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.
npx skills add manishiitg/coding-agent-loop --skill video-model-selectiongit clone --depth 1 https://github.com/manishiitg/coding-agent-loopWrote 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.
[](https://agentmods.dev/skills/manishiitg/coding-agent-loop/video-model-selection)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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.
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.
- 1mo ago Changed · +2 lines 714b9481b047
- 1mo ago Changed · +1 lines 3ffcdf4ada1e
- 1mo ago First seen · 357 lines · 93 tokens per session scan A ed748c50655b
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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