video-model-selection

video-model-selection is a skill for Claude Code, Codex from manishiitg/coding-agent-loop. It costs 93 tokens per session (4,996 once invoked), scanned A, original, MIT.

A guide for choosing an AI video model for one shot, based on the shot’s input type, consistency needs, length, audio, and cost.

In plain words
What is it for?
It helps select between text-to-video and image-to-video models, and compare support for consistent subjects, longer clips, native audio, and pricing.
Why use it?
It helps avoid using a model that cannot meet the shot’s requirements or creates unnecessary cost. It is used before generation and when those requirements change.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps select between text-to-video and image-to-video models, and compare support for consistent subjects, longer clips, native audio, and pricing.

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Install with agentmods
npx agentmods add skills/manishiitg/coding-agent-loop/video-model-selection
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add manishiitg/coding-agent-loop --skill video-model-selection
Clone the repo
git clone --depth 1 https://github.com/manishiitg/coding-agent-loop

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for video-model-selection

README.md
[![agentmods](https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/video-model-selection/github.svg)](https://agentmods.dev/skills/manishiitg/coding-agent-loop/video-model-selection)
Your own site
<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/video-model-selection"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/video-model-selection/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for video-model-selection

Your own site · 80×15
<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/video-model-selection"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/video-model-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,996 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00093 $0.04996
Opus 5.5 $0.00037 $0.01998
Sonnet 5.5 $0.00019 $0.00999
Haiku 4.5 $0.00009 $0.00500

Measured 1mo ago against content hash 714b9481b047, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

video-model-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agent_go/internal/videoproduct/skills/video-model-selection/SKILL.md · 360 lines

How it starts

The opening of the file, as written. The whole thing — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Choosing a generation model

fal-ai and google-ai teach how to call a model once you've picked one. This skill teaches how to pick it. Read references/model-capabilities.md whenever quoting model duration, resolution, aspect ratio, audio, language, or reference support. Read references/cost-guidance.md whenever the user compares models, asks about price, or must approve a paid plan. Neither this skill nor either of those pins a model catalog or a "best model for X" table -- both providers' catalogs, capabilities, and pricing change independently and on their own schedule, and a fixed ranking here would go stale exactly the way a fixed model ID would.

Resolve production-level unknowns before the first paid call

video-creation already asks one compact question only when a missing choice would substantially change the result or cost (see "Understand the request"). A long-form AI-generated production has its own unresolved-question shape, and getting it wrong is more expensive to unwind than a typography choice: a wrong shot is a paid regeneration, not a free re-render. Before the first generation call, resolve whatever the brief hasn't already answered:

  • Provider/model preference. Does the user already know they want fal.ai, Google, or a specific model? If not, state the tradeoff briefly (see "Decide by requirement" below) rather than picking silently on their behalf for a paid call.
  • Character/subject consistency requirements. Does any character, presenter, or product need to recognizably recur across multiple shots? This determines which models are even viable (see video-cinematography's consistency section) and must be known before the first shot of that subject is generated, not discovered after three independently-drifting attempts.
  • Shot count vs. budget. A true long-form (8-15+ minute) production is assembled from dozens of separately-generated clips. Do the arithmetic for the actual model in hand rather than quoting a fixed number: a 10-minute piece is 600 seconds of finished runtime, so a model producing 5-second clips needs ~120 calls, one producing 15-second clips ~40, and one producing 30-second clips ~20 -- before accounting for re-prompts, which the regeneration tolerance below multiplies on top. Per-call duration is therefore a budget decision as much as a creative one. If the user hasn't stated a cost ceiling or a maximum acceptable number of paid generation calls, ask once, compactly, showing the call-count arithmetic for the model actually being considered -- do not silently generate an open-ended number of variants across a production this large.
  • Regeneration tolerance. If a shot doesn't match the brief on the first attempt, how many re-prompts is reasonable before stopping to ask the user rather than continuing to spend on the same shot? State a default (e.g. two re-prompts) and get confirmation rather than assuming unlimited budget for one stubborn shot.

Read the full file on GitHub · 360 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +2 lines 714b9481b047
  2. 1mo ago Changed · +1 lines 3ffcdf4ada1e
  3. 1mo ago First seen · 357 lines · 93 tokens per session scan A ed748c50655b

Subscribe to this mod's changes

video-model-selection is a skill published in the GitHub repository manishiitg/coding-agent-loop (13 stars, last pushed 19d ago), licensed MIT. It adds 93 tokens to every session and 4,996 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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