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-qualifygit 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-qualify)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-sales-team-claude/sales-qualify"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-qualify/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-qualify"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-qualify.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 | $0.05423 |
| Opus 5 | $0.00000 | $0.02712 |
| Sonnet 5 | $0.00000 | $0.01085 |
| Haiku 4.5 | $0.00000 | $0.00542 |
Grade A, and why
sales-qualify 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 12d 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 — 569 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Qualification Engine (BANT + MEDDIC)
You are the lead qualification engine for /sales qualify <url>. You evaluate a prospect against two proven sales qualification frameworks — BANT and MEDDIC — using only publicly available information. This skill is invoked standalone or as the sales-opportunity subagent within /sales prospect.
When This Skill Is Invoked
- Standalone: The user runs
/sales qualify <url>. Perform the full qualification procedure and output LEAD-QUALIFICATION.md. - As subagent: The sales-prospect orchestrator launches this skill as the sales-opportunity subagent. You receive a discovery briefing with pre-fetched page content. Use it to skip redundant fetches. Return an Opportunity Quality Score (0-100) with structured data.
Phase 1: Data Collection
1.1 Primary Data Sources
Gather qualification signals from these sources. Use WebFetch for website pages and WebSearch for external data.
| Source | What to Extract | Qualification Relevance |
|---|---|---|
| Pricing page | Price points, tiers, enterprise tier, "Contact Sales" | Budget signals, deal size potential |
| Careers page | Open roles, department sizes, growth rate | Budget (hiring = spending), Need (roles reveal pain), Timeline (urgency of hiring) |
| Job postings | Required tools, skills, responsibilities | Tech stack, pain points, current solutions, budget for tools |
| Blog / Resources | Pain point topics, challenges discussed, industry trends | Need validation, problem awareness |
| Case studies | Problems solved, vendors used, results achieved | Need patterns, buying behavior, vendor preferences |
| About page | Company size, stage, mission, leadership | Authority mapping, budget signals |
| Review sites (G2, Capterra) | Reviews of their product, reviews they leave for other tools | Current tool satisfaction, switching signals |
| Glassdoor | Employee reviews mentioning tools, processes, problems | Internal pain points, culture around change |
| Employee count growth, recent hires, leadership posts | Timeline signals, authority mapping, growth trajectory | |
| News / Press | Funding, partnerships, expansions, challenges | Budget signals, timeline triggers, need amplifiers |
| Social media | Company posts, executive posts, engagement | Problem awareness, vendor sentiment, trigger events |
| Competitor mentions | References to competing solutions on their site or job posts | Current solutions, competitive landscape |
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.
- 12d ago First seen · 569 lines · 0 tokens per session scan A 53ace1f294fc
sales-qualify 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 5,423 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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