Qbr Planning

Organization

LeadMagic

qbr-planning

Plan and execute Quarterly Business Reviews — value documentation, ROI presentation, executive alignment, and expansion roadmap. Use when preparing for QBRs, business reviews with customers, or executive stakeholder meetings. Triggers on: "QBR", "quarterly business review", "executive business review", "customer review", "value review", or any request about strategic customer meetings.

Overview

Publisher	LeadMagic
Repository	`gtm-skills`
Skill name	`qbr-planning`
Stars	72
Forks	18
Bundled files	3
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by LeadMagic on GitHub. Read the source before you install it.

Installation

Install the Qbr Planning AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/LeadMagic/gtm-skills/tree/main/skills/customer-success/qbr-planning
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/LeadMagic/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/customer-success/qbr-planning .claude/skills/qbr-planning

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qbr Planning in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Qbr Planning on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Qbr Planning is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

QBR Planning

Overview

A QBR is not a status update — it is a strategic alignment meeting that demonstrates value delivered, aligns on future priorities, and sets the stage for expansion. This skill builds QBR templates that executives want to attend because they see clear ROI from the relationship.

When to Use

"Plan a QBR"
"Prepare for an executive business review"
"Build a QBR deck"
"Document value for a customer"
"Structure a quarterly review"

Authoritative Foundations

Gainsight QBR methodology: QBRs should cover value realized, value roadmap, and strategic alignment — not product updates. Winning by Design Bowtie metrics: frame expansion within the customer's lifecycle, not your quota.

Step-by-Step Process

Phase 1: Pre-QBR Preparation

Gather: usage metrics, support history, NPS/CSAT trends, expansion signals, stakeholder map, competitor activity. Document value delivered in their language — not your feature names.

Phase 2: QBR Structure

Executive Summary — value delivered this quarter, in their words
Metrics Review — adoption, usage, outcomes achieved vs. goals
Success Stories — wins since last QBR
Strategic Alignment — how this maps to their company priorities
Roadmap Preview — what's coming, how it benefits them
Expansion Opportunity — logical next steps, not a sales pitch
Action Items — mutual commitments for next quarter

Phase 3: Delivery

60 minutes: 40 min presentation, 20 min discussion. Send deck 24 hours in advance. Include their logo, their metrics, their language. This is about them — not you.

Output Format

QBR deck template, value documentation template, stakeholder map, and pre-QBR checklist.

Quality Check

Before delivering, verify:

All required sections are complete
Output matches the user's stated need
Named frameworks are cited for key recommendations
No vague claims — every recommendation has a specific action
Deliverable is ready for operational use, not just conceptual

Common Pitfalls

Product update disguised as QBR — executives don't care about feature releases. They care about business outcomes.
Surprise expansion ask — if expansion is first mentioned at the QBR, it's too late. Plant the seed months before.
Your metrics, not theirs — "you logged in 50 times" means nothing. "Your team reduced time-to-close by 40%" means everything.
No action items — a QBR without mutual commitments is a meeting, not a milestone.

Execution Artifacts

references/framework-notes.md — named frameworks, citation anchors, and operating assumptions
templates/output-template.md — copy-paste deliverable structure for the user
scripts/check-output.py — local checklist validator for required sections This skill includes lightweight artifacts the agent can load on demand: Use the artifacts when the user asks for an implementation-ready deliverable, a repeatable workflow, or a quality check rather than generic advice.

Implementation Depth

Use this section when the user asks for a finished asset, not a high-level explanation.

Diagnostic Questions

What is the primary motion: founder-led, sales-led, product-led, partner-led, or lifecycle-led?
Which ICP tier is the output for: small business, mid-market, enterprise, or mixed?
What proof is available today: customer stories, usage data, third-party validation, screenshots, or none?
What system will execute the work: CRM, sequencer, warehouse, support desk, product analytics, or manual workflow?
What decision will the user make from this output: launch, prioritize, route, rewrite, score, coach, or measure?

Framework Application

Map the recommendation explicitly to the named frameworks in this skill:

Gainsight QBR Methodology: apply only the part that directly improves the requested deliverable.
Winning by Design Bowtie Metrics: apply only the part that directly improves the requested deliverable.
Gainsight — Customer Success Maturity Model: apply only the part that directly improves the requested deliverable.

Deliverable Standard

A strong output from this skill includes:

A crisp diagnosis of the current situation
A recommended path with tradeoffs, not a generic list
A concrete artifact the user can use immediately: table, script, checklist, scorecard, sequence, dashboard spec, or implementation plan
A measurement plan with leading and lagging indicators
Risks and edge cases called out before execution

Adaptation Rules

For small business: reduce complexity, shorten time-to-value, and prioritize owner/operator clarity.
For mid-market: include workflow ownership, handoffs, integrations, and enablement assets.
For enterprise: include governance, risk, procurement, stakeholder mapping, and proof requirements.

Related Skills

cs-playbooks: Full CS operating system
customer-marketing: Case studies and advocacy from QBRs
expansion-selling: Turn QBR insights into expansion

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Qbr Planning AI skill do?

Why use Qbr Planning on TypingMind?

Because you install it once and use it with any model. Qbr Planning is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Qbr Planning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeadMagic/gtm-skills/tree/main/skills/customer-success/qbr-planning. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Qbr Planning?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Qbr Planning?

As many as you like. As long as a model supports skills, you can use Qbr Planning with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Qbr Planning AI skill free?

Yes. It is published on GitHub by LeadMagic under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

QBR Planning

Overview

When to Use

Authoritative Foundations

Step-by-Step Process

Phase 1: Pre-QBR Preparation

Phase 2: QBR Structure

Phase 3: Delivery

Output Format

Quality Check

Common Pitfalls

Execution Artifacts

Implementation Depth

Diagnostic Questions

Framework Application

Deliverable Standard

Adaptation Rules

Related Skills

Bundled files

Frequently asked questions

Related AI Skills

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