Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
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 gooseworks-ai/goose-skills --skill create-video-seedance-2-falgit clone --depth 1 https://github.com/gooseworks-ai/goose-skillsWrote 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/gooseworks-ai/goose-skills/create-video-seedance-2-fal)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/create-video-seedance-2-fal"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/create-video-seedance-2-fal/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/gooseworks-ai/goose-skills/create-video-seedance-2-fal"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/create-video-seedance-2-fal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 49 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 111 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Rogue Agent · line 76 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Data Exfiltration · line 81 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
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 | $0.00121 | $0.06255 |
| Opus 5.5 | $0.00048 | $0.02502 |
| Sonnet 5.5 | $0.00024 | $0.01251 |
| Haiku 4.5 | $0.00012 | $0.00626 |
Grade A, and why
create-video-seedance-2-fal 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 2d 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Superseded in the video kit by the video-seedance-2 part (1.3.0, 720p by default; scripted lines use creator-h3). This atom still runs, unchanged, for skills outside the kit.
create-video-seedance-2-fal
⚠️ REQUIRED PREFLIGHT (2026-06-04): every Seedance prompt that names a branded product MUST be paired with a real reference image of that exact product as one of the
--image-refinputs. Text-only product description without a ref invites Seedance to invent geometry that does not match the real SKU. If none exists, take one from the brand's assets, existing ads or product page; clean an occluded one with a gpt-image-2 edit first.
Purpose
Wraps the FAL endpoint bytedance/seedance-2.0/reference-to-video. Pass 1-N image references (product + optional empty setting, never a photoreal face) + a structured prompt → get back a 4-15s clip with ambient audio, at 720p by default.
Seedance refuses photoreal faces, whatever model made them (a fresh Seedream v5 Pro face too, 2026-09-29), and a refused submit still bills. Talking heads and any shot with a realistic face go to creator-h3 / video-h3max (H3). In a GooseWorks custom video Seedance always needs the customer's new yes to its price, saved in the approved review; send that call's --query (below).
Seedance 2.0 renders multiple internal cuts within a single 15s call when the prompt specifies sub-scene structure (e.g. WIDE HOOK / PRODUCT HERO / SIDESTEP / REACTION). This is the architectural win over the older NB2 + i2v multi-keyframe approach — fewer calls, native lip-sync, no Soul ID required.
Use this atom when:
- The shot has no realistic face: a product hero, a product in motion, an empty set
- It needs hard camera motion or an exact product the cheaper H3 route can't hold
- Length 4-15s, vertical 9:16 (or other aspect ratios)
- Multi-image conditioning (product + setting, or several products for a hook scene)
Do NOT use for:
- Any shot with a realistic face (a talking head, a creator, a stranger) →
creator-h3orvideo-h3max -
15s clips → split across multiple calls and stitch (
assembly/stitch-videos-ffmpeg) - Pure cinematic camera moves without an on-screen person → Veo through
create-video-falis often better - Product-only B-roll with no person → text-to-video Seedance, or another model through
create-video-fal - Clips that need video reference for continuity from a prior AI-gen scene → not supported (content_policy_violation)
What ships with it
8 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.
- 2d ago Changed · -7 lines · +1 tokens per session 01765976e913
- 10d ago Changed · +7 lines · +3 tokens per session b3655a9af888
- 15d ago Changed 11eb638f7fc9
- 19d ago Changed · +3 lines cecb5fded64f
- 21d ago Changed · +13 lines e262ba7ad695
- 24d ago Changed 0cc96fc4e884
- 1mo ago First seen · 251 lines · 117 tokens per session scan A 283bf0072072
create-video-seedance-2-fal is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,271 stars, last pushed yesterday), licensed MIT. It adds 121 tokens to every session and 6,255 once invoked, about $0.0005 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 2026-09-03.
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