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model-researcher | ai-video-producer

Skill

model-researcher

From ai-video-producer

Research what fal.ai and Replicate (and optionally other providers — Runway, Kling, Pika, Hedra, ElevenLabs, OpenAI) currently offer for a given workload — text-to-image, text-to-video, image-to-video, lip-sync, voice, upscaling, interpolation — and recommend the best-fit model for the project's brief. Compares quality reputation, price, max duration/resolution, aspect ratio support, and known failure modes. Updates `brief/tools-and-models.md` with the recommendation and rationale on user approval.

$

npx claudepluginhub danielrosehill/claude-code-plugins --plugin ai-video-producer

Popularity

Stars

1

Invocation

How this skill is triggered — by the user, by Claude, or both

Slash command

/ai-video-producer:model-researcher

User invocable

Model invocable

Inline context

Default effort

Context Preview

The summary Claude sees in its skill listing — used to decide when to auto-load this skill

You shortlist and recommend AI models for a given video generation workload.

SKILL.md

52 lines · ~699 tokens

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LanguagePython

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MaintenanceExcellent

Last CommitApr 26, 2026

Actions

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Model researcher

You shortlist and recommend AI models for a given video generation workload.

Inputs

The workload the user wants to fill (e.g. "image-to-video, 9:16, ~6s, photorealistic faces").
brief/creative-brief.md for resolution / aspect / duration / tone constraints.
brief/tools-and-models.md for what's already chosen (don't re-recommend).
Optional: a budget cap per shot from the user.

Sources

fal.ai catalogue — fal.ai/models and the model pages. Use the fetch / web tools.
Replicate explore — replicate.com/explore and individual model pages.
Provider-direct: Runway, Kling, Pika Labs, Hedra, Sync.so, ElevenLabs, OpenAI for models not on fal/replicate.
Recent independent comparisons (Reddit r/aivideo, AInVFX, fxguide) — flag as opinion, not fact.

Always note the date you pulled the information; this space turns over fast.

What to capture per candidate

- Name + provider:
- Workload fit: <how well does it suit the task>
- Quality (reputation): <strong / mid / weak> + 1-line reasoning
- Price: <per-second / per-image / per-call>
- Max output: <duration, resolution>
- Aspect ratios: <list>
- Notable failure modes: <e.g., "drifts on long shots", "weak hands", "no lip-sync">
- Provider: <fal / replicate / direct>
- Date checked: <YYYY-MM-DD>

Output

A short list — 3 to 5 candidates — saved to research/models-<workload>-<YYYY-MM-DD>.md.
A recommendation with reasoning: best overall, cheapest acceptable, highest quality if budget allows.
On user approval, update brief/tools-and-models.md — replace or append the relevant slot. Keep a "Considered but rejected" footer with one line per dropped candidate.

Discipline

Don't trust marketing pages on provider sites for failure modes. Look for community examples and known issues.
Flag pricing where the model bills per second of output vs per second of generation time — they're very different.
If a model is only on the provider's own platform (no fal/replicate proxy), call out the credential implication — the user needs a separate account/key.
Don't recommend a model the project's selected MCP servers can't reach without the user adding a new server. Surface that as a separate decision.