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Score your creative against what's actually winning in the ad market — before you spend the credit.

Most AI generation failures are predictable. A weak prompt, an off-formula creative — you can't tell until after you've burned the token. Dali scores it first, and it doesn't grade against opinions or generic "prompt tips." It grades against a real, living corpus of proven-winning ads — creatives still running in the market months after launch, scraped, embedded, and ranked. Two jobs:

• score_prompt — judge the prompt before you generate (craft: camera, motion, lighting, model-native language).
• score_creative — judge the actual image against proven winners (does it look like what converts, and what's missing).

Every wasted generation has a real cost — a Seedance retry is ~$6. The live dashboard tracks what the community has saved by catching bad creatives before they burned a credit.

You: "make a video ad for our glass serum bottle"

dali::score_prompt(prompt, "veo3")
→ 8/100  Grade: F
→ no camera move · no motion · no lighting · 8 words
→ Verdict: Generic stock footage guaranteed. Enhance first.
→ enhancement_brief included (score < 70):

  ① lead with camera — Veo 3's #1 lever: "Slow dolly", "Orbital push"
  ② describe physics: "a drop falls", "liquid ripples", "glass refracts"
  ③ lighting type + quality: "warm backlight", "rim-lit edges"
  ↳ [Camera]. [Subject + motion]. [Lighting]. [Mood]. [No text.]

✦ YOUR LLM rewrites using the brief:

  "Slow orbital push around a glass serum bottle on white marble. A single
   amber drop falls in extreme slow motion, catching warm backlight. Macro:
   liquid gold ripples outward from impact. Rim-lit edges, soft studio
   diffusion. Premium, clinical. No text."

dali::score_prompt(enhanced, "veo3")
→ 91/100  Grade: A  ✓ Safe to generate.

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The real winning data layer

This is what makes Dali more than a prompt linter. The scores are grounded in real ads that are actually winning, not hand-written rules.

How the corpus is built — longevity is the outcome signal. We scrape the public Meta Ad Library. An ad still running months after it launched is one the advertiser keeps paying for — a proven winner. That "still-running-after-N-days" longevity is a market-validated label you can't fake, and it's the spine of the whole dataset.

What's in it, today:

Ads ingested	10,204 (14,100 raw archive)
Proven winners (long-running)	3,808
Distinct advertisers	4,121
Verticals	8 — beauty, wellness, supplements, fitness, food, apparel, tech, pets
Winner creatives embedded	800 (1408-dim, balanced ~100/vertical)
Longest-running winner seen	2,431 days (6.6 years live)

The pipeline (offline → serving). The tools never scrape or embed on the fly — they read pre-built stores:

scrape Meta Ad Library         → proven winners (longevity label)
      → Gemini vision           → creative attributes (lighting, format, before/after, offer…)
      → prevalence SQL          → winning-pattern lift per vertical (winners vs baseline)
      → Vertex embeddings       → BigQuery VECTOR_SEARCH (nearest proven winners, cosine)
      → graph edges (Memgraph)  → (:Pattern)-[:WINS_IN {lift, n}]->(:Category)

So when score_creative runs, it embeds your image and finds the actual winning ads it most resembles by full visual signature — then tells you which winning attributes you're missing. When enhance_prompt runs with a category, the rewrite brief is backed by real market lift ("before/after shows up in 78% of winning wellness ads, 4× baseline"), not craft opinion.

Honest scope. The winner label is longevity (a strong market-validated proxy), not per-ad conversion rate — measured CVR validation is in progress. The corpus grows on a schedule, so coverage per vertical keeps deepening. What you get today: your creative scored against what's demonstrably surviving in the live market.

dali::score_creative(image_url, "beauty")
→ score 62/100  — partial resemblance to proven winners
→ looks_like:      Frøya Organics (ran 411d), tashportcosmetics (884d), Face Reality (346d)
→ what_to_change:  winners use "before/after" 4× more · offer-visible 1.8× more
→ defects:         none
→ Verdict: Partial — strong resemblance, but add the high-lift attributes before spending.

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Contents

• The real winning data layer
• Install
• Tools
• Supported models
• Platform supersets
• Why model-specific?
• MCP resources
• Contributing

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Install

Hosted MCP — connect once, scores every prompt and creative:

# Claude Code
claude mcp add --transport http dali https://dali.getlulu.dev/mcp

// Cursor / Windsurf — .cursor/mcp.json or windsurf settings
{
  "mcpServers": {
    "dali": { "url": "https://dali.getlulu.dev/mcp" }
  }
}

// stdio-only clients — npx wrapper around the hosted server, no Python needed
{
  "mcpServers": {
    "dali": { "command": "npx", "args": ["-y", "dali-mcp"] }
  }
}

→ Full install guide with all clients

Self-hosted — local, no auth required:

pip install dali-mcp
claude mcp add dali -- python -m dali.server

The self-hosted package exposes the prompt-scoring tools locally. The creative-scoring tools (score_creative, analyze_winning_formula) and the winning-ad corpus run on the hosted server — connect via the hosted MCP to use them.

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Tools

Score the creative — against real winners

Tool	What it does
score_creative(image_url, category)	Score an actual ad image. Embedding similarity to proven winners is the headline score; also returns the winners it resembles, which winning attributes it's missing, and generation defects — in one call
score_creative_from_view(category, …)	Score an image you're looking at (pasted/attached in the chat) — no URL. The model reads the creative's attributes and Dali scores them against the winning corpus (verdict + what to change). Use for images shared in-conversation; score_creative (URL) adds the embedding headline
analyze_winning_formula(csv, category, email)	Paste your own ads export (creative URL + CPA/CTR/ROAS) → your winning formula vs your losers, plus how you compare to the industry median

Score the prompt — before you generate

Tool	What it does
score_prompt(prompt, model, category?)	Grade 0–100 with a per-dimension breakdown and verdict. When the score is weak, the rewrite brief is returned in the same call. Reads intent with the conversation LLM (understands negation, any language)
enhance_prompt(prompt, model, category?)	Returns a structured rewrite brief — YOUR LLM writes the enhanced prompt. With a category, the brief is backed by real winning-ad lift
track_enhancement(original, enhanced, generator)	Record a before/after pair in the graph brain — trains community patterns
score_variations(prompts, generator)	Rank a list of prompt variants in one call — highest to lowest
suggest_generator(concept, budget_usd_max)	Pick the best model for your concept + budget

The graph brain & meta

Tool	What it does
creative_patterns(model)	Community top patterns for this model from the graph
community_benchmark(prompt, model)	Compare your prompt against community top scorers
prompt_neighbors(prompt, model)	Find A/B-grade prompts that share your patterns (score the prompt first, so its patterns are in the graph)
analyze_intent(prompt)	Parse dimensions: camera, motion, lighting, style, mood, gaps
my_story()	Your scoring history, model stats, grade distribution
list_generators()	All supported models with medium and core strength
dali_version()	Server version + changelog

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Supported models

Video

Model	Platforms	Best for	Prompt style
veo3	Higgsfield, Google AI Studio (veo-3.1-generate-preview), Runway	Cinematic brand films, narrative ads, photorealistic motion	Camera move → Subject → Action → Location → Lighting → Mood
seedance	Higgsfield, fal.ai (bytedance/seedance-2.0)	UGC, social-native content, TikTok/Reels performance ads	Natural language, motion-first, authentic feel
kling	Higgsfield (kling3), Kling.ai (kling-v3-text-to-video)	Character animation, product showcases, facial performance	Scene → Characters → Action → Camera → Style; multi-shot labels
runway	Runway (gen4_turbo)	VFX, character performance, cinematic motion	Motion-first — describe what moves, not what exists
wan	fal.ai (fal-ai/wan/v2.7/text-to-video)	4K, 20-second clips, native audio, open-source workflows	Scene → Motion → Sound → Duration → Mood
minimax	fal.ai (fal-ai/minimax/hailuo-02/pro/text-to-video)	Cinematic storytelling, character animation	Natural language + [camera movement] bracket syntax
higgsfield	Higgsfield (native model)	Physics-driven motion — cloth, hair, fluid, particles	Describe materials in motion, not motion abstractly

Sora 2 (OpenAI): API shutdown September 24, 2026. Do not build new dependencies on it — use Runway or Kling instead.

Image

Model	Platforms	Best for	Prompt style
flux	BFL API (flux-pro-v1.1), fal.ai, Replicate	Photorealism, technical photography, product shots	30–80 words; camera body + lens specs; front-load subject
midjourney	Midjourney (v8.1)	Artistic depth, editorial, stylized illustration	Prose + params appended: --ar 16:9 --s 300 --v 8.1 --style raw
ideogram	Ideogram API (V_4), fal.ai	Typography, logos, text-in-image, graphic design	Describe text exactly in quotes inside the prompt
firefly	Adobe Firefly 5 (enterprise)	IP-indemnified commercial assets, 4MP brand content	Natural language + contentClass and style.presets API params

Imagen 4 (Google): deprecated — use gemini-3.5-flash with image output. Dali still scores legacy Imagen prompts via the imagen model key but don't build new things on it.

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Platform supersets

Higgsfield and Runway are aggregator platforms — they proxy multiple underlying models under one API. The model you pick matters more than the platform name:

Platform	Model selector	Underlying model
Higgsfield	veo3	Google Veo 3.1
Higgsfield	seedance	ByteDance Seedance 2.0
Higgsfield	kling3	Kling 3
Higgsfield	wan2-7	Wan 2.7
Higgsfield	image2video	Higgsfield native
Runway	veo3	Google Veo 3.1
Runway	gen4_turbo	Runway Gen 4.5
Runway	seedance	ByteDance Seedance 2.0

Dali scores for the underlying model's native prompt language, not the platform wrapper. Pass the model name (veo3, kling, seedance…), not the platform name.

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Why model-specific?

Generic prompt optimizers don't know that:

• Veo 3.1 needs camera movement specified above everything else
• Kling 3 supports multi-shot scene labels natively in the prompt
• Flux responds to camera body and lens names like a photographer ("Sony A7 IV, 85mm f/1.4")
• Midjourney V8.1 reads prose + parameters, not keyword lists
• Higgsfield simulates physics — you describe materials in motion, not motion abstractly
• Minimax uses [Pan left] bracket syntax for camera moves — plain text camera commands are ignored
• Ideogram V4 needs text quoted exactly in the prompt for typography accuracy
• Wan 2.7 generates native audio — include sound descriptions alongside visuals

Dali has a separate scoring rubric and rewrite brief for each model. Your LLM does the creative rewriting — Dali provides the intelligence.

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MCP resources

creative://guide/veo3       → Veo 3.1 camera language guide
creative://guide/seedance   → Seedance UGC motion guide
creative://guide/kling      → Kling multi-shot + expression guide
creative://guide/runway     → Runway motion-first guide
creative://guide/wan        → Wan 2.7 audio + motion guide
creative://guide/minimax    → Minimax bracket camera guide
creative://guide/higgsfield → Higgsfield physics-motion guide
creative://guide/sora       → Sora 2 guide (API shutdown Sep 24, 2026)
creative://guide/flux       → Flux photography brief guide
creative://guide/midjourney → Midjourney V8.1 + parameters guide
creative://guide/ideogram   → Ideogram V4 typography guide
creative://guide/firefly    → Firefly 5 commercial content guide
creative://guide/imagen     → Imagen 4 guide (deprecated Aug 17, 2026)
creative://models           → All models overview

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Contributing

Model guides live in dali/data/guides/{model}.json on the hosted server. Found practitioner patterns that consistently produce high-grade results? Open an issue with the model, the pattern, and a sample prompt + result. The best contributions come from Reddit, Discord, and YouTube — real practitioners, not official docs.

→ Prompt best practices by model — cheat sheets, do/don't tables, top patterns per model → Dali creative flow skill — install this skill so your LLM follows the score → enhance → generate workflow automatically

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MIT License · Built by Lulu · dali.getlulu.dev