Live Product Lineup

v1.0.0

Plan the optimal product reveal order for TikTok Live sessions based on price anchoring, audience warmup, and peak engagement timing.

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byLeroyCreates@leooooooow

Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for leooooooow/live-product-lineup.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Live Product Lineup" (leooooooow/live-product-lineup) from ClawHub.
Skill page: https://clawhub.ai/leooooooow/live-product-lineup
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

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openclaw skills install live-product-lineup

ClawHub CLI

Package manager switcher

npx clawhub@latest install live-product-lineup
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OpenClawOpenClaw
Benign
high confidence
Purpose & Capability
Name/description ask for sequencing Live product reveals; SKILL.md only requires session length, product list, goals, and optionally historical Live metrics — all directly relevant to producing a timed rundown. No unrelated credentials, binaries, or config paths are requested.
Instruction Scope
Runtime instructions explain how to generate minute-by-minute rundowns and host cue sheets and explicitly state limitations (no real-time viewer pulls). The skill does not instruct the agent to read system files, environment variables, or call external endpoints beyond using user-provided inputs.
Install Mechanism
No install spec and no code files — instruction-only skill. This minimizes disk/network installation risk; nothing will be downloaded or executed by the installer.
Credentials
Requires no environment variables, credentials, or system config paths. Requested inputs (product data, goals, optional historical analytics) are proportional to the feature set.
Persistence & Privilege
always is false (not forced into every agent run) and the skill does not request persistent system-wide changes or access to other skills' configs. Agent autonomous invocation remains possible (platform default) but is not excessive given the skill's benign scope.
Assessment
This skill appears internally consistent. Before using it, avoid pasting sensitive credentials into its prompts; the only sensitive items you might provide are historical viewer analytics or proprietary margin/stock figures — share only what you're comfortable exposing. If a future version adds installs, URLs, or asks for API keys (TikTok developer keys, cloud storage, etc.), re-evaluate before granting them.

Like a lobster shell, security has layers — review code before you run it.

latestvk97fptf1d0h10crr2mpnn1gf0d84vrtw
78downloads
0stars
1versions
Updated 2w ago
v1.0.0
MIT-0

Live Product Lineup

A TikTok Live session with the wrong product order leaves money on the table even when GMV is high — the hero product drops before the audience has warmed up, the cheap impulse item lands after the expensive anchor, and the replenishment SKU never gets its moment. This skill designs a minute-by-minute product reveal plan that uses price anchoring, viewer accumulation curves, and category momentum to squeeze more conversions from every hour you go live.

Use when

  • A TikTok Shop seller is planning a 60-to-180 minute Live and asks "which product should I show first, and when do I drop the hero?"
  • A brand team wants to sequence a launch Live around a new hero SKU and fill the surrounding slots with existing catalog
  • A Live host or agency needs a structured rundown with on-air cues, price reveal timing, coupon pushes, and audience-call-to-action moments
  • A seller is debriefing a low-performing Live and wants to diagnose whether the lineup ordering — not the products themselves — was the problem

What this skill does

Takes the planned Live duration, the product catalog for the session, target audience, and (optionally) historical Live analytics, and produces a time-coded rundown that distributes products across the Live based on three principles: warmup (low-commitment, high-curiosity SKUs in the first 15 minutes while the audience accumulates), anchor (the highest-margin hero drops at peak concurrent viewer count), and retention (replenishment and discount SKUs in the tail to keep late-joiners converting). It assigns each slot a talk-track angle, CTA, and the exact moment to push the pinned product link.

Inputs required

  • Live session length and start time (required): so the rundown can map to actual clock minutes, not just relative offsets
  • Product list with prices, margins, stock levels (required): needed to route anchor vs. impulse vs. replenishment roles
  • Goal of the session (required): launch hype, GMV maximization, inventory clear-out, or follower growth — each reshapes the lineup
  • Historical Live data (optional): past concurrent-viewer curves, average watch time, best-performing SKUs
  • Known audience demographic or past comment themes (optional): helps tune talk-track angles

Output format

A structured rundown in three layers. The minute-by-minute schedule lists every product, its role (warmup, anchor, impulse, replenishment), the planned reveal window, and the price or coupon moment. The host cue sheet gives each slot a spoken hook, a demo beat, and a call-to-action script tailored to the audience state at that minute. The contingency block lists two or three backup SKUs to pull in if concurrent viewers drop below a threshold, plus the rules for extending or cutting segments in real time.

Scope

  • Designed for: TikTok Shop sellers, Live hosts, MCN agencies, and brand teams running their own Live channels
  • Platform context: optimized for TikTok Shop Live; principles adapt to Shopee Live, Lazada LiveStream, and Amazon Live with platform-specific notes
  • Language: English

Limitations

  • Does not pull real-time viewer counts; rundown assumes a typical concurrency curve unless you provide historical data
  • Cannot predict algorithm-driven viewer surges from For You page pushes
  • Host charisma and product demo quality remain the dominant GMV drivers — lineup ordering is a multiplier, not a substitute

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