 11 files
  • Propose precise canonical terms for vague languagein 25 of 1048, across 7 files
  • Create documentation files lazilyin 24 of 1048, across 5 files
  • Assign severity to every findingin 24 of 1048

Said here and by no other author read

  • identify affected parties
  • check for ethics and privacy risks
  • screen for manipulative tactics
  • assess harm severity and likelihood
  • classify the proposal
  • specify required controls

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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