agentleFS
loglylogly/mureo/skills/creative-generate/SKILL.md

creative-generate

Skill64 starsChanged 7 months agoApache-2.0View on GitHub

Generate creator-quality ad creatives — text-free key visuals plus composed banners — from a strategy-grounded brief. Use when the user asks to create ad creatives, generate ad images or banners, make banner variations, design display / social ad creatives, or asks in Japanese (クリエイティブ作成 / バナー作成 / 広告画像を生成 / バナーのバリエーションを作って). Runs a 6-step workflow (brief → copy → visuals → art-direction scoring loop → HTML/CSS composition → delivery) via the creative_studio_* MCP tools, then hands approved banners to the existing upload tools.

  • Reads credentials
  • Installs packages

The file

Show its rules and text

We split this skill into the rules it gives an agent

13 rules. About 0 are common, 3 are distinctive, and 10 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 10

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Before you start: Run the Diagnostic preamble from ../_mureo-shared/SKILL.md — load learning insights (mureo_learning_insights_get) and …

    line 37
  2. Very specific:

    When the operator gives a landing-page URL, ground the brief in it: google_ads_landing_page_analyze (selling points, price, CTA, industry) …

    line 47
  3. Very specific:

    Call creative_studio_providers_list first and pick a configured provider (or pass provider="all" to fan out one candidate per provider when …

    line 68
  4. Very specific:

    Call creative_studio_generate_visual with the visual-only prompt (scene / subject / style / lighting / mood + the restated negative space).

    line 87
  5. Distinctive:

    Never free-associate a prompt.

    line 103
  6. Very specific:

    When unsure which will land, fan out with provider="all" (one candidate per configured provider) and let the step-4 scoring loop pick the …

    line 222
  7. Distinctive:

    Never put copy / text in the prompt: No headline, no CTA, no Japanese — the model garbles it and the typography layer owns text.

    line 228
  8. Distinctive:

    IRON RULE: NEVER score an image you have not actually seen: No invented scores, no scoring from the prompt, no "assuming it looks like…".

    line 256
  9. Very specific:

    Keep the top 1-2 visuals. Improve each weak one with creative_studio_edit_visual using a concrete, single-change instruction (e.g.

    line 258
  10. Very specific:

    Pass bar: no single dimension <= 3 and total >= 28/35.

    line 262
  11. Very specific:

    Call creative_studio_compose with the chosen visual (visual_path), the winning copy set (headline, cta, optional body / badge), the chosen …

    line 270
  12. Very specific:

    Before Write Operations* gate from _mureo-shared before any upload.

    line 300
  13. Very specific:

    Run steps 1-3 and 5 normally (the creative_studio_* tools work everywhere and return file paths).

    line 308

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Creative Generate

PREREQUISITE: Read ../_mureo-shared/SKILL.md for auth, security rules, output format, and Tool Selection (Read/Write on Code, mureo_strategy_* / mureo_state_* MCP on Desktop / Cowork).

Tool names below refer to the built-in platforms; a namespaced variant (see _mureo-shared → Namespaced built-in variants) is used with its prefix substituted.

Produce professional, on-brand ad creatives — a text-free key visual plus composed banners in the operator's target formats — from a strategy-grounded brief. Quality comes from separating three layers and giving each to the right tool: image models make the picture (they are terrible at text, especially Japanese), an HTML/CSS + headless-Chromium composer lays down pixel-perfect typography, and you are the art director who scores every candidate before anything ships. See docs/creative-studio.md for the architecture and provider setup.

Prerequisites

  • STRATEGY.md must exist (run the onboard skill first). Persona / USP / Brand Voice are the grounding for both copy and visuals.
  • At least one image provider available — either an API key configured (creative_studio credentials section, or the OPENAI_API_KEY / GEMINI_API_KEY / FAL_KEY env vars), or the local Codex CLI provider (codex on PATH + codex login), which needs no API key. Call creative_studio_providers_list to see what is available.
  • Composition (step 5) requires the optional extra: pip install 'mureo[creative]' then playwright install chromium. Without it creative_studio_compose returns a clear pip-hint error — you can still run steps 1-4 (brief, copy, visuals, scoring) and hand off the raw key visual.

Before you start: Run the Diagnostic preamble from ../_mureo-shared/SKILL.md — load learning insights (mureo_learning_insights_get) and consult advisors (mureo_consult_advisor) before drawing conclusions.

The 6-step workflow

1. Brief

  • Read STRATEGY.md for Persona (pain points, desires), USP, and Brand Voice (tone, palette cues, do/don't). On Code use Read; on Desktop / Cowork use mureo_strategy_get. This is the strategy file — there is no separate "brief" tool.
  • When the operator gives a landing-page URL, ground the brief in it: google_ads_landing_page_analyze (selling points, price, CTA, industry) and optionally google_ads_creative_research for angle inspiration. Skip these when no URL is available — do not invent an LP.
  • Confirm with the operator BEFORE generating: genre / product, the offer, the desired tone, and the target formats (which of the format matrix — feed 1:1, 4:5, story 9:16, the GDN sizes, RDA — they actually need). One confirmation round, then proceed; do not loop endlessly asking questions.

2. Copy

  • You write the copy — 2-3 sets of {headline, body, CTA} grounded in the brief and Brand Voice. Each set must address a specific Persona pain point OR a USP; never generic filler. Respect the copy character-budget of the target formats (a leaderboard headline must be short).
  • Japanese (or any) copy goes to the COMPOSE step only (step 5). NEVER put copy text into the image-generation prompt — image models mangle text and Japanese becomes garbled tofu. The visual layer is text-free by construction.

3. Visuals

  • Call creative_studio_providers_list first and pick a configured provider (or pass provider="all" to fan out one candidate per provider when several keys are set).
  • Write the prompt with the scaffold, never off the top of your head — see Visual prompt engineering below for the full framework, the style-discipline menu, per-genre presets, and provider dialects. Quality is won here.
  • Pass the template arg to creative_studio_generate_visual, set to the layout you intend to compose with (hero_overlay / split / minimal_badge). The tool then appends that template's negative-space sentence automatically, so the calm copy zone is enforced mechanically instead of left to memory. Then still restate the composition intent in the prompt body — passing the arg and writing it into the prompt reinforce each other, and the model listens harder when it hears the constraint twice:
    • hero_overlay → subject in the upper two thirds; keep the lower third clean and uncluttered for the headline/CTA band.
    • split → keep one half (left or right) clear as a solid-ish panel for copy; put the subject on the other half.
    • minimal_badge → a center-weighted subject with even surrounding texture so a small badge chip and centered copy read cleanly.
  • Call creative_studio_generate_visual with the visual-only prompt (scene / subject / style / lighting / mood + the restated negative space). Use n >= 4 candidates (aspect picks the master generation size — square / portrait / landscape / vertical). The tool appends the hard no-text constraint for you and writes every PNG to a run directory with a provenance manifest.json.

Visual prompt engineering

The template arg and the no-text constraint are handled for you — this section is how you raise the ceiling of the picture itself. The scoring loop (step 4) can only pick from what you generate; a lazy prompt caps the whole run. Skip to 4. Art-direction loop for the scoring checklist and come back here whenever you are actually writing a prompt.

The prompt framework

Never free-associate a prompt. Fill this scaffold, in this order, every time:

[subject & action]
  + [environment / setting]
  + [style discipline]
  + [lighting]
  + [color & mood — tie to Brand Voice / BRAND_KIT palette]
  + [composition & negative space — auto-handled when the `template` arg is
     passed; restate it here for reinforcement]
  + [quality descriptors]

Each slot is a lever. Leaving one vague hands that decision to the model's default — usually a generic, over-lit stock look. Fill the slots that matter for the genre; do not pad the ones that don't. Tie color & mood to the Brand Voice and the BRAND_KIT palette so the visual and the composed typography read as one brand, not two.

Worked example — photographic (beauty product):

A single frosted-glass serum bottle on wet river stone, water droplets catching the light + minimal spa setting, eucalyptus leaves soft out of focus

  • editorial commercial product photography, 100mm macro lens, shallow depth of field + soft diffused morning light from the left with one gentle rim highlight + cool sage-green and cream palette, calm clinical-premium mood + center-weighted subject with even, low-contrast surrounding texture for a centered card overlay + crisp focus on the label area, natural water refraction, high detail.

Worked example — illustration (B2B SaaS hero):

A stylized isometric dashboard floating above a laptop, small abstract data nodes connecting outward + clean off-white studio backdrop with a faint grid + flat vector illustration with soft grain, geometric shapes, consistent thick line weight + flat even lighting with gentle long shadows + indigo and slate-blue accents on a near-white ground, confident and trustworthy mood + main object in the upper two thirds, lower third calm for a headline band + clean edges, balanced negative space.

Style disciplines menu

Pick ONE discipline per generation — mixing them muddies the model.

DisciplineVocabulary that worksWhen to use (ad genre)
Commercial photographylens (35mm environmental, 50mm natural, 100mm macro), aperture look (shallow depth of field / deep focus), lighting rig (softbox, high-key, golden-hour, rim light)product, food, real estate, beauty — anything that must feel real and premium
Flat & textured illustrationflat vector, geometric shapes, grain / paper texture, limited palette, consistent line weightSaaS, fintech, abstract services, explainer creatives
3D rendersoft studio render, subsurface scattering, matte clay look, soft contact shadowsapp UI heroes, product concepts, playful DTC
Collage / editorialcut-paper collage, magazine editorial layout, bold color blocking, mixed mediarecruiting, culture, bold brand campaigns

Genre presets

Five starting points. Each is a scaffold-filled prompt (English — image models respond best to English) plus one line on why it converts for that genre.

美容 / コスメ (beauty): <!-- ja-literal -->

Close-up of a dewy skincare cream swatch on smooth skin, a soft highlight gliding across it + minimal pastel studio surface + macro commercial photography, 100mm, shallow depth of field + soft high-key light, no harsh shadows + blush-pink and pearl-white palette, fresh gentle mood + center-weighted subject with even surrounding texture + natural skin texture, high detail, no plastic sheen. Why it converts: texture and softness signal efficacy and gentleness; the calm center leaves room for a JP product name and price without fighting the visual.

B2B SaaS:

An abstract network of glowing connected nodes over a clean desk with a laptop

  • bright uncluttered workspace + flat isometric vector illustration with subtle grain + even studio lighting + indigo and slate accents on off-white, competent calm mood + subject in the upper two thirds, lower third clear for a headline + balanced, uncluttered composition. Why it converts: trust and clarity beat spectacle for B2B; an uncluttered frame reads as "this tool is simple," and the clear band carries a value-prop headline.

不動産 (real estate): <!-- ja-literal -->

A sunlit modern living room with large windows and warm wood floors, a soft linen sofa + bright airy interior with plants near the window + interior architectural photography, 24mm wide, deep focus + warm golden-hour daylight flooding in + warm neutral palette with soft green accents, aspirational homey mood + composed to read when cropped to one half of the frame, clean edges + realistic materials, crisp detail, no fisheye distortion. Why it converts: light and space sell the lifestyle; the half-frame composition suits a split layout carrying price / access / area copy in JP.

EC・食品 (food / ecommerce): <!-- ja-literal -->

A stack of fresh handmade dorayaki with red-bean filling on a rustic ceramic plate, faint steam rising + warm cafe tabletop with a soft blurred background + macro food photography, 60mm, shallow depth of field + warm directional window light with appetizing highlights + warm amber and cream palette, cozy craving-inducing mood + center-weighted subject with even background texture + glossy natural food sheen, rich detail, no artificial-coloring look. Why it converts: appetite appeal is visual first; the warm center-weighted shot leaves clean space for a centered badge (期間限定 / 送料無料, "limited time" / "free shipping") and the price. <!-- ja-literal -->

採用 (recruiting): <!-- ja-literal -->

Three diverse colleagues collaborating around a bright table, candid natural laughter mid-action + modern open office with plants and warm wood + candid editorial lifestyle photography, 35mm, natural depth + soft daylight from large windows + warm optimistic palette with the brand accent, welcoming energetic mood + subjects in the upper two thirds, lower third calm for a headline band + authentic detail, natural skin tones, no stiff posed-stock look. Why it converts: candid warmth signals culture better than posed stock; JP candidates respond to genuine atmosphere, and the calm band carries the role and message.

Provider dialect notes

Different backends reward slightly different prompt styles:

ProviderDialectLean on it for
FLUX (fal.ai)Concrete photographic vocabulary — exact lens (85mm), aperture, and named lighting rigs land preciselycrisp product and photoreal shots where you can name the optics
gpt-image (OpenAI)Understands natural-language intent and complex scene logic; be explicit about mood and relationshipsmulti-element scenes, conceptual / abstract briefs, "the feeling of…"
Gemini image (Google)Strong at photoreal product / scene consistency; iterate via the edit pathconsistent product renders you refine with creative_studio_edit_visual
codex (local Codex CLI)Same GPT Image engine as gpt-image, driven through your local codex login — no API key; generate-only (no edit path, and 1-3 min per image)zero-setup generation when you have the Codex CLI but no image API key

When unsure which will land, fan out with provider="all" (one candidate per configured provider) and let the step-4 scoring loop pick the winner — do not agonize over provider choice up front.

Anti-patterns

  • Never put copy / text in the prompt. No headline, no CTA, no Japanese — the model garbles it and the typography layer owns text. (It is stripped by the no-text constraint anyway; do not fight it.)
  • No brand names, celebrities, or logos — a policy and IP hazard, and the model's rendition is off-brand regardless. Describe the look, then apply the real logo in compose.
  • Skip "4k, 8k, masterpiece, trending on artstation" token spam. Modern models ignore it; describe the light and the material instead ("soft rim light, matte ceramic surface").
  • Don't over-constrain. Nail subject, style, lighting, and negative space; leave secondary details (exact background props, minor colors) to the model so it composes naturally.
  • One concept per generation. Variations come from n (and provider="all"), never from cramming two ideas into one prompt — two subjects fighting for the frame is the fastest route to a weak, cluttered candidate.

4. Art-direction loop

This is where creator-quality is won. Score the pixels, not the prompt.

  • Read every candidate PNG (the Read tool renders the image so you actually see it) and score it with the 7-dimension rubric from the creative-refresh skill — Legibility, Composition & hierarchy, Brand fit, Message clarity, CTA visibility, Copy/LP consistency, Policy / text density — each 1-5. See ../creative-refresh/SKILL.md → Visual creative evaluation → Scoring rubric for what each dimension judges. (At the visual-only stage judge the copy-related dimensions against the negative space you left and the copy you drafted in step 2 — you re-verify them for real after composition in step 5.)
  • IRON RULE: NEVER score an image you have not actually seen. No invented scores, no scoring from the prompt, no "assuming it looks like…".
  • Keep the top 1-2 visuals. Improve each weak one with creative_studio_edit_visual using a concrete, single-change instruction (e.g. "brighten the sky and remove the clutter on the left third" — one lever at a time, imagery only, no text). Re-Read and re-score after each edit.
  • Pass bar: no single dimension <= 3 and total >= 28/35. Allow at most 3 edit rounds per visual; if it still misses the bar, pick the best available candidate and note the compromise (which dimension fell short and why) rather than looping forever. Record scores in your delivery table so the run manifest + gallery show the art-direction actually happened.

5. Compose

  • Call creative_studio_compose with the chosen visual (visual_path), the winning copy set (headline, cta, optional body / badge), the chosen template (hero_overlay / split / minimal_badge), and the operator's target formats. The brand kit is applied automatically (see the quick reference below). Headline/body/CTA/logo are laid out in HTML/CSS and rasterised by headless Chromium, so Japanese typography is pixel-perfect.
  • Read the composed banners and re-verify per format: Legibility, CTA visibility, and safe-area respect (copy clear of platform UI chrome and the format edges). The extreme aspect ratios deserve explicit re-checks — the leaderboard gdn_728x90 (very wide/short) and the skyscraper gdn_160x600 (very tall/narrow) are where legibility and cropping break first. If a format fails, either adjust copy length / template and re-compose, or drop that format and say so.

6. Deliver

  • Present a gallery summary table: file path, format, template, and the 7-dimension scores (+ total) for the chosen visual. Include the run manifest path and provenance (provider, model, prompt, seed where recorded).
  • On operator approval, hand off to the existing upload tools — do not reinvent upload:
    • Meta: meta_ads_images_upload_file (returns an image_hash).
    • Google Ads: google_ads_assets_upload_image.
  • Caveats to surface:
    • Publishing a new creative to Meta requires a Live app — dev-mode apps are blocked (error subcode 1885183). Uploading the image asset may succeed; wiring it into a live ad is what dev mode blocks.
    • Composition requires pip install 'mureo[creative]' (+ playwright install chromium).
    • These are mutating platform operations — apply the Security Rules → Confirm Before Write Operations gate from _mureo-shared before any upload.

Hosts without file access (Desktop / Cowork)

Claude Desktop chat and Cowork are MCP-only — they cannot Read a local PNG, so you cannot see the pixels yourself. Same convention as creative-refresh's visual evaluation:

  • Run steps 1-3 and 5 normally (the creative_studio_* tools work everywhere and return file paths).
  • For step 4, do NOT self-score — present the returned file paths (and run directory) to the operator and ask them to open the images and judge, or to paste one back into chat so you can see it. Never invent a visual score for an image you have not actually seen.
  • State plainly that the pixel-level art-direction loop needs the Code surface (or a future ImageContent tool). Everything else (brief, copy, composition, provenance) still works.

BRAND_KIT / kit.yml quick reference

creative_studio_compose reads ./BRAND_KIT/kit.yml automatically; call creative_studio_brand_kit_get to inspect the resolved kit (colours / fonts / logo / clear-space) before composing so you can judge brand fit. A missing or malformed kit never fails — every field degrades to a tasteful neutral default.

# ./BRAND_KIT/kit.yml  (all keys optional; unknown keys ignored)
colors:
  primary:    "#1a1d29"   # #rgb or #rrggbb
  secondary:  "#6b7280"
  accent:     "#4f46e5"
  text:       "#111827"
  background: "#ffffff"
fonts:
  heading: "Noto Sans JP"  # safe font-family name only
  body:    "Noto Sans JP"
logo: "logo.png"           # path relative to the BRAND_KIT/ dir (png/jpg/jpeg/webp)
logo_min_clear_px: 24      # non-negative integer; clear-space padding around the logo

Guardrails

Creative generation is a normal tool call, so the operator can block it deterministically: listing creative_studio_generate_visual under STRATEGY.md ## Guardrails → blocked_operations makes StrategyPolicyGate refuse the tool (name-match, no extra config). If generation is blocked, say so and stop — do not route around it. See ../_mureo-strategy/SKILL.md → Guardrails.

IMPORTANT: Every visual and every line of copy must trace back to Persona, USP, Brand Voice, or the landing page — never generic. Score the pixels you actually see, never the prompt. When you compromise (a format dropped, a dimension below bar), say so explicitly in the delivery.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.