A structured review for sales deals, covering deal health, risks, stakeholders, competition, and next actions. MEDDIC is a sales qualification method used to check whether a deal has the right business case, decision process, and contacts.

Use it to assess a deal, identify risks, review competitive position and stakeholder influence, and create a concrete plan for progressing it.

It turns scattered deal information into a consistent review and exposes gaps that could prevent the sale. It helps reps and managers decide what to do next.

deal-review — Command for Claude Code by jbalbu01

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/jbalbu01/sales-enablement-plugin

agentmods

npx agentmods add commands/jbalbu01/sales-enablement-plugin/deal-review

Made for: Claude Code.

Or install sales-enablement, the plugin that ships this one along with the rest of its 18 skills, 7 commands, 10 MCP servers.

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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/jbalbu01/sales-enablement-plugin/deal-review/github.svg)](https://agentmods.dev/commands/jbalbu01/sales-enablement-plugin/deal-review)

Your own site

<a href="https://agentmods.dev/commands/jbalbu01/sales-enablement-plugin/deal-review"><img src="https://agentmods.dev/badge/commands/jbalbu01/sales-enablement-plugin/deal-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/commands/jbalbu01/sales-enablement-plugin/deal-review"><img src="https://agentmods.dev/badge/commands/jbalbu01/sales-enablement-plugin/deal-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 23 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,903 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00023	$0.01903
Opus 5	$0.00012	$0.00951
Sonnet 5	$0.00005	$0.00381
Haiku 4.5	$0.00002	$0.00190

Measured 10d ago against content hash dde9420a1fe8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

deal-review 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 10d 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.

commands/deal-review.md · 233 lines

How it starts

The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/deal-review

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Run a structured deal review and strategy session. This is the command reps and managers use to stress-test a deal, identify blind spots, and build a concrete action plan to win.

Usage

/deal-review

Then describe the deal, paste CRM data, or upload a pipeline export.

How It Works

┌─────────────────────────────────────────────────────────────────┐
│                       DEAL REVIEW                                 │
├─────────────────────────────────────────────────────────────────┤
│  STANDALONE (always works)                                        │
│  ✓ Describe your deal and I'll run a structured review           │
│  ✓ MEDDIC qualification score with gap analysis                  │
│  ✓ Risk identification and mitigation strategies                 │
│  ✓ Competitive positioning assessment                            │
│  ✓ Stakeholder map and power analysis                            │
│  ✓ Win plan with specific next actions                           │
├─────────────────────────────────────────────────────────────────┤
│  SUPERCHARGED (when you connect your tools)                      │
│  + CRM: Pull deal data, opportunity history, contact records     │
│  + Chat: Find internal discussions about this account            │
│  + Calendar/Email: Meeting context and recent correspondence     │
│  + Knowledge base: Reference existing playbooks and collateral   │
└─────────────────────────────────────────────────────────────────┘

Execution Flow

When this command runs, follow this sequence:

1. Auto-Pull Data (Before Asking Questions)

CRM — Check if CRM tools are available (look for search_crm_objects, get_crm_objects). If available:

Search deals by name/company the user mentioned
Properties: dealname, amount, dealstage, closedate, pipeline, hubspot_owner_id, dealtype, description, notes_last_updated, notes_last_contacted, num_notes, hs_deal_stage_probability, hs_forecast_amount
Pull associated contacts with: firstname, lastname, jobtitle, email, phone, company, hs_lead_status
Pull associated companies with: name, domain, industry, numberofemployees, annualrevenue, description
Map contacts to deal roles (Economic Buyer, Champion, etc.) by job title

Read the full file on GitHub · 233 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.

10d ago First seen · 233 lines · 23 tokens per session scan A dde9420a1fe8

Subscribe to this mod's changes

deal-review is a command published in the GitHub repository jbalbu01/sales-enablement-plugin (14 stars, last pushed 6mo ago), licensed MIT. It adds 23 tokens to every session and 1,903 once invoked, about $0.0001 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 2026-08-30.

Planning claude-plugin clay enablement gong gtm hubspot

churn-prevention

Win/loss interview + churn analysis + retention playbook. Focuses on retention (not acquisition) — where 80% of the current plugin is focused. For B2B SaaS with churn > 5%/month, consulting with renewal rate < 70%, or a recurring business on a plateau.

henriquecaner/hormozi-gtm · 63 tokens

audit

Offer diagnostic via the Value Equation — scores 1-10 on each vector (Dream Outcome, Probability, Time Delay, Effort), pinpoints the critical bottleneck, proposes the top 3 concrete levers, and rewrites the offer in 1 paragraph. Recommended prerequisite before LP and script.

henriquecaner/hormozi-gtm · 60 tokens

client-onboarding

The client's first 30 days after the sale. A structured touch cadence, early quick wins to validate the purchase decision, visible value milestones. Cuts early churn (< 30 days) by a typical 40-60%. For consulting, agencies, cohort programs, mid-market B2B SaaS.