linkedin-launch-video

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---
name: linkedin-launch-video
description: Produce a LinkedIn-native 1:1 (or 16:9) launch video using HyperFrames. Caption-first, sound-off, B2B register, anti-corporate-jargon. Optionally hand off to LinkedIn Ads to launch as a sponsored content campaign with ICP-aligned targeting.
version: "1.0.0"
author: Cogny AI
platforms: [linkedin-ads, hyperframes]
user-invocable: true
argument-hint: "<product URL or short brief>"
allowed-tools:
  - WebFetch
  - WebSearch
  - Bash
  - Read
  - Write
  - Edit
  # Cogny MCP context tree — richer product context than public scraping
  - mcp__cogny__get_context_tree_overview
  - mcp__cogny__browse_context_tree
  - mcp__cogny__read_context_node
  - mcp__cogny__search_context
  # Optional handoff to LinkedIn Ads when the user wants to publish as sponsored content
  - mcp__cogny__linkedin_ads__*
---

# LinkedIn Launch Video

Produce a LinkedIn-native launch video using [HyperFrames](https://hyperframes.heygen.com).

LinkedIn's autoplay is muted, the feed is scrolled in 2-second chunks, and the audience is allergic to either extreme — too corporate (sounds like a 2010 SaaS press release) or too TikTok (looks unprofessional in front of buyers). This skill targets the middle: caption-driven, conversational founder/operator voice, real numbers, no jargon.

## Usage

`/linkedin-launch-video https://example.com/launch` — pulls context from a launch page
`/linkedin-launch-video "we're shipping inbound CRM scoring for B2B sales teams"` — short brief
`/linkedin-launch-video` — interview the user

## Prerequisites

```bash
node --version            # ≥ 22
ffmpeg -version | head -1 # ≥ 6
```

## Steps

### 1. Gather product context AND ICP context

LinkedIn videos die when the audience is "everyone". They work when the first frame names the role.

Try sources in order:

**1a. Local file** — `.agents/product-marketing-context.md` or `.claude/product-marketing-context.md`.

**1b. Cogny MCP context tree** — if the `cogny` MCP server is connected:

```
mcp__cogny__get_context_tree_overview
mcp__cogny__search_context query="ICP" / query="<product>"
mcp__cogny__read_context_node node_id="…"
```

The tree usually contains the actual ICP definition (role, seniority, industry, ARR band) and the most-recent shipped feature — both load-bearing for LinkedIn-grade specificity. Check it before public scraping.

**1c. Public web** — `WebFetch` the URL only if 1a/1b are empty.

Then ask:

1. Who is this for, by **role and seniority**? ("Heads of Demand Gen at B2B SaaS, $5–50M ARR")
2. What is the *expensive* problem they have right now? (cost, time, headcount)
3. What's the proof point — a customer name, a metric, a screenshot, a benchmark?
4. Who is the on-screen voice? (Founder? Operator? Customer? No-face / text-only is also valid on LinkedIn.)
5. What's the conversion goal? (Signup, demo, follow, comment, reshare)

If the role / proof / goal are vague, push back before writing. LinkedIn ROI lives or dies on these three.

### 1.5 Capture brand identity

Make this look like the user's brand. LinkedIn's audience is buyers — generic stock styling reads as "untrusted vendor".

Ask for one of: site URL, repo path with `tailwind.config.*` / CSS variables, an existing `brand-kit.json`, or manual input (3 hex + font + voice). Save the result as `brand-kit.json` in the HyperFrames project root.

For full schema, extraction patterns, and voice → composition mapping see [`references/brand-identity.md`](references/brand-identity.md).

LinkedIn-specific brand notes:

- **Avoid LinkedIn blue (#0a66c2)** as the brand accent — it merges with the platform UI and the video reads as a chrome screenshot.
- **Avoid the same accent the platform overlays use** — LinkedIn places white profile names with drop shadow on the top-15%; ensure your accent reads under that.
- **Display fonts are fine here** (unlike Reddit) — LinkedIn is the channel where serif headlines and editorial typography land best.

The composition template reads `--bg`, `