About store-listing-optimization
store-listing-optimization is a skill for improving product pages in stores before installation or wishlisting. It reviews messaging, assets, trust, policy fit, localization, and experiments, then prioritizes changes and measurement.
Key features
- 01Channel and audience analysis
- 02Metadata and asset review
- 03Trust and policy-fit review
- 04Localization assessment
- 05Prioritized fixes and experiment planning
Use cases
- 01Reviewing a product listing before submission
- 02Improving clarity before installation
- 03Planning listing experiments
- 04Checking localization and channel constraints
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/store-listing-optimizationin Claude Code to confirm it loaded.
Bundled filesagents/openai.yamlreferences/store-listing-optimization-patterns.md
Details
From the project READMEStore Listing Optimization
Use this skill to make store pages communicate product value before users install or wishlist.
Workflow
- Identify channel, audience, product type, conversion goal, and current performance.
- Read
references/store-listing-optimization-patterns.md. - Review metadata, visual assets, proof, trust, policy fit, localization, and experiments.
- Separate channel-specific constraints from universal conversion problems.
- Produce prioritized listing changes and measurement plan.
When not to use
- Do not use for generic advice the base model already handles without this skill's specific artifact contract.
Guardrails
- Do not overclaim features, privacy, rankings, or compatibility.
- Do not optimize keywords at the expense of user understanding.
- Verify channel policy and asset specs before submission.
Output format
Channel:
Conversion goal:
Listing fixes:
- <rule id> <asset/field> -> <change>
Experiment plan:
- Hypothesis: <hypothesis>
Asset: <title/screenshot/video/etc>
Metric: <metric>
Policy/trust risks:
- <risk>
Comments
Markdown supported