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 BlackBeltTechnology/pi-agent-dashboard --skill veo-showreel-production-kitgit clone --depth 1 https://github.com/BlackBeltTechnology/pi-agent-dashboardWrote 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/blackbelttechnology/pi-agent-dashboard/veo-showreel-production-kit)<a href="https://agentmods.dev/skills/blackbelttechnology/pi-agent-dashboard/veo-showreel-production-kit"><img src="https://agentmods.dev/badge/skills/blackbelttechnology/pi-agent-dashboard/veo-showreel-production-kit/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/blackbelttechnology/pi-agent-dashboard/veo-showreel-production-kit"><img src="https://agentmods.dev/badge/skills/blackbelttechnology/pi-agent-dashboard/veo-showreel-production-kit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00039 | $0.00808 |
| Opus 5 | $0.00019 | $0.00404 |
| Sonnet 5 | $0.00008 | $0.00162 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
veo-showreel-production-kit 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 9d 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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
Use when a user has a script/timeline + voiceover and wants to generate a consistent, professional AI video (Veo) where each fragment can be rendered separately/in parallel yet stays visually coherent and is easy to revise. Especially for trade-show/booth showreels.
Procedure
- Research the target context (e.g., trade-show showreel best practices) and Veo prompting + consistency features (reference images, first/last-frame, seed, enhance_prompt=false). Write a research/strategy doc.
- Write a STYLE BIBLE = global consistency anchor: single location/world, color palette, verbatim STYLE LOCK and AUDIO LOCK sentences, recurring object/character descriptions, a global NEGATIVE prompt, brand/compliance rules, and fixed reproduction settings (model, 16:9, 4K/24fps, constant seed, enhance_prompt=false).
- Split the timeline into <=8s render units (Veo max clip ~8s); long scenes become A/B sub-shots. Mark each boundary as hard-cut (parallel) or SEAMLESS (chain last-frame of A as first-frame of B).
- Generate per-shot markdown via a data-driven Python script: each file has the 7-layer prompt (camera, subject, action, environment, lighting, STYLE LOCK, AUDIO LOCK), an assembled Full Veo prompt (~110-170 words), the negative prompt, continuity notes, and repro settings. Also emit one combined VIDEO_MASTER.md.
- Generate AI storyboard sketches with nano-banana (Gemini image model): a master world-anchor frame + one sketch per cut. Use these as Veo reference/first-frame images. Run with limited concurrency (ThreadPoolExecutor max_workers=3) over a sketch_prompts.json.
- Audio policy: instruct Veo 'no spoken dialogue, no voiceover, no song vocals' so it only makes ambient SFX; the official VO + music are added in post.
- Write an index README explaining read order, per-slice render steps, assembly, and how to revise one beat (re-render only that slice with same seed+anchor).
Pitfalls
- Veo clips are ~8s max — never author a single >8s render unit; split into A/B and chain frames.
- Paraphrasing the style block per shot causes drift; the STYLE LOCK and AUDIO LOCK must be byte-identical across every prompt.
- Without a shared reference image attached to every generation, parallel slices diverge — always attach the world-anchor.
- Let Veo invent narration if you don't explicitly forbid speech in the audio line.
- For neutral/defense content add 'no flags, no national insignia, no real weapons, no brand logos' to the negative prompt and reserve a clean empty center for the logo in the final shot.
- nano-banana CLI: npx -y @the-focus-ai/nano-banana "" --output file.png ; needs GEMINI_API_KEY; describe 16:9 in the prompt (square by default).
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.
- 9d ago First seen · 32 lines · 39 tokens per session scan A 075f501c3f61
veo-showreel-production-kit is a skill published in the GitHub repository BlackBeltTechnology/pi-agent-dashboard (280 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 808 once invoked, about $0.0002 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 2026-09-03.
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