Growth experiment prioritization
Skill duandigi/duandigi-growth-marketing-skill/skills/growth-experiment-prioritization
Evidence-first AI Agent Skills for growth marketing — multi-channel analytics (SEO, paid media, social, local search, CRM), secure account integration, AI evaluation, experimentation & approval-safe optimization for Claude Code.
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What its author says it does
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Use this skill when scoring, ranking, sequencing, or selecting growth experiments and research tasks across one or many projects using impact, confidence, strategic fit, reusability, revenue relevance, effort, risk, and dependencies.
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
2.9 KB, 473 tokens by cl100k_base, as published. Nobody here has run it
Growth Experiment Prioritization
Purpose
Create a transparent, comparable experiment queue without hiding judgment behind a score.
Inputs
- Candidate experiments
- Required score dimensions from 1 to 10
- Dependencies and deadlines
- Available capacity and strategic priorities
If critical input is unavailable, label it unknown and create a research or instrumentation task. Do not invent values.
Workflow
- Reject experiments that lack a measurable outcome, owner, or minimum evidence.
- Score each dimension using the rubric in references/scoring-rubric.md.
- Run scripts/score_experiments.py for consistent calculation when structured data is available.
- Apply dependency, capacity, legal, brand, and instrumentation gates after scoring.
- Balance quick learning, near-term value, and strategic capability building.
- Publish both the numeric ranking and the rationale; allow an explicit strategic override with a reason.
- Limit active work to the team's realistic work-in-progress capacity.
Required output
Return a concise, decision-oriented response containing:
- Ranked backlog
- Dimension scores
- Score explanation
- Dependencies
- Selected now/next/later
- Override reasons
Label important statements as confirmed, inferred, assumed, or unknown when the distinction affects the decision.
Guardrails
Do not:
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Treating the score as objective truth
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Using made-up precision
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Ranking unsafe or unmeasurable experiments
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Claim guaranteed growth or present an estimate as observed fact.
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Recommend spam, fake reviews, impersonation, deceptive urgency, dark patterns, policy evasion, or unauthorized production changes.
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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.
Available script
scripts/score_experiments.py— deterministic scoring utility bundled with this skill. It accepts JSON or CSV and returns structured JSON.
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.