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harrylabsj

Live Commerce Showrunner

by haidong · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
90
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1
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Install in OpenClaw
/install live-commerce-showrunner
Description
Plan and run a live commerce session across Douyin, TikTok Shop, Taobao Live, Amazon Live, or similar channels. Use when a team needs a run of show, offer la...
README (SKILL.md)

Live Commerce Showrunner

Overview

Use this skill to turn a rough live selling idea into an operator-ready show brief. It helps structure the show objective, offer flow, host responsibilities, moderation plays, risk controls, and debrief checklist.

This MVP is heuristic. It does not connect to live commerce dashboards, inventory systems, ad platforms, comment feeds, or payment tools. It relies on the user's provided channel context, product priorities, offer assumptions, and team constraints.

Trigger

Use this skill when the user wants to:

  • build a run of show for a live commerce session
  • plan a launch, promo, clearance, or education-led livestream
  • brief hosts, moderators, operators, and product assistants
  • design the offer ladder, urgency moments, and comment-conversion plays
  • create a pre-show checklist and post-show debrief template

Example prompts

  • "Help me plan a 45-minute Douyin live for our new skincare launch"
  • "Create a run of show for a clearance livestream on TikTok Shop"
  • "What should our host, moderator, and ops lead each do during the live?"
  • "Build a backup plan for low traffic and inventory risk during a live selling event"

Workflow

  1. Capture the show goal, channel, lead products, and commercial constraints.
  2. Choose the likely show type, such as launch, promo, education, or guest session.
  3. Build the run of show with opening hook, proof moments, offer stacking, and close.
  4. Define host, moderator, ops, and product support responsibilities.
  5. Return a markdown show brief with risk controls and debrief guidance.

Inputs

The user can provide any mix of:

  • live channel such as Douyin, TikTok Shop, Taobao Live, Amazon Live, or Instagram Live
  • show objective such as launch conversion, stock clearance, education, or audience growth
  • product priorities, bundles, pricing, or coupon assumptions
  • host profile, guest involvement, moderator support, and crew availability
  • planned show length, promo calendar, and traffic expectations
  • risk notes such as low stock, compliance pressure, weak script confidence, or technical concerns

Outputs

Return a markdown show brief with:

  • show strategy summary
  • run of show by segment
  • offer and merch plan
  • host and crew checklist
  • comment moderation and conversion plays
  • backup plan for common failure modes
  • debrief checklist with learning questions

Safety

  • Do not claim access to live traffic, GMV, comment, or stock systems.
  • Do not promise sales outcomes from the show plan.
  • Compliance, pricing, and inventory decisions remain human-approved.
  • Downgrade certainty when the user provides weak offer, product, or traffic detail.

Best-fit Scenarios

  • brand or marketplace teams running founder-led or host-led live selling sessions
  • operators who need structure before a launch or promo livestream
  • smaller teams that do not yet have a dedicated live commerce producer

Not Ideal For

  • live in-session control or real-time comment moderation
  • guaranteed forecasting of GMV, traffic, or conversion
  • regulated categories that require formal legal review before scripting

Acceptance Criteria

  • Return markdown text.
  • Include run of show, team roles, risk controls, and debrief sections.
  • Make the no-live-data limitation explicit.
  • Keep the brief practical for a host, moderator, and ecommerce operator.
Usage Guidance
This skill appears coherent and limited to producing operator-ready show briefs from user input. Before installing, consider: (1) confirm your platform enforces no-network rules for instruction-only skills if you want absolute assurance that no external calls can occur; (2) do not paste any credentials, proprietary inventory dumps, or private customer data into the prompt—the skill doesn't need them and a best practice is to keep sensitive data out of skill inputs; (3) review handler.py if you want to be extra cautious—it is short, local, and contains only deterministic text processing. Overall, it looks appropriate for planning live-commerce sessions.
Capability Analysis
Type: OpenClaw Skill Name: live-commerce-showrunner Version: 1.0.0 The 'live-commerce-showrunner' skill is a template generator for planning livestream selling events. The Python code (handler.py) uses simple keyword matching and string manipulation to generate a markdown show brief based on user input, with no network access, file system interaction, or use of dangerous functions like eval/exec. The instructions in SKILL.md are well-defined and include explicit safety disclaimers regarding data access and human oversight.
Capability Tags
cryptocan-make-purchases
Capability Assessment
Purpose & Capability
The name/description (run-of-show, crew checklists, risk controls) matches the SKILL.md and handler.py implementation. The code performs only text normalization, heuristic classification, and markdown rendering—no platform APIs, cloud services, or unrelated system access are requested.
Instruction Scope
SKILL.md limits scope to generating a show brief from user-provided context and explicitly forbids claiming access to live traffic, inventory, or comment systems. The instructions do not ask the agent to read files, environment variables, or transmit data to external endpoints.
Install Mechanism
No install specification is provided (instruction-only). The included handler.py and tests are simple local Python code with no external downloads or extraction steps.
Credentials
The skill declares no required environment variables, credentials, or config paths. The implementation does not read env vars or require secrets.
Persistence & Privilege
always is false and the skill does not attempt to persist configuration, modify other skills, or require elevated/system-wide privileges. It runs as a simple renderer based on input.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install live-commerce-showrunner
  3. After installation, invoke the skill by name or use /live-commerce-showrunner
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of live-commerce-showrunner skill. - Provides a structured workflow to plan live commerce sessions for platforms like Douyin, TikTok Shop, Taobao Live, and Amazon Live. - Returns a markdown show brief including run of show, offer ladder, team roles, risk controls, and debrief checklist. - Designed for teams needing show structure without access to live platform or performance data. - Emphasizes practical planning, clear role division, and documented contingencies for common risks. - Does not connect to live data or make sales claims; suitable for pre-live session planning.
Metadata
Slug live-commerce-showrunner
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Live Commerce Showrunner?

Plan and run a live commerce session across Douyin, TikTok Shop, Taobao Live, Amazon Live, or similar channels. Use when a team needs a run of show, offer la... It is an AI Agent Skill for Claude Code / OpenClaw, with 90 downloads so far.

How do I install Live Commerce Showrunner?

Run "/install live-commerce-showrunner" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Live Commerce Showrunner free?

Yes, Live Commerce Showrunner is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Live Commerce Showrunner support?

Live Commerce Showrunner is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Live Commerce Showrunner?

It is built and maintained by haidong (@harrylabsj); the current version is v1.0.0.

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