/generate-personas
Create synthetic user personas for the current product, following the synthetic-personas skill.
Input: $ARGUMENTS
Workflow
Load product context. Read product/overview.md if it exists; otherwise look for a README or similar product description. If no product context exists, suggest /start-product first (it also captures the unverified beliefs later stages test); if the user prefers to continue here, ask for the product name, a short description, and target users — then offer to save that as product/overview.md.
Read existing personas in product/personas/ to avoid duplicates and to fill diversity gaps (the new set should complement, not repeat — including the type mix: note which types the existing set already covers). An existing persona without a type: line was created before the field existed: propose a type for it from its content and offer to write it in — with the user's approval, never silently.
Determine count, mix, and focus. If the arguments name a mix ("2 primary, 1 tertiary, 1 negative"), honor it to the letter — that count, those types. If they don't, propose a mix and get it confirmed before generating: derive it from the mode: line of the overview and the segment — a default of 4 (2 primary, 1 secondary or tertiary, 1 negative), with a tertiary whenever someone other than the user decides, pays, or approves (internal products: the sponsor; B2B: the buyer or admin). Show the proposal in one line ("2 primary, 1 tertiary, 1 negative — ok?") and wait for the answer. Never generate a set without at least one primary and one negative; if the requested mix lacks one, say so in one line and follow the request anyway. If a segment or focus was named, honor it.
Generate the personas per the synthetic-personas skill: archetype principles, full structure with the type: field, diversity requirements, in the language of the product context. A negative persona is a scope boundary, not a caricature: someone who looks like a user and is left out on purpose.
Present the set briefly — name, role, type, one-line perspective each — and ask for the review with two explicit questions per persona: does this persona belong to the product? Is the type the right one? A type change is applied on the spot. A discard is recorded in product/corrections.md with its reason (dated entry — artifact, what the AI proposed, what the human decided, why; create the file on first use). Discard for not belonging to the product, never for taste or because "it looks like a stereotype"; if every persona belongs, none is discarded.
Save each persona to product/personas/{name-slug}.md, with the type written literally as - **Type:** {value} right after the role line (label and value in English, whatever the language of the rest).
Close by suggesting next steps in one line: interview a persona (/interview-persona) or critique a spec with them (/critique-spec).
Language
Conversation and the saved personas in the language of the conversation. The type: values (primary, secondary, tertiary, negative) are fixed English tokens in every language, like source: values.
1---2name: generate-personas3description: Generate a diverse set of synthetic user personas for your product — typed primary/secondary/tertiary/negative, mix on request — saved as markdown files ready for interviews and critiques4---56# /generate-personas78Create synthetic user personas for the current product, following the `synthetic-personas` skill.910Input: $ARGUMENTS1112## Workflow13141. **Load product context.** Read `product/overview.md` if it exists; otherwise look for a README or similar product description. If no product context exists, suggest `/start-product` first (it also captures the unverified beliefs later stages test); if the user prefers to continue here, ask for the product name, a short description, and target users — then offer to save that as `product/overview.md`.15162. **Read existing personas** in `product/personas/` to avoid duplicates and to fill diversity gaps (the new set should complement, not repeat — including the type mix: note which types the existing set already covers). An existing persona without a `type:` line was created before the field existed: propose a type for it from its content and offer to write it in — with the user's approval, never silently.17183. **Determine count, mix, and focus.** If the arguments name a mix ("2 primary, 1 tertiary, 1 negative"), honor it to the letter — that count, those types. If they don't, **propose** a mix and get it confirmed before generating: derive it from the `mode:` line of the overview and the segment — a default of 4 (2 `primary`, 1 `secondary` or `tertiary`, 1 `negative`), with a `tertiary` whenever someone other than the user decides, pays, or approves (internal products: the sponsor; B2B: the buyer or admin). Show the proposal in one line ("2 primary, 1 tertiary, 1 negative — ok?") and wait for the answer. Never generate a set without at least one `primary` and one `negative`; if the requested mix lacks one, say so in one line and follow the request anyway. If a segment or focus was named, honor it.19204. **Generate the personas** per the `synthetic-personas` skill: archetype principles, full structure with the `type:` field, diversity requirements, in the language of the product context. A `negative` persona is a scope boundary, not a caricature: someone who looks like a user and is left out on purpose.21225. **Present the set** briefly — name, role, **type**, one-line perspective each — and ask for the review with two explicit questions per persona: does this persona belong to the product? Is the type the right one? A type change is applied on the spot. A discard is recorded in `product/corrections.md` with its reason (dated entry — artifact, what the AI proposed, what the human decided, why; create the file on first use). **Discard for not belonging to the product, never for taste or because "it looks like a stereotype"; if every persona belongs, none is discarded.**23246. **Save** each persona to `product/personas/{name-slug}.md`, with the type written literally as `- **Type:** {value}` right after the role line (label and value in English, whatever the language of the rest).2526Close by suggesting next steps in one line: interview a persona (`/interview-persona`) or critique a spec with them (`/critique-spec`).2728## Language2930Conversation and the saved personas in the language of the conversation. The `type:` values (`primary`, `secondary`, `tertiary`, `negative`) are fixed English tokens in every language, like `source:` values.
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