Breaks down a competitor page by operating dimension
The instruction separates positioning, pricing, conversion tactics, user experience, and search content instead of returning one undifferentiated summary.
Competitive Teardown is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to you are still deciding which…
You are still deciding which product, market, or direction deserves investment. Bring Benchmark your Shopify store's feature set against the top 3 competitors and Assess a new market entrant's threat level before responding strategically into the same operating path before… Start with a small test around “Benchmark your Shopify store's feature set against the top 3 competitors”, then check whether “Assess a new market entrant's threat level before responding strategically” fits the way your team actually works.
The instruction separates positioning, pricing, conversion tactics, user experience, and search content instead of returning one undifferentiated summary.
The source asks the model to group, classify, answer, or prioritize review feedback rather than treating every comment as an isolated case.
The source covers product or listing copy alongside SEO-related fields such as keywords, metadata, or image text when those fields are requested.
The complete source is shown below. Copy it from the top right to use it.
You are a competitive intelligence analyst. Follow the teardown workflow: 1) Define 2-4 competitors with a primary focus. 2) Collect data from at least 3 sources per competitor: pricing pages, app store reviews (prioritize 1-3 star), job postings, SEO, social media. 3) Score each competitor across 12 dimensions (1-5 scale) with evidence for every score. 4) Generate: Feature Comparison Matrix, Pricing Analysis, SWOT with data-anchored bullets, Positioning Map (2x2), UX Audit (TTFV, friction, mobile). 5) Build Action Items: quick wins (0-4 wks), medium (1-3 mo), strategic (3-12 mo). 6) Package into 7-slide stakeholder presentation. Every SWOT bullet must link to a data signal.Starter prompts for the main use cases—copy and use them directly.
Do not begin with a store-wide rollout. Pick one reversible task where Competitive Teardown can help you research demand, competitors, customer needs, and product opportunities. Use this when the input boundary, owner, and one primary measure from source coverage, estimate error, margin viability, and decision confidence are written down.
Use the Skill above to help me with this task: Start with one real task.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not begin with a store-wide rollout. Pick one reversible task where Competitive Teardown can help you research demand, competitors, customer needs, and product opportunities.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]Collect only the a precise research question, market and date boundaries, product economics, and rejection criteria needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.
Use the Skill above to help me with this task: Prepare the input and guardrails.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Collect only the a precise research question, market and date boundaries, product economics, and rejection criteria needed for this test. Remove unrelated personal data and state which actions must never run automatically.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]Read the source, installation method, and permission notes before adding Competitive Teardown to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a market or product decision brief that a responsible operator can inspect, and it stayed inside the approved boundary.
Use the Skill above to help me with this task: Inspect the source Skill, then run it.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Read the source, installation method, and permission notes before adding Competitive Teardown to a separate test project. Keep commands and Skill text exactly as published.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when source coverage, estimate error, margin viability, and decision confidence has a pre-test baseline, and errors and exceptions are logged separately.
Use the Skill above to help me with this task: Review it against a baseline.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]When this instruction drafts marketing or product content, review specifications, comparisons, performance claims, and testimonials against approved evidence before publishing.
Any price, discount, cost, or margin recommendation is only as current as the values you provide. Recheck live prices, tax, shipping, and margin rules before publishing or sending an offer.
The instruction can group and prioritize feedback, but repeated wording is not proof of a product defect or customer-wide preference. Check the underlying sample before changing a product, policy, or campaign.
HealthTech CTO and open-source maintainer focused on applied AI, agentic coding, and practical skills for product, research, growth, and operations teams.
Review third-party permission scopes before providing store data. Never paste payment credentials, customer passwords, or unnecessary personal data into a model. Outputs must be checked by the operator responsible for the workflow.
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