Best AI video and image generation skills for your agent

Ask an agent to make a fifteen-second product video and watch what it writes: cinematic, epic, hyper-detailed, 8K, masterpiece, trending on ArtStation, no blur, no artifacts. Every one of those words is a decision the agent declined to make. The generator bills for the render anyway — video is metered per attempt, so a vague prompt is not a retry, it is a purchase. Then the second failure arrives, quieter and more expensive: the agent narrates a spec it invented. A charging time, a port count, a price. Nobody asked it to lie; it just had a slot to fill and no rule against filling it.

The useful thing about reading these skills closely is that the good ones all found the same enemy, independently, and built machinery against it. One classifies filler into six named patterns and tells the agent what to replace each with. Another — a full ad pipeline that pays for the render itself — maintains a literal allowlist of numbers the ad is permitted to say out loud, and refuses everything else. Note that those two are not the same kind of thing. One edits a string and stops; the other spends money and hands back a file. That is the split to get straight before you install anything here, because the craft can live on either side of it and the file only comes out of one. Installing only one kind is how you end up stuck.

Top picks

Five recommendations, plus four skills below the line that a reader will meet anyway and should meet with the caveat attached.

skill	publisher	license	verdict	updated
seedance-antislop	Emily2040	MIT	Pick	2026-07-25
ugc-ads	Pika-Labs	Apache-2.0	Pick	2026-07-20
model-selector	cclank	MIT	Pick	2026-06-01
fal-model-guide	JosiahSiegel	MIT	Pick	2026-06-18
seedance-ai-avatar	rediumvex	MIT	Pick	2026-06-11
higgsfield-soul-id	higgsfield-ai	MIT	Situational	2026-07-27
Digital Twin Generation	eachlabs	none declared	Situational	2026-04-21
ugc-ad-production	alecs5am	Apache-2.0	Check before use	2026-07-27
seedance-2-5	evanmopsps	MIT	Check before use	2026-06-29

Fix the prompt before you pay for the render

seedance-antislop works on the string in the last moment before it costs money, which is why it pairs with everything else here rather than competing with it. It applies one test — "Every major phrase should be visible to a camera, measurable by a light meter, audible in the mix, or observable as motion" — and then sorts whatever fails into six named classes, each with its own repair. Borrowed image-model tokens get deleted outright, on the reasoning that resolution is a setting rather than prose. Comma-dumped keyword salad gets rewritten as shooting-brief sentences with a time axis.

The class worth the install on its own is negation. Telling a video model "no blur, no artifacts, no extra fingers" is the instinct every agent has; this skill's position is that "negation summons", so you describe what is present instead and keep negation in the constraint slot only. It also refuses to over-correct: genre language paired with concrete direction stays. The caveat is scope — it edits prompts for one model family and will not tell you which model to send them to.

The UGC ad pipeline that will not invent a number

ugc-ads does not stop at the prompt. It runs an entire fifteen-second vertical product ad end to end — hook, three jump cuts, outro, every beat carrying a spoken line for lip-sync — pays for the render, burns the overlays, and hands back a finished URL. What makes it worth naming is the grounding discipline wrapped around that. Step one fetches the product page and builds a claims_allowlist, where every numeric spec must carry a source_quote from visible page text. Nothing outside that list may be spoken or overlaid. The instruction is blunt: "forbid inventing numbers", with unsupported claims rewritten qualitatively instead.

It also closes the loop rather than trusting itself. Brand and spec text are burned on afterward by a deterministic overlay, because video models garble wordmarks, and the result is OCR-checked from an extracted frame before captions run. A final pass returns unauthorized_numeric_claims and blocks delivery if anything unapproved appears on screen. The caveat is money: it gates on a stated cost estimate of about 4,000 credits, roughly $40, and asks for explicit consent before spending it.

Choose the model before you write the prompt

model-selector is a shopping assistant, and a well-built one. It asks three or four questions that actually change the answer — duration, native audio, language, whether you are editing existing footage, deployment constraints — then walks a decision tree to one recommendation and a named runner-up. The tree encodes real distinctions: editing existing video routes to a single option because the alternatives cannot do it, and local deployment with LoRA training routes to the only open-weights entry in the capability matrix, complete with suggested sampler settings — while the same answer points past the matrix at two further open-source models it says are not in the repository's main directory.

Two caveats, both concrete. The document is written in Chinese, so an agent reasoning in English is translating as it goes. And its own headline counts have drifted apart: the front-matter description advertises sixteen models, the opening line says fifteen, and the capability matrix is headed with eleven while carrying twelve rows, counted off against the table itself. The routing logic is sound; the arithmetic wrapped around it is stale.

The English-language equivalent, if you are already on fal.ai

fal-model-guide answers the same question for one platform's catalogue, and answers it in a form an agent can act on immediately: the catalogue tables carry the literal endpoint string beside each model, so the decision and the call are the same lookup. It goes further than a comparison table by pricing the tradeoff in time — image models with an approximate seconds-per-render band, video models likewise — and by separating a development phase from a production phase, cheap models for prompt iteration and expensive ones only for finals.

Where it stops is worth knowing. It ships model-specific parameter defaults that are genuinely useful — a guidance-scale band, a step count, the note that the fast variant is tuned for four steps and not more — but it does not present itself as the index. For anything absent it hands you to the platform's live catalogue, which it describes as running to 600+ models. Treat it as a curated shortlist with endpoints attached.

If a generated person is the subject, start with the eyes

seedance-ai-avatar is a prompt-construction guide for video whose main subject is a generated character — spokesperson clips, product demos, virtual influencer posts. It organises the work as four style lanes, from photorealistic human through to non-humanoid geometric entities, and gives each lane its concrete prompt markers, its best-fit use cases, and its risk. Its structural argument is that the uncanny valley has two exits, full photorealism or committed stylisation, and that the failure mode is stopping between them.

The payoff is ten numbered rules, and rule nine is the one you would have gotten backwards alone: describing an avatar as AI-generated or digital in the prompt produces visual cliché — grids, floating data, lens flare — so you describe a person or you describe a designed character and let the aesthetic carry the rest. Rule three is the practical one: catch light, blink timing and gaze direction all have to be stated, because the document names underspecified eyes as the single most common uncanny-valley failure.

Which of these actually generates a video?

A skill with a name like these may never render anything, and the name will not tell you which one you are holding. seedance-2-5 is the clearest case because it says so itself: a safety rule in the document instructs the agent not to claim it generates the video, since what it produces is a prompt, a settings block and a shot plan that the user then runs elsewhere. Useful, provided you know that is what you are getting — and worth noting that every product link it hands back carries the same ?ref=skillsmp tracking parameter, including the one the workflow tells the agent to close with.

The job-runners look different once you know what to look for: an API base URL, an environment variable, a polling loop. seedance is one — a Node.js runner against Volcengine's Ark endpoint, and it carries the operational detail a prompt guide never would, including the warning that task data and the returned video URL are retained for 24 hours and must be downloaded before then. higgsfield-generate is a broader one: it wraps a vendor CLI and submits to whichever of the platform's image, video, 3D or audio models the brief calls for, blocking on a --wait flag until the result URL prints rather than leaving the agent to poll. ugc-ads, the second pick above, is the same kind of thing narrowed to one job — it makes the paid render call itself and returns the finished file.

Then there is a third shape, easy to mistake for a runner because it authenticates against the same CLI. higgsfield-soul-id trains a reusable identity model from 5 to 20 varied face photos and returns a reference id — and that is all it returns. Its own front matter spells out the chain: train the identity once, then pass that id to higgsfield-generate, which is where a file actually gets made. It states plainly that training requires a paid plan and that the agent should say so before submitting. Genuinely useful, and useless on its own.

The practical rule: if you want your agent to actually produce a file, one of each is the minimum — something that writes the prompt and something that runs the job. Two prompt guides and no runner leaves you with beautiful text and no video.

What to check before you install one

Check the licence twice. The licence a directory shows you is usually the repository's, and the skill's own front matter can say something different — when they disagree you have two answers, not one. ugc-ad-production is the live example: its own front matter declares MIT, while the record for the repository it ships in reads Apache-2.0. Whichever is right, you cannot tell from the card. The same document rewards a careful read for other reasons — it names one model for script writing in its workflow and a different one in its own cost summary, and it hardcodes a promotional coupon code and a per-clip price, neither of which will age well.

Names are not identities. Skills are community-published and nothing namespaces them, so the same name routinely covers different documents. A skill called seedance is either the Node.js runner above, or inference-sh's skill of the same name — a hosted-CLI dispatcher that routes to four differently-named app IDs on someone else's platform, priced per token off its own formula. Same name, different service, different credentials, different failure modes. The publisher and the body are a skill's identity; the name only tells you its topic. Open the body and find the base URL before you assume two of them are the same tool.

If the work involves a synthetic person, read the document for what it says about the person. Some of these are explicit. Digital Twin Generation carries a short privacy-and-consent section near the end of the document, written as preconditions on doing the work at all rather than as advice about it — disclosing synthetic footage where disclosure is required is one of them, consent from the subject is another. Its error-handling table makes the same point operationally, routing a content-policy rejection back to the question of whether the source photographs were permitted in the first place. seedance-examples-ko approaches it as a labelling system: it defines four labels — safe, needs-owned-reference, surface-specific, rewrite-required — and its output contract is that whatever it hands back arrives with one of them attached, plus a risk note and a safer variant where one is needed. That last label covers prompts naming a protected identity, brand, celebrity, scene, song or voice, and the document supplies a rewrite pattern for turning those into original descriptors. ugc-ads runs the same idea as a preflight, classifying the supplied portrait and stopping before any paid render when it reads as a recognisable trademarked character. seedance-style applies the equivalent rule to aesthetics, replacing studio and franchise anchors with layered descriptors of medium, surface, palette, render and motion rhythm — and it explains why faux-authenticity fails in a single line: "The flaw vocabulary is the style: faux-UGC that looks too clean reads as fake twice over."

What you have now avoided

The vague prompt and the invented spec are the same failure wearing two costumes: a slot the agent filled without evidence. Both are cheap to prevent and expensive to discover after the render.

So: one skill that fixes the prompt, one that picks the model, one that runs the job. seedance-antislop turns the adjectives into camera, light and motion before you pay for them. model-selector or fal-model-guide — the first if you want the field surveyed, the second if you are already on fal.ai and want the endpoint string with the verdict. seedance-ai-avatar if a synthetic presenter is the subject, because it treats the eyes as the thing that gives you away. Those three write; none of them renders, so pair them with something that does. ugc-ads if the output is an ad — it pays for the fifteen seconds itself and will not let a number reach the screen without a source quote behind it. Otherwise whichever runner matches the platform you are actually paying: seedance for Volcengine, higgsfield-generate for Higgsfield — and when a recurring face has to stay the same across shots, higgsfield-soul-id alongside that runner rather than instead of it, since what it returns is an identity reference the runner then consumes. Check the front matter against the record before you install any of them, and if a real person's face is going into the pipeline, read what the document says about consent before you read what it says about lighting.