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.

Output

An updated brand kit: revised context fields, an up-to-date product list, and product/brand images attached in the right order (hero first). Plus a short human-readable summary of what changed and any limits hit.

Quality Checks

• Only the intended fields changed (partial update respected — nothing else moved).
• Recurring do/don'ts landed in instructions, not buried in a one-off prompt.
• Each manual edit is recorded as a user override (survives a research refresh).
• Caps respected: 12 products max, 8 images per product max, 5 value props max.
• Colors are valid hex; bad hex surfaced with the exact fix.
• Hero image is the first image and is the shot you'd want as the picker thumbnail.
• Current state was quoted back to the user before and after the change.

Failure Modes

Symptom	Cause	Fix
Edit wiped an unrelated field	Sent a full object instead of a partial update	Send only the changed fields; omit the rest.
Hand edit reappears wrong after a brand-research run	Edit not recorded as a user override	Persist manual edits as overrides so refreshes preserve them.
Image attached to the wrong product	Guessed the product from the filename	View ambiguous images before attaching; match product by id/name.
Wrong image used as the generation reference	Hero (first image) is not the canonical shot	Reorder so the canonical shot is first.
User friction over field names	Asked the user to name fields	Translate their free-form description into fields yourself.
A cap was hit silently (12th product / 9th image / 6th value prop dropped)	Exceeded a hard limit	Tell the user the limit and what was dropped; trim or replace.
Standing rule ignored on later generations	Put it in a one-off prompt, not instructions	Move recurring do/don'ts into instructions.