GooseWorks ads skill — create, edit, AND analyze ad creative. Turn an approved source ad into a branded ad for the user's product, edit/re-roll an existing creative, research a brand for ads, OR analyze ad performance (Meta/Google campaign diagnostics, creative fatigue, CAC & lead quality, competitor ad intelligence, ad angles & hooks). Use when the user says "remix this ad", references a static ad template id/slug, asks to "make an ad", "edit this ad", "research my brand", or asks to analyze/diagnose ad campaigns. Generation runs through the GooseWorks backend's single cloud workflow (the same one the ads app uses) — credits are reserved and billed server-side. Analytics recipes are fetched from goose-skills on demand.
GooseWorks ads skill — create, edit, AND analyze ad creative. Turn an approved source ad into a branded ad for the user's product, edit/re-roll an existing creative, research a brand for ads, OR analyze ad performance (Meta/Google campaign diagnostics, creative fatigue, CAC & lead quality, competitor ad intelligence, ad angles & hooks). Use when the user says "remix this ad", references a static ad template id/slug, asks to "make an ad", "edit this ad", "research my brand", or asks to analyze/diagnose ad campaigns. Generation runs through the GooseWorks backend's single cloud workflow (the same one the ads app uses) — credits are reserved and billed server-side. Analytics recipes are fetched from goose-skills on demand.
Create / edit ad creative — a thin wrapper over the backend's single generation
workflow. You pick the brand + approved source ad(s) and submit ONE batch; the backend runs the
whole pipeline (compose → generate → persist → judge), reserves and bills credits, and
stores the renders. You do NOT generate images, call FAL, manage render rows, or upload
files — those are gone. This is the exact same workflow the GooseWorks ads app uses, so the
skill and the app can never drift.
Analyze ad performance — fetch ad-analytics recipes from goose-skills on demand
(these are unrelated to generation; see "Analyze / intelligence" below).
Prerequisite — the GooseWorks MCP server is REQUIRED
Everything goes through the mcp__gooseworks__* tools. If they are not available, stop and
tell the user to run gooseworks install --claude --mcp (and restart Claude Code). There is
no HTTP/file fallback — the REST ad endpoints are session-cookie-only and reject your token.
Start from the brand context — don't re-ask what it already answers
If the gooseworks router handed you brand context, USE IT. If you were invoked directly, call
brand_get_context first (falling back to get_brand_kit for the selected brand). It already
answers most of what the flows below would otherwise ask the user:
Which product to feature → products[]. Offer the real catalog entries; never guess a
product name and never ask the user to list their products.
The vibe / tone of the copy → the brand's voice. Use it; don't ask "what tone?".
Who the ad is for → the brand's audience. Don't ask "who's the target?".
The angle, offer framing, and what to claim → positioning, value props, proof points.
Logo, colors, fonts → owned by the backend research pass. Never re-derive them.
Whether the facts are trustworthy yet → research status. If it isn't complete, say so in
one line and continue; the batch queues and runs when research finishes.
Ask only for what the context genuinely doesn't answer: the specific campaign intent (season,
promo, which of several angles), the source ad, and anything the user must consent to.
Identity & credits
One agent-scoped token authenticates the gooseworks MCP tools. Never print it. The tools
resolve your org automatically — you do NOT resolve an "Ads agent" or pass target for the
generation tools.
Credits are handled entirely by the backend.submit_remix_batch reserves the estimated
cost up front (it errors with insufficient_credits if the wallet is short — relay the
message and stop) and bills only the images that actually complete. Call
estimate_remix_batch first to tell the user the cost; gooseworks credits shows balance.
Live MCP contract — inspect it before asking
The currently registered MCP tool schemas are the source of truth for inputs, supported choices,
and defaults. Do not copy an exhaustive input list from this skill or rely on remembered fields.
Before each tool call:
Inspect the live schema for the tool you are about to use.
Fill required inputs already known from the Brand Kit, selected source, or conversation.
Ask the user only for required inputs that cannot be inferred and for choices that materially
change the result. Do not turn every optional field into a questionnaire.
Omit unspecified optional settings so the backend applies its current app defaults.
If the live schema conflicts with this workflow, follow the live schema and report the drift
with log_cli_event.
The generation tools (the new, single-workflow surface)
submit_remix_batch — the one call that makes ads. Inspect its live schema and supply
the required brand/source inputs plus any choices the user explicitly made.
Returns the batch with a links block (brand_url + per-creative app_url). If the brand's
research isn't finished yet the batch comes back status: "queued" — it auto-runs the moment
research completes; tell the user it'll appear shortly, don't error.
estimate_remix_batch — cost preview. Reserves nothing. Use it to quote the cost first and
check whether every selected source resolved before submitting.
get_remix_batch — poll status. Returns each creative with its renders and
completed/failed/pending counts, plus links. A creative is done when its pending is 0
— NOT when current_render_url is set (during a regenerate that field still points at the prior
image). Each render carries age_seconds (since queued) and elapsed_seconds (time generating):
use them to tell a slow-but-healthy render from a stuck one. A render only failed when its
status is "failed" — never assume a stall and re-submit, that double-bills.
list_brand_creatives — the brand's gallery feed (newest
first) + brand_url. Alternative poll target; also use to show everything made for a brand.
surprise_me_templates — the "Surprise me" recommender. Picks
remixable Community creations (SAME logic as the web /create "Surprise me" button), shuffled
so picks stay fresh. It does not use the retired curated third-party catalog.
Returns the picked templates (id, slug, title, image, ratio) AND a ready-to-open create_url
(the /create page with cli=true and the picks pre-selected). This is how you recommend
templates — do NOT hand-pick from the raw catalog yourself (see "Picking templates" below).
regenerate_creative — edit or re-roll one existing creative through the same pipeline.
Inspect the live schema to select the supported mode and required source inputs. Returns a
single-item batch; poll it with get_remix_batch.
set_creative_feedback — record the user's reaction to a generated image. Use it whenever
the user reacts; inspect the schema for the current rating and reason choices.
Plan mode — review the plan BEFORE generating (optional)
For users who want to approve each ad's plan before spending credits (the app's "Plan it" flow):
Use the approval option exposed by submit_remix_batch — it composes each creative's plan and PAUSES.
No credits are reserved and no image renders until you approve.
list_ad_approvals — poll this. While a creative is
composing, wait; once awaiting_approval, show its plan (composed prompt + refs + quality)
to the user.
revise_ad_plan — recompose from a chat steer, still
free. Poll list_ad_approvals until it's awaiting_approval again.
approve_ad_plan — approve one creative or the whole batch using the live schema.
This is the step that reserves credits and renders. Then poll
get_remix_batch and hand back links as usual.
Only offer plan mode when the user asks to review/approve first — the default path generates
immediately.
Reading the brand & picking inputs (still MCP, read-only)
get_brand_kit — read the canonical brand context and available products/assets.
list_ad_brands / get_ad_brand — find and fetch the active brand.
list_user_ad_templates — list the org's own uploads and
imported ads. Prefer relationship: "self" when the user wants to reuse their own ads;
relationship: "competitor" is research/inspiration, never proof that the user owns the ad.
search_ad_templates — search remixable Community generations. The
retired curated third-party catalog is not returned.
get_static_ad_template — resolve a source already owned by
the org, including an own upload or a snapshotted Community creative. It does not resolve the
retired curated third-party catalog.
remix_community_ad — turn a selected Community creative into a private remix source before
submitting it. A Community ad id is an ad_project id, not a
template id. Call this FIRST to snapshot it into a private template, then use the returned
template id in items.
create_user_ad_template — upload a source image as a private template. Answer any
ownership/rights input only from the user's explicit confirmation. Never claim rights for a
competitor ad or an image found online.
get_ad_project / append_project_message — inspect a creative / leave a note on its thread.
Keep the brand kit in sync — reconcile, then update (ASK first)
The brand kit is the source of truth every generation reads. During ANY task, when the user
tells you something about the brand or asks to change something brand-level — a different
tagline, audience, voice, a product's name/price/description, "our logo is X", "we don't sell Y
anymore", a new product photo — treat it as a possible kit update, don't just use it for this one
ad and forget it:
Check it against the kit. Call get_brand_kit for the active brand and see whether what the user said
matches, is missing from, or contradicts the kit.
If it's already in the kit and matches — nothing to do; proceed.
If it's new or different — ASK before writing. Confirm in one line: "Want me to update
the brand kit so this sticks for future ads?" Only persist on a yes (or when the user clearly
asked you to change the brand). Don't silently mutate the kit, and don't nag on trivia.
Persist with the write tools (partial — only the fields you pass are touched; each edit is
recorded as a user override that later re-research won't clobber):
add_brand_product_image / remove_brand_reference_image — product and reference photos.
Inspect each live schema and send only the fields needed for the confirmed change.
Confirm what changed and continue the task. (Logo, colors, and fonts are owned by the
backend research pass — prefer update_ad_brand / the research flow for those, not free text.)
This is the parity gap the app closes in-product: a brand fact the user gives mid-task should be
able to flow back into the kit — with their ok — instead of being lost.
Picking source ads — use approved sources, not the retired catalog
When the user wants to make ads but has NOT named a specific template (id/slug/Community
ad/upload), do NOT silently browse the raw catalog and hand-pick for them. Instead run this
short ask flow — it mirrors the web app and keeps the human in the loop:
Ask what kind of ads they want — the angle/offer/theme/season. The brand context already
gives you the vibe (voice), the audience, and the product catalog — do NOT ask for those.
Offer the real products[] to pick from rather than asking "which product?", and derive the
tone from the brand's voice. This shapes both the source choice and your steering prompt.
Keep it to one quick question about campaign intent.
Ask how to pick a source: their own ads, Community, upload, or "Surprise me".
Their own ads → use list_user_ad_templates to load the active brand's own sources and
let them choose from the results.
Community → search_ad_templates, let them choose, then call remix_community_ad
before submitting.
Upload → upload through the workspace and call create_user_ad_template. If its live
schema requires an ownership or permission answer, only supply it after explicit confirmation.
Surprise me (they want you/the app to pick) → call surprise_me_templates for the active
brand and hand the user the returned create_url.
It opens /create in CLI mode with the picks pre-selected, a preview modal, and the
copyable remix prompt at the bottom (in place of the Generate input). They can swap
picks and copy that prompt. If they'd rather you "just make them" without reviewing in the
app, you MAY submit the surprise_me_templates picks directly (skip to submit).
Browse in the app → hand the user this URL, with the
active brand's slug filled in:
https://make.gooseworks.ai/create?brand=<brand-slug>&cli=true
In CLI mode the app shows the copyable remix prompt at the bottom (dismissable / switchable
back to the UI composer). They browse the available own/Community sources and copy the prompt.
Close the loop. When the user pastes back the copyable remix prompt from the app
(it names the brand + the templates they chose), THAT is your cue to generate: resolve the
named source(s), inspect submit_remix_batch, and collect only its unresolved required inputs.
If the user already named an owned source (id/slug), a Community ad, or an upload, skip the source
choice. Competitor ads may inform the angle or structure, but describe them as inspiration, never
claim ownership, and never attest rights for the user.
Workflow — make ads from a template
Resolve the brand. Use list_ad_brands by name/site, then call get_brand_kit for the
selected brand. If the
kit's researchStatus isn't complete, you can still submit (the batch queues and runs when
research finishes) — just tell the user. Use the kit to pick product_name (a real entry from
products[], not a guess) and, if the user supplied product photos, reference_image_urls.
Pick the source ad(s) via the ask flow above. Once you have concrete ids:
call get_static_ad_template for each.
For a Community ad, remix_community_ad first; for an uploaded image, create_user_ad_template
first.
(Optional) Craft the steering prompt. The prompt is OPTIONAL — this is where the skill
adds value: turn the user's intent (from step 1) into a concise steering note (e.g. tone,
season, emphasis). Don't over-specify; the backend pipeline + brand kit handle palette, fonts,
product swap.
Quote the cost. Inspect and call estimate_remix_batch, then tell the user.
Submit ONE batch. Inspect the current submit_remix_batch schema, fill known required
inputs, ask only for unresolved user decisions, and omit unspecified optional settings. Keep
the returned batch_id and links.
Poll until done. Call get_remix_batch for the returned batch (or use
list_brand_creatives) every ~20-30s
until every creative's pending is 0. Most images finish in a few minutes; text-heavy templates
and quality: high take longer. Read each render's elapsed_seconds rather than guessing — a
render that's still running is healthy; do NOT re-submit thinking it stalled (that double-bills).
Hand back the links from the batch's links block — brand_url (gallery) and each
creative's app_url — copied verbatim. Never end on just "done" or a file path.
Workflow — edit an existing ad
User wants to tweak a creative they already made → use regenerate_creative. Infer whether they
want another take, a targeted edit, or an exact instructed change from their request. Then inspect
the live schema, ask only for any required source or instruction that is still missing, submit,
poll with get_remix_batch, and hand back the links.
Brand research
Prefer the backend's result: call get_brand_kit for the selected brand. If researchStatus is
complete, REUSE it — never re-research.
The split — backend owns visuals, you own the qualitative depth:
Backend LIGHT pass (automatic).create_ad_brand with a website_url kicks off the same
backend research the web app uses, in mode: "light": it resolves the authoritative logo,
colors, and fonts (Brandfetch + context.dev) plus a baseline kit, then flips
research_status to complete — usually under a minute. You can't reproduce those visual
signals locally, so never re-derive logo/colors/fonts. (Web onboarding via /api/ads/onboard
runs the full thing; nothing to do but read it.)
Your DEEP pass (local, agentic). You add the qualitative depth the light pass leaves thin —
positioning, audience segments, voice, brandType, value props, proof points, products — grounded
on the actual site.
CLI brand-research flow:
Inspect and call create_ad_brand with the known brand identity and website, then keep its id
and slug. The brand comes back with
research_status: "pending" (light pass in flight).
Wait for the backend light pass: poll get_brand_kit for that brand until researchStatus
is complete (usually <60s). Now the kit has authoritative logo/colors/fonts + a baseline.
At this point generation is already unblocked — but do the deep pass to make it good.
Deep research locally:gooseworks fetch brand-research and follow its phases. Ground
every fact on the fetched site — if the site can't be read, say so and ask the user; never
guess a category from the brand name alone.
Write the pack with write_file under agent-config/brands/<slug>/:
the brand-research/*.md docs + brand-assets/manifest.json (human-readable pack), AND
brand-research/kit-patch.json — the STRUCTURED fields the web UI renders. Field-for-field
contract; only what you put here reaches the kit. Shape:
{ positioning?: string, audience?: string, voice?: string, brandType?: string, tagline?: string, valueProps?: string[], proofPoints?: string[], products?: [{ name, description?, link?, pricing?, imageUrls?: string[] }] }
(brandType ∈ product | saas | service | agency | restaurant | fashion | beauty | fitness |
finance | education | health). Only URLs already in our storage for product images.
Do NOT set logo / colors / fonts here — the backend light pass already owns those.
Persist it: call finalize_brand_research for the brand. It merges kit-patch.json into the kit
NON-CLOBBERINGLY (it will NOT overwrite the backend's visuals or any user edit), then re-confirms
research_status: complete.
Verify: call get_brand_kit again and confirm the qualitative fields you wrote are present
before generating.
If the brand has NO website, the backend light pass can't run (nothing to fetch) — do the whole
thing locally (steps 3–6) and finalize; an un-finalized brand has no kit for generation and leaves
no artifact to debug a wrong run (this is how a bad local classification, e.g. mislabelling a SaaS
as a "drink company", used to vanish without a trace).
Analyze / intelligence (fetched recipes — NOT generation)
These are analysis recipes you fetch from goose-skills with gooseworks fetch <slug> and
follow; they do NOT touch the generation tools or credits-for-images. Pick the closest match;
if unsure, gooseworks search "<what the user wants>" first:
Campaign performance diagnosis ("why is my Meta/Google campaign underperforming",
creative fatigue, learning phase, pacing, auction overlap) → gooseworks fetch meta-ads-analyzer
(or ad-campaign-analyzer for cross-platform).
Lead/CAC quality ("are these ads driving qualified leads", true CAC vs vanity CPA,
Scale/Keep/Investigate/Cut) → gooseworks fetch ad-lead-quality-analyzer.
Competitor ad intelligence ("what ads are competitors running") →
gooseworks fetch competitor-ad-intelligence (Meta Ad Library: meta-ad-scraper;
Google: google-ad-scraper).
Save their scripts to /tmp/gooseworks-scripts/<slug>/ and follow their instructions. These
run through the gooseworks CLI (gooseworks fetch / gooseworks call), like the GTM skills.
Rules
MCP required — if mcp__gooseworks__* is unavailable, stop and tell the user to run
gooseworks install --claude --mcp.
One backend workflow — generation is submit_remix_batch / regenerate_creative ONLY.
Do NOT call FAL, the media proxy, submit_render, update_render_status, or upload render
files yourself; do NOT gooseworks fetch a local remix recipe to generate. The backend owns it.
Always end a successful run with the links from the batch's links block (brand_url +
each creative's app_url), copied verbatim. Never end on just "done" or a file path.
Quote cost before generating when it's non-trivial (use estimate_remix_batch), and
relay insufficient_credits plainly if the submit is rejected — don't retry blindly.
Use approved source paths. If the user didn't name a source, run the ask flow (own ads,
Community, upload, Surprise me, or browse in the app). "Surprise me" goes through
surprise_me_templates; browsing uses /create?brand=<slug>&cli=true. Never use the retired
curated third-party catalog.
Generate when they paste the app's copyable remix prompt back (or submit the surprise picks
directly if they'd rather not review).
Treat competitor ads as inspiration — never attest rights, imply ownership, or promise to
copy a competitor's distinctive expression.
Reconcile brand facts into the kit — when the user states or changes something brand-level
mid-task, check it against get_brand_kit and, with their ok, persist it via update_brand_kit
/ upsert_brand_product / add_brand_product_image so it sticks for future ads. Ask first;
never silently mutate the kit.
Record feedback — when the user reacts to a generated image, inspect and call
set_creative_feedback so the quality loop learns.
Plan mode is opt-in — only use the live approval option, then list_ad_approvals and
approve_ad_plan, when the user wants to review before spending credits; otherwise generate
immediately.
Don't busy-loop — poll get_remix_batch on a sensible interval (~20-30s); a queued
batch is waiting on research and will start on its own.
Report problems so we can fix them — when a batch fails/is rejected and you can't resolve it,
a required brand input/asset is missing, or a recipe/instruction is ambiguous or contradictory,
call the log_cli_event MCP tool (event_type: error/blocker/missing_input/confusion,
with the real error + step in details) so the team gets visibility. Still tell the user too.
