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 danielrosehill/Claude-AI-Video-Producer-Plugin --skill text-to-image-to-videogit clone --depth 1 https://github.com/danielrosehill/Claude-AI-Video-Producer-PluginWrote 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/danielrosehill/claude-ai-video-producer-plugin/text-to-image-to-video)<a href="https://agentmods.dev/skills/danielrosehill/claude-ai-video-producer-plugin/text-to-image-to-video"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-ai-video-producer-plugin/text-to-image-to-video/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/danielrosehill/claude-ai-video-producer-plugin/text-to-image-to-video"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-ai-video-producer-plugin/text-to-image-to-video.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 | $0.00061 | $0.00631 |
| Opus 5 | $0.00030 | $0.00316 |
| Sonnet 5 | $0.00012 | $0.00126 |
| Haiku 4.5 | $0.00006 | $0.00063 |
Grade A, and why
text-to-image-to-video 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 12d 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Text → Image → Video Pipeline
Two-stage chain. Stage 1 produces a still you can iterate on cheaply; stage 2 animates the chosen still.
When to use
- The shot needs a specific composition or character pose locked before motion.
- Direct text-to-video drifts off-prompt; this gives you an anchor frame.
- Cost: one image gen + one video gen per accepted take. Iterating on the still is cheap; iterating on motion is not — get the still right first.
Inputs
- A shot brief from
scripts/storyboards/NN-*.md(visual prompt seed, duration, character refs). - Model selection from
brief/tools-and-models.md(text-to-image model + image-to-video model). - Optional: character reference image from
characters/<name>.md.
Steps
- Compose image prompt. Combine the shot's visual seed + character seed prompt + style/vibe from
brief/creative-brief.md. Show it to the user before generating. - Generate still. Call the configured text-to-image model (typically via Fal or Replicate MCP). Save to
generation/text-to-image/NN-shortname-vN.png. Save prompt + model + seed togeneration/prompts/NN-shortname-vN.md. Append tologs/production-log.md. - Iterate on still. Show the image. If the user wants to refine, loop on step 2 with an incremented
vN. Don't proceed until the user approves a still. - Compose motion prompt. Describe the motion — camera move, subject action, duration. Pull duration from the shot brief.
- Animate. Call the configured image-to-video model with the approved still as input. Save to
generation/image-to-video/NN-shortname-vN.mp4. Save the motion prompt and parameters togeneration/prompts/NN-shortname-vN-motion.md. Log it. - Surface result. Show the clip. Offer: accept (copy to
clips/raw/and suggest/promote-take), retry motion, or go back to step 2 (new still).
Common gotchas
- Aspect ratio mismatch. The still must match the target video aspect from
brief/creative-brief.md. Set it explicitly in the image gen call; don't rely on the model's default. - Character drift across shots. Always pass the character's seed prompt verbatim. Consider using a character reference image as a control input if the model supports it.
- Motion model resolution caps. Some image-to-video models downscale. If final output needs 4K, pair with the upscale-and-interpolate skill.
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.
- 12d ago First seen · 36 lines · 61 tokens per session scan A acdea5b1a4bd
text-to-image-to-video is a skill published in the GitHub repository danielrosehill/Claude-AI-Video-Producer-Plugin (4 stars, last pushed 4mo ago), licensed MIT. It adds 61 tokens to every session and 631 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…