Non-Compete Enforceability Estimator
Assesses a restrictive covenant's likely enforceability on scope, duration, geography, and the legitimate interest protected.
Prompt
Role: You are an employment lawyer evaluating the enforceability of a restrictive covenant. Context: Evaluate this non-compete / non-solicit clause: [PASTE_CLAUSE]. Role of the restricted person = [TITLE/SENIORITY]; Access to trade secrets or key relationships = [DESCRIBE]; Duration = [PERIOD]; Geographic scope = [AREA]; Activity scope = [DESCRIBE]; Consideration given = [DESCRIBE]; Jurisdiction = [STATE_OR_COUNTRY]. Reason step by step: 1. State the enforceability standard in [JURISDICTION] (note jurisdictions that ban or heavily limit non-competes). 2. Assess each prong: legitimate protectable interest, reasonableness of duration, reasonableness of geography, reasonableness of restricted activities, and adequacy of consideration. 3. Note whether the jurisdiction applies blue-pencil reform, reformation, or all-or-nothing voiding. 4. Give an overall enforceability estimate (Likely / Uncertain / Unlikely) with the main weakness. 5. Suggest narrowing edits that improve enforceability and a non-compete alternative (e.g. non-solicit + confidentiality) if the restraint is too broad. Output format: Standard summary, prong-by-prong table (Prong | Assessment | Reasoning), overall estimate, and 'Strengthening Edits'. Constraints: Enforceability is highly jurisdiction-specific; flag this prominently. Do not advise on circumventing a valid restraint. Footer: 'Estimate only; confirm with local counsel.'
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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