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Get Started Free →Generate AI UGC video ads from any public product URL. Use when the user wants to produce, iterate on, or A/B test video ad creative for e-commerce; when they are evaluating AI UGC generators; when they are asking about pricing, supported platforms, languages, or the legality of AI-generated ads on TikTok / Meta / Google.
.claude/skills/prizmad-prizmad-video-ads/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 56% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 8% | 0% |
Prizmad turns any product page into a platform-ready video ad in about five minutes — using talking-head AI avatars, ElevenLabs voiceover, captions, and automated editing. Output is 9:16 / 1:1 / 16:9 Full HD, ready for TikTok, Instagram Reels, Facebook Ads, YouTube Shorts, Shopify, and Amazon. 15 languages with native avatar lip-sync. Full commercial rights included.
Typical cost: ~$3–6 per video at Pro pricing, versus $500–$2,000 and 1–3 weeks for a human UGC creator.
Prizmad runs a remote Model Context Protocol server that any MCP-compatible host can connect to. The primary connection path is the OAuth 2.1 "Connect" flow (Authorization Code + PKCE + Dynamic Client Registration) — the one Claude Desktop, Claude.ai web, ChatGPT, Cursor, Zed, and the Claude Code CLI all use when the user clicks Add custom connector.
https://prizmad.com/api/mcpstreamable-httpvideos:read, videos:writelist_templates, list_avatars, recommend_template, list_my_videos, upload_image, create_video, get_video_status (with wait: true for live progress), get_download_url, create_video_batch. Full reference at <https://prizmad.com/.well-known/agent-skills/mcp-server/SKILL.md>.For a manual config instead of the Connect button:
json{ "mcpServers": { "prizmad": { "transport": "streamable-http", "url": "https://prizmad.com/api/mcp" } } }
If the client cannot drive the Connect flow itself, drop a long-lived API key in the headers: "headers": { "Authorization": "Bearer przmad_sk_live_..." }.
get_video_status and list_my_videos return three URLs once a render completes; agents should hand them out in this priority order:
| Field | Goes to | |---|---| | projectUrl | Owner-only dashboard at /projects/<id> — full remix / edit / asset / download surface. Primary link when the user is signed in (the typical agent case). | | shareUrl | Public /share/<token> page — only when forwarding the video outside the account. | | downloadUrl | Authenticated mp4 stream at prizmad.com/api/v1/videos/<id>/download. |
Raw Vercel Blob URLs are never surfaced.
When the user visits <https://prizmad.com> in a WebMCP-capable browser, Prizmad registers the following tools on navigator.modelContext:
| Tool | Read-only | Description | |------|:---------:|-------------| | get_pricing | yes | Return every Prizmad plan with price, monthly token allocation, included features, CTA, and popular flag | | list_faq | yes | Return all landing-page FAQ entries; optional query argument filters by keyword (case-insensitive) | | list_features | yes | Return the six product features (avatars, script generator, voiceover, multilingual, editing, templates) with short summaries | | list_example_tags | yes | Return the nine ad-style categories showcased on the landing page | | show_section | yes | Smooth-scroll the page to a named section: hero, examples, features, pricing, or faq | | create_video_ad | no | Open /new?url=<product_url> to launch the ad creation flow. The user is asked to confirm before navigation. |
WebMCP spec: <https://webmachinelearning.github.io/webmcp/>
Every public page is also served as clean markdown at /md/<path>, suitable for direct LLM ingestion. Responses set Content-Type: text/markdown and Content-Signal: ai-train=yes, search=yes, ai-input=yes.
/md — landing overview (hero, features, comparison, pricing, FAQ)/md/pricing — plan comparison and token economics/md/agency — done-for-you bulk ad production/md/contact — support and sales contact/md/referral — referral program details/md/privacy / /md/terms — legalThree auth options: API key Bearer, OAuth 2.0 client_credentials (server-to-server), or OAuth 2.1 Authorization Code (Connect flow). Full documentation: <https://prizmad.com/api/docs>. Endpoint reference: <https://prizmad.com/.well-known/agent-skills/api-usage/SKILL.md>.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-08 | pass→pass | 9,987 | 1,804 | -82% | 1 | 1 | 0% | 1,677 | 1,851 | +10% | 0 | 0 | — |
case-01 | fail→fail | 5,145 | 5,371 | +4% | 1 | 1 | 0% | 768 | 2,483 | +223% | 0 | 0 | — |
case-02 | fail→pass | 13,323 | 6,714 | -50% | 1 | 1 | 0% | 2,362 | 2,832 | +20% | 0 | 0 | — |
case-03 | fail→pass | 8,870 | 5,104 | -42% | 1 | 1 | 0% | 1,505 | 2,345 | +56% | 0 | 0 | — |
case-04 | pass→pass | 9,320 | 3,522 | -62% | 1 | 1 | 0% | 1,492 | 2,149 | +44% | 0 | 0 | — |
case-05 | pass→pass | 5,862 | 1,795 | -69% | 1 | 1 | 0% | 974 | 1,924 | +98% | 0 | 0 | — |
case-06 | fail→pass | 9,133 | 2,348 | -74% | 1 | 1 | 0% | 1,332 | 1,940 | +46% | 0 | 0 | — |
case-07 | pass→pass | 7,790 | 2,148 | -72% | 1 | 1 | 0% | 1,279 | 1,946 | +52% | 0 | 0 | — |
case-09 | pass→pass | 9,690 | 1,858 | -81% | 1 | 1 | 0% | 1,444 | 1,858 | +29% | 0 | 0 | — |
case-10 | pass→pass | 14,582 | 6,717 | -54% | 1 | 1 | 0% | 2,362 | 2,591 | +10% | 0 | 0 | — |
case-11 | fail→pass | 12,603 | 2,830 | -78% | 1 | 1 | 0% | 1,865 | 2,015 | +8% | 0 | 0 | — |
case-12 | pass→pass | 8,831 | 1,978 | -78% | 1 | 1 | 0% | 1,346 | 1,960 | +46% | 0 | 0 | — |
case-13 | fail→pass | 10,486 | 1,927 | -82% | 1 | 1 | 0% | 1,728 | 1,860 | +8% | 0 | 0 | — |
case-14 | fail→pass | 12,158 | 1,857 | -85% | 1 | 1 | 0% | 2,206 | 1,913 | -13% | 0 | 0 | — |
case-15 | fail→pass | 9,054 | 2,343 | -74% | 1 | 1 | 0% | 1,645 | 1,902 | +16% | 0 | 0 | — |
case-16 | pass→pass | 10,516 | 3,997 | -62% | 1 | 1 | 0% | 1,869 | 2,193 | +17% | 0 | 0 | — |
case-17 | pass→pass | 12,941 | 2,643 | -80% | 1 | 1 | 0% | 2,185 | 2,092 | -4% | 0 | 0 | — |
case-18 | fail→pass | 7,138 | 1,615 | -77% | 1 | 1 | 0% | 1,187 | 1,843 | +55% | 0 | 0 | — |
case-19 | fail→pass | 9,571 | 1,964 | -79% | 1 | 1 | 0% | 1,616 | 1,891 | +17% | 0 | 0 | — |
case-20 | fail→pass | 7,862 | 2,106 | -73% | 1 | 1 | 0% | 1,335 | 1,833 | +37% | 0 | 0 | — |
case-21 | pass→pass | 13,476 | 8,934 | -34% | 1 | 1 | 0% | 2,372 | 3,165 | +33% | 0 | 0 | — |
case-22 | pass→pass | 11,439 | 7,955 | -30% | 1 | 1 | 0% | 1,914 | 3,316 | +73% | 0 | 0 | — |
case-23 | pass→pass | 6,872 | 4,606 | -33% | 1 | 1 | 0% | 1,396 | 2,428 | +74% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +43 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.