Update Brand Kit

Update Brand Kit

Purpose

Keep a brand kit correct and rich by talking to the user. A brand kit is the canonical context an ad/content-generation pipeline reads, so what's in it directly shapes every downstream generation. Reach for this whenever the user wants to set or refine brand positioning/voice/audience, add standing do/don't guidance, manage the product list, or attach product photos.

This skill is the portable domain model — the fields, the semantics, the rules. How you actually persist a change is the host's concern: a brand-kit API, a CLI, or a brand-kit document in a workspace. The field model, semantics, and caps below hold regardless of which backend stores the kit.

Inputs

• A natural-language request — free-form ("make the voice warmer and more technical", "we sell to ops teams not consumers", "add our Pro plan at $49"). You translate this into the field model below; do not ask the user to name fields.
• Optionally, a brand identifier or name — when the user owns more than one brand, you need to know which one. If they only have one, use it.
• Optionally, a folder of product photos to attach to a product.

The brand-kit field model

These are the context fields. Treat each edit as a partial update (see semantics): set only what's changing.

• description — what the brand does, in 1–2 sentences.
• audience — target audience / ICP.
• voice — tone of the copy.
• instructions — standing guidance applied to every generation (e.g. "always show the product in use", "never use red"). High-leverage — set this whenever the user describes a recurring do/don't, not a one-off.
• brand_type — one of: product, saas, service, agency, restaurant, fashion, beauty, fitness, finance, education, health.
• value_props — short selling points; the kit keeps the first 5.
• primary_color / accent_color — hex like #1a2b3c (validate the format).

Products (a list on the kit) carry: name (required), description, link, pricing (free text, e.g. "$49"), offers (e.g. "20% off launch week"), notes (markdown), and an ordered list of images.

Semantics (the rules that make edits safe)

• Partial update. Pass only the fields you want to change; omitted fields are left untouched. Editing voice never disturbs audience.
• Clear with empty. To clear a field, set it to empty/null — distinct from omitting it.
• Idempotent. Re-applying the same values is safe (no duplication). Images dedupe per product.
• User overrides win. Treat every manual edit as a user override so a later automated brand-research re-run preserves it instead of overwriting. This is the whole reason hand edits are durable — don't let a refresh clobber intentional human choices.
• Product matching. Match an existing product by a stable id when you have one; otherwise by case-insensitive name. Same logic for delete.
• Images live on the product, not on an edit — they survive a product rename/edit. The first image is the hero: the picker thumbnail and the generation reference. Order matters; put the canonical shot first.

Workflow

1. Identify the brand. If the user has multiple brands, confirm which one by name; if one, use it. Read its current kit and quote back what's already set so the user sees exactly what you're about to change.

2. Translate intent into fields. Map the free-form request onto the field model above. If something is a recurring rule, it belongs in instructions, not in a one-off generation prompt.

3. Apply context edits as a partial update. Confirm each write by reflecting the new value back.

4. Manage products (create/edit/delete) before attaching any image — the product must exist first.

5. Attach product photos. For each image: if the filename is ambiguous about which product it depicts, view it rather than guessing; rename to a semantic name if helpful. Then upload/host it and attach it to the right product (omit the product to make it a brand-level reference image — general imagery not tied to one product). Remember the first image is the hero.

6. Confirm. Summarize what changed — fields updated, products added/edited, images attached and to which product — and surface any cap or validation issue with the exact limit and the fix.