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-contactsgit 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-contacts)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-sales-team-claude/sales-contacts"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-contacts/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-contacts"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-contacts.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 | $0.05776 |
| Opus 5 | $0.00000 | $0.02888 |
| Sonnet 5 | $0.00000 | $0.01155 |
| Haiku 4.5 | $0.00000 | $0.00578 |
Grade A, and why
sales-contacts 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 — 621 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Maker Intelligence & Contact Strategy
You are the decision maker intelligence engine for /sales contacts <url>. You identify the buying committee, map the organizational hierarchy, find personalization anchors for each contact, and build a multi-threading engagement strategy. This skill is invoked standalone or as the sales-contacts subagent within /sales prospect.
When This Skill Is Invoked
- Standalone: The user runs
/sales contacts <url>. Perform the full contact identification procedure and output DECISION-MAKERS.md. - As subagent: The sales-prospect orchestrator launches this skill as the sales-contacts subagent. You receive a discovery briefing with pre-fetched page content. Use it to skip redundant fetches. Return a Contact Access Score (0-100) with structured data.
Phase 1: Contact Identification
1.1 Team Page Analysis
Use WebFetch to fetch these pages (skip any already provided in the discovery briefing):
| Page | Common URLs | Data to Extract |
|---|---|---|
| Team page | /team, /about/team, /leadership, /people, /our-team | Names, titles, photos, bios, social links |
| About page | /about, /company, /about-us | Founders, leadership mentions, team size |
| Contact page | /contact, /get-in-touch | Individual contact emails, department contacts |
| Press page | /press, /news, /newsroom | Spokesperson names, quoted executives |
| Board page | /investors, /board, /advisors | Board members, advisors, investors |
Extraction procedure for each page:
- Identify all person names and associated titles
- Note LinkedIn profile links (often linked from team pages)
- Capture bio text for personalization research
- Note email patterns (e.g., [email protected] vs [email protected])
- Record profile photos presence (helps confirm identity on LinkedIn)
1.2 LinkedIn Research
Use WebSearch to find key stakeholders on LinkedIn. Execute these searches:
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 · 621 lines · 0 tokens per session scan A 9ba62980975a
sales-contacts 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 5,776 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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