Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/S3YED/appie-kitnpx agentmods add skills/s3yed/appie-kit/branded-reel-pipelineWrote 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/s3yed/appie-kit/branded-reel-pipeline)<a href="https://agentmods.dev/skills/s3yed/appie-kit/branded-reel-pipeline"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/branded-reel-pipeline/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/s3yed/appie-kit/branded-reel-pipeline"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/branded-reel-pipeline.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 | $0.00183 | $0.02976 |
| Opus 5 | $0.00092 | $0.01488 |
| Sonnet 5 | $0.00037 | $0.00595 |
| Haiku 4.5 | $0.00018 | $0.00298 |
Grade A, and why
branded-reel-pipeline 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 8d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Branded Reel Pipeline
Reusable framework that produces a premium short-form brand reel end to end. It is the
premium layer on top of media/short-form-video-production (ideation, hook,
retention, captions craft).
Use those for the creative craft; use this for the premium branded assembly with real
on-brand visuals and live data.
The proven stack (do not deviate without reason)
- Script — hook-first, retention-structured. Borrow craft from
content-creation(Hook→Tension→Payload→CTA) andshort-form-video-production. Beat table:t | OST | VO. - Visuals + music —
higgsfield-generateskill. GPT Image 2 for hero frames (9:16, 2k, brand palette),sonilo_musicfor the bespoke bed. Frames are premium connective tissue; live screens carry conversion. - Voiceover — ElevenLabs
eleven_v3, stability0.30, default voice idcjVigY5qzO86Huf0OWal("Eric"). Key from environment. Calm-operator delivery. - Live-data capture —
reference/live-data-capture.py(Playwright). Parameterized: URL + auth method + selector / API path. The differentiator: real dashboards, real numbers count up on screen. - Assembly — Remotion with brand-preset tokens (
templates/remotion-template/). Parameterized by aspect ratio. Count-ups, curve-draws, glass device frames, the audio-reactive orb. 1080x1920 @ 30fps for 9:16. - Render + verify —
remotion render→ffprobeconfirms duration/resolution/codec.
Input: the brief
A reel is driven by a brief object:
{
"subject": "Our AI agent that posts daily and tracks every number",
"brand_preset": "my-brand",
"format": "9:16",
"mode": "gated",
"live_data_source": {
"url": "https://your-dashboard.example.com/api/analytics?days=30",
"auth": { "method": "session_token", "mint": "node scripts/mint-session-token.js" },
"capture": "json",
"selector": null
},
"script": null
}
subject(required): what the reel is about.brand_preset(required): a dir underpresets/matching your brand.format:9:16(default) |1:1|16:9.mode:gated(default) |auto.live_data_source(optional):{ url, auth, capture: json|screenshot, selector }.script(optional): pre-written beat table; if null the pipeline writes one in Stage 1.
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.
- 8d ago First seen · 202 lines · 183 tokens per session scan A e11222a405d8
branded-reel-pipeline is a skill published in the GitHub repository S3YED/appie-kit (9 stars, last pushed 16d ago), licensed MIT. It adds 183 tokens to every session and 2,976 once invoked, about $0.0009 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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