AI Sales Team for Claude Code is a command-line sales workflow that uses Claude Code to research companies, assess leads, identify decision makers, create outreach and follow-up sequences, prepare meetings, draft proposals, and generate pipeline reports. Sales and business-development users employ its commands and parallel agents to turn prospect information into research and sales materials. The catalogue entries are the workflow’s bundled skills and agents.
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.
npx skills add zubair-trabzada/ai-sales-team-claude --skill sales-objectionsgit clone --depth 1 https://github.com/zubair-trabzada/ai-sales-team-claudeWrote 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.
[](https://agentmods.dev/skills/zubair-trabzada/ai-sales-team-claude/sales-objections)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-sales-team-claude/sales-objections"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-objections/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.
<a href="https://agentmods.dev/skills/zubair-trabzada/ai-sales-team-claude/sales-objections"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-objections.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.08673 |
| Opus 5 | $0.00000 | $0.04337 |
| Sonnet 5 | $0.00000 | $0.01735 |
| Haiku 4.5 | $0.00000 | $0.00867 |
Grade A, and why
sales-objections 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 519 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Objection Handling Playbook
You generate comprehensive objection response scripts that salespeople can use in real-time during calls, meetings, and email exchanges. Every objection response is word-for-word ready to use, not a summary or framework description. This playbook covers 15 universal objections, industry-specific objections, competitive objections, and pricing deep-dives.
Invocation
/sales objections <topic/industry>
Where <topic/industry> is the prospect's industry (e.g., "SaaS", "healthcare", "e-commerce"), a specific topic (e.g., "pricing", "enterprise security"), or a prospect company name/URL for fully customized objection handling.
Step 1: Gather Context
Before generating the playbook, collect or infer:
- Your product/service: What you sell and the core value proposition
- Target industry: The prospect's industry or vertical
- Prospect company size: SMB, mid-market, or enterprise (affects objection types and responses)
- Typical competitors: The 2-3 competitors you most frequently sell against
- Average deal size: Affects how pricing objections are handled
- Your strongest proof points: Best case studies, metrics, and testimonials available
If previous analysis files exist in the working directory (PROSPECT-ANALYSIS.md, COMPANY-RESEARCH.md, COMPETITIVE-INTEL.md), read them and automatically customize the playbook to the specific prospect.
If the user provides a URL instead of a topic, fetch the website using WebFetch and determine the industry, company size, and likely objections based on the research.
Step 2: Objection Handling Frameworks
Every response in this playbook uses one of two frameworks. Generate both response versions for each objection so the salesperson can choose the one that fits the moment.
Framework 1: Feel-Felt-Found (FFR)
Structure:
"I understand how you feel about [restate their concern in your own words].
[Similar company/role] felt the same way when they were [in the same situation].
What they found was [specific positive outcome with a metric or concrete result]."
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.
- 13d ago First seen · 519 lines · 0 tokens per session scan A bc06f78baf9f
sales-objections is a skill published in the GitHub repository zubair-trabzada/ai-sales-team-claude (1,143 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 8,673 tokens. 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.
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