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Compare legal document approaches
Uses comparable products' documents to identify research questions and clearer structure without copying their legal assumptions.
comparative-legal-document-research
See how it helpsWhen is it useful?
Use when improving terms, privacy notices or AI clauses for a product with known markets and architecture.
What does it help you do?
- Chooses comparable business models, customer groups and jurisdictions.
- Compares coverage of data, termination, subprocessors and AI commitments.
- Checks observed approaches against primary law and verified product behavior.
Try asking this
Compare three relevant SaaS privacy notices and identify useful structural ideas and questions our own product facts must answer. Present a comparison table with dated sources and unanswered product questions. Reused structure must not imply that another company's clauses fit our service.
Detailed documentation
Read the skill’s original instructions, examples and checks in their authored language.
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Comparative Legal Document Research
Use comparable companies' terms, privacy notices, DPAs, and AI addenda as structured benchmarks to spot industry patterns and edge cases—never as a substitute for primary legal sources or actual product inspection.
Source-Critical Comparative Methodology
- Select Genuinely Comparable Reference Companies:
- Choose 3–5 reference products that match the product across key dimensions:
- Business & delivery model (multi-tenant SaaS, usage-based AI platform, marketplace, e-commerce, mobile subscription app).
- Customer segment (B2C, SMB self-serve B2B, enterprise B2B, or hybrid).
- Target jurisdictions (EU/EEA, specific Member States, UK, US).
- Technical architecture (third-party LLM subprocessors, workspace collaboration, payment gateways).
- Choose 3–5 reference products that match the product across key dimensions:
- Analyze Structure, Coverage & UX Integration:
- Compare how reference documents organize:
- Layered privacy tables (purpose, data categories, legal basis, retention).
- B2B vs. B2C clause separation and consumer withdrawal disclosures.
- AI input/output governance, training opt-out commitments, and EU AI Act notices.
- Subprocessor lists, change-notification mechanics, and DPA annexes.
- Account termination, data export windows, and post-termination deletion timelines.
- Compare how reference documents organize:
- Subject Every Observed Pattern to Independent Verification:
- Treat every clause or pattern seen in a competitor's document strictly as a research hypothesis:
- Check against primary law: Does this pattern actually satisfy current statutory requirements and supervisory guidance, or is the competitor relying on outdated or non-compliant boilerplate?
- Check against our product: Does our codebase and operational workflow actually work this way?
- Treat every clause or pattern seen in a competitor's document strictly as a research hypothesis:
- Synthesize Original, Product-Tailored Drafting:
- Use comparative insights to improve clarity, table layouts, and issue coverage while writing 100% original text tailored to the verified facts of our system.
Guardrails
- Never cite another company's Terms or Privacy Policy as legal authority or proof that a practice is lawful.
- Never copy paragraphs, distinctive phrasing, or whole sections from third-party legal documents.
- Never adopt a competitor's operational promise (such as SOC 2 / ISO 27001 certification, 24-hour breach notice, or EU-only data residency) unless verified as true for our own system.
Included files 1
SKILL.md