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.
git clone --depth 1 https://github.com/stockvalue/genspark-mcpWrote 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/commands/stockvalue/genspark-mcp/video)<a href="https://agentmods.dev/commands/stockvalue/genspark-mcp/video"><img src="https://agentmods.dev/badge/commands/stockvalue/genspark-mcp/video.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.00502 |
| Opus 5 | $0.00011 | $0.00251 |
| Sonnet 5 | $0.00004 | $0.00100 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
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 6d 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.
What it actually says
mcp__genspark-video-mcp__generate_video を使い、下の指示でGensparkのAI動画を生成してください。
- ユーザーのシーン説明を
promptに渡す。 - 明示された希望だけを渡す。未指定の設定は省略し、Gensparkの現在の既定値を使う。
model、aspect_ratio(9:16/16:9)、resolution(720p/1080p/4k)、duration、countaudio、fast_mode、tier(Lite/スタンダード/Ultra)、auto_prompt- image-to-videoモデルでは
imagesに利用者のローカル絶対パスを渡す。開始・終了フレーム型は順序を保つ。
- 指定できる設定が不明なときだけ
mcp__genspark-video-mcp__list_video_optionsを使って実物のUIから確認する。これは生成しないが、取得に時間がかかる。 not_logged_inなら、AI自身はログイン操作を行わず、利用者本人に次を実行してもらう。
パスワード、2段階認証コード、cookieを要求・表示・保存しない。cd ~/Genspark-MCP && .venv/bin/python login.pybusyなら少し待って再実行する。別のプロセスを広範囲に終了させるコマンドは実行しない。- 完了後は、本数・実際に適用された設定・保存先を簡潔に報告する。生成には数分以上かかることがある。
指示: $ARGUMENTS
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.
- 6d ago First seen · 24 lines · 22 tokens per session scan A 79531201d65f
video is a command published in the GitHub repository stockvalue/genspark-mcp (3 stars, last pushed 29d ago), licensed MIT. It adds 22 tokens to every session and 502 once invoked, about $0.0001 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 commands, from other repositories
imagegen
Generate an image (or a coherent set of images) from a prompt — /imagegen " ". Or run /imagegen setup to verify the install. Routes through the imagegen MCP server (Codex CLI / gpt-image).
imagine-pro
Generate an AI image with ImaginePro using best practices and intelligent guidance.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.