qbr-prep

qbr-prep is a skill for Claude Code, Codex from quivly/skills. It costs 54 tokens per session (741 once invoked), scanned A, original, MIT.

A preparation skill for quarterly or executive business reviews, meetings about delivered value, product usage, customer health, and future plans. QBR means quarterly business review; EBR means executive business review.

In plain words
What is it for?
Use it to prepare a review with usage trends, delivered value, unresolved issues, commercial health, roadmap requests, and a forward plan.
Why use it?
It connects usage evidence to business outcomes, surfaces gaps honestly, and organizes renewal or next-quarter discussions around the customer's priorities.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/quivly/skills/qbr-prep
Any agent
npx skills add quivly/skills --skill qbr-prep
Clone the repo
git clone --depth 1 https://github.com/quivly/skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for qbr-prep

README.md
[![agentmods](https://agentmods.dev/badge/skills/quivly/skills/qbr-prep.svg)](https://agentmods.dev/skills/quivly/skills/qbr-prep)
Your own site
<a href="https://agentmods.dev/skills/quivly/skills/qbr-prep"><img src="https://agentmods.dev/badge/skills/quivly/skills/qbr-prep.svg" alt="Measured on agentmods" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 741 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.00741
Opus 5 $0.00027 $0.00370
Sonnet 5 $0.00011 $0.00148
Haiku 4.5 $0.00005 $0.00074

Measured 6d ago against content hash 75b1bda9909a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

qbr-prep 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 6d 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.

customer-engineering/qbr-prep/SKILL.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

QBR Prep

You build the content for a business review that earns its meeting slot: value proven with their numbers, honest gaps, and a forward plan worth an executive's time.

Core principle: usage is evidence; outcomes are the story — a QBR that reads as a usage report wastes an executive hour.

Workflow

  1. Establish the baseline: what success criteria were set at kickoff or the last QBR (search-calls, review prior QBR notes). The QBR's spine is "you said X mattered — here's where X stands".
  2. Build the value story (get-usage, trend-analysis): quarter-over-quarter usage narrative, adoption milestones hit, workflows now running through the product. Convert usage into outcome language wherever possible (time saved, revenue influenced, risk reduced).
  3. Be honest about gaps: under-adopted capabilities they're paying for, stalled initiatives, unresolved asks from last quarter. Bring each with a proposed fix.
  4. Check commercial posture (get-contracts, get-revenue, get-health-score): renewal distance, growth precedent, health — this calibrates how forward the renewal/expansion conversation should be.
  5. Draft the forward plan: next quarter's priorities in their language, our asks of them (stakeholder time, data access, rollout support), and any expansion motion that genuinely serves their goals.

Output Format

QBR: {Customer} — {quarter}

Executive summary slide — 3 bullets: value headline, health headline, next-quarter headline

Value delivered — success criteria vs. actuals, with numbers and trend charts to pull

Honest gaps — each with a proposed fix and owner

Next quarter plan — priorities, milestones, mutual commitments

Renewal/expansion positioning — how forward to be, and the specific motion if any

Meeting design notes — who must attend, 2-3 discovery questions to ask live, topics to pre-wire before the room

Guidelines

  • A QBR that reads as a usage report wastes an exec meeting; usage is evidence, outcomes are the story.
  • Bring last QBR's commitments and score ourselves publicly — credibility compounds.
  • If value evidence is thin, the QBR's honest job is realignment, not celebration — say so in the prep.
  • Pre-wire anything surprising; the QBR room is for alignment, not ambushes.

Read the full file on GitHub · 56 lines

Changes

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.

  1. 6d ago First seen · 56 lines · 54 tokens per session scan A 75b1bda9909a

Subscribe to this mod's changes

qbr-prep is a skill published in the GitHub repository quivly/skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 741 once invoked, about $0.0003 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 2026-08-31.

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